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On the predictability of low visibility conditions at Vienna Airport

Diplomarbeit

Zur Erlangung des akademischen Grades

Magistra der Naturwissenschaften

an der

Leopold Franzens Universität

Innsbruck

eingereicht von

Eva Maria Müllauer

Innsbruck, im Jänner 2006

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Abstract

Fog at Vienna Airport (Kaltenböck, 2005) Air traffic is very sensitive to adverse weather conditions. Especially during approach and take off, low visibility and ceiling can be a hazard for aircraft. Apart from the dangers that go along with adverse conditions, also the economic impacts on air traffic can be enormous. In times of rising fuel costs, flight diversions or any other measure that leads to additional fuel consumption will hurt airlines. To reduce these costs for airlines, precise and reliable forecasts of visibility are necessary. In this thesis, observations in METAR code form for Vienna International Airport are analyzed for the years 1990-2003. The main part of this study concentrates on fast decreases of RVR, meaning a decrease from visibility of 1500 m or greater to values below 500m within one hour. For these cases, spread, wind speed and direction, clouds and soil moisture are investigated two hours before this fast decrease of RVR occurred. Most of these RVR events have values of spread below 2°C. When spread is higher, fast decreases of RVR are very unlikely to happen. For wind speed and direction, different categories were defined. Comparing these categories it can be seen that on the one hand low wind speeds (6kt or less) favour the formation of fog but that also higher wind speeds in combination with wind direction SE also lead to fast decreases of RVR. This is due to the advection of moist air from Neusiedler Lake that is situated SE of Vienna Airport.

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Before fast decreases of RVR, the sky is often clear. But also a relative high percentage of RVR events show an overcast sky before fast decreases of RVR. In another step, “ideal” weather conditions are defined and the frequency of occurrence of fast decreases of RVR is analyzed. In the best case, 20 percent of all “ideal” weather conditions events are followed by a fast decrease of RVR. For other measuring sites percentages are even lower. This shows why it is so hard to create reliable forecasts of visibility. Some RVR events are then investigated in detail. For this task one-minute weather observations are utilized. These observations reveal details about the behaviour of RVR that are lost at the high time resolution of METAR codes. For these events, some meteorological parameters as well as RVR are plot to find out what happened before and during this specific event, what lead to the dissipation of fog and what these events have in common.

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Acknowledgements

Without the scientific and private support of a large number of people this thesis would not have been possible: Thanks to my supervisor Dr. Herbert Pümpel, who had the idea to this work. Thanks for answering all my questions and for the numerous meetings. Thanks to the people at Austro Control in Vienna, who provided me the data: Harald Kovar, Herbert Fiala, Andreas Pfoser and Gregor Mitternast. I also want to thank to Dr. Georg Mayr for proof-reading this thesis. Thanks to the whole Austro Control team at Innsbruck Airport, especially to Bertl for providing me the data. I also want to thank Herbert’s family for allowing my visits at their home. Thanks to Alex Niederl for allowing me to use some of his research data. I am deeply grateful to my parents Maria und Erwin. Thanks for making my studies of meteorology possible. Thanks for all the support! A hearty thank I apply to Silvia, Franziska and Wolfram for being the best sisters and brother ever. Thanks to all my friends and collegues at university, especially to Gabi, who showed me the most beautiful places around Innsbruck, who went with me on an uncountable number of hikes and thus made it possible for me to relax from work. Thanks to all my friends from the mountains –Conny and all the other people I know from the Höttinger Alm. Last I want to thank the mountains around Innsbruck: thanks for changing my view on so many things.

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Table of Contents:

Abstract .....................................................................................1

Acknowledgements..................................................................4

1. Introduction...........................................................................7

1.1 Outline ........................................................................................................................... 9

2. RVR and visibility ...............................................................10

2.1 Visibility....................................................................................................................... 10 2.2 Measuring Runway Visual Range......................................................................... 12

2.2.1 Transmissometers ............................................................................................ 12 2.2.2 Forward scatter sensor.................................................................................... 13 2.2.3 Backward scatter sensor................................................................................. 13 2.2.4 Sources of error................................................................................................. 14

2.3 RVR regulations and impacts on air traffic ....................................................... 14 2.3.1 Instrument flight rules (IFR) and visual flight rules (VFR) ..................... 14 2.3.2 Effects of low visibility/ceiling and valuable forecasts on air-traffic .. 15

3. Fog as a meteorological phenomenon.............................22

3.1 Basics .......................................................................................................................... 22 3.2 Fog, mist and haze ................................................................................................... 23 3.3 Typical parameters for fog cases......................................................................... 23 3.4 Fog formation ............................................................................................................ 24

3.4.1 Different mechanisms for fog formation..................................................... 25 3.4.2 The role of different parameters in the fog formation-process ............ 26

3.5 Various types of fog................................................................................................. 28 3.5.1 Radiation fog ...................................................................................................... 29 3.5.2 Advection (mixing) fog..................................................................................... 31 3.5.3 Frontal fog ........................................................................................................... 32

3.6 Dissipation of fog ..................................................................................................... 33 3.6.1 Different mechanisms for fog dissipation .................................................. 33 3.6.2 Dissipation of advection fog .......................................................................... 34 3.6.3 Dissipation of a well-mixed fog ..................................................................... 34

4. Vienna International Airport ..............................................36

4.1 Description of the site ............................................................................................. 36 4.2 Low visibility procedures ....................................................................................... 41 4.3 Forecasting fog at Vienna International Airport............................................... 42 4.4 Impacts of low RVR on air traffic at Vienna International Airport ............... 45

5. Short-term forecasts of low ceiling and visibility ...........47

5.1 High density surface weather observations...................................................... 47 5.2 High-frequency surface weather observations ................................................ 50 5.3 Fuzzy logic in the prediction of ceiling and visibility ..................................... 54

5.3.1 Basics ................................................................................................................... 54

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5.3.2 Fuzzy logic for short term weather forecasts ............................................ 58 5.3.3 Fuzzy case-based prediction of ceiling and visibility ............................. 60

6. Data analysis.......................................................................66

6.1 Number of Cases ...................................................................................................... 66 6.2 Trends .......................................................................................................................... 68 6.3 Cases with a fast decrease of RVRs.................................................................... 75

6.3.1 Hour of the day................................................................................................... 75 6.3.2 Dew point differences ...................................................................................... 77 6.3.3 Wind ...................................................................................................................... 79 6.3.4 Clouds .................................................................................................................. 87 6.3.5 Soil......................................................................................................................... 88 6.3.6 “Ideal” conditions ............................................................................................. 89

6.4 Cases with a fast increase of RVR ....................................................................... 91 6.4.1 Hour of the day................................................................................................... 91 6.4.2 Wind ...................................................................................................................... 92

7. Case studies .......................................................................98

7.1 Introduction ................................................................................................................ 98 7.2 Cases ........................................................................................................................... 99

7.2.1 20011003: radiation fog ................................................................................... 99 7.2.2 20011008: radiation fog ................................................................................. 101 7.2.3 20020205: SE winds ........................................................................................ 104 7.2.4 20020317: strong SE wind............................................................................. 107 7.2.5 20000207: strong S- and SW-winds, also during RVR event (atypical)........................................................................................................................................ 110 7.2.6 20000111: relatively strong NW winds before RVR event .................... 113 7.2.7 20010209: fast increase of RVR after sunrise (due to short wave radiation)...................................................................................................................... 115 7.2.8 20021101: fast increase of RVR after sunrise (due to short wave radiation)...................................................................................................................... 118 7.2.9 20000103: unpredictable RVR development ............................................ 120

7.3 Time Lag Auto Correlations................................................................................. 123

8. Conclusions......................................................................126

Appendix ...............................................................................129

A) Glossary of acronyms............................................................................................. 129 B) Scores ......................................................................................................................... 131 C) The METAR Code ..................................................................................................... 132 D) A fuzzy-logic example............................................................................................. 136 E) RVR-Trend .................................................................................................................. 141 F) Cloud conditions ...................................................................................................... 142 G) Figures ........................................................................................................................ 143

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1. Introduction Fog is a widespread meteorological phenomenon that has always influenced human activities. Despite all kinds of technical equipment people are still getting lost due to low visibilities. Especially hikers in mountainous regions or boats at sea are in great danger when they lose orientation. But low visibilities reduced by fog, mist and haze can have even more disastrous impacts when it comes to aviation.

Figure 1.1: Low stratus deck in the Inn-valley The only steady characteristic of weather is its permanent change. These changes can occur at a fast pace and are sometimes almost unpredictable. Thus, like no other traffic category, aviation is highly dependent on weather forecasts and their improvements. Severe weather conditions such as low visibility can have significant impacts on safety, efficiency and timeliness of aviation operations. Especially during landing and take-off, which are the most crucial phases of any flight, aeroplanes are very sensitive to adverse weather conditions. When an aircraft lands in less-than-ideal weather conditions, one of the most important factors will be runway visibility. Low visibilities became such a danger for planes that during World War II fuel was burned along Great Britain’s runways to dissipate fog to make landings for planes returning from Germany safe (Rella, 2002). Apart from accidents also economic impacts of weather can be considerable for airlines. For safety reasons, air navigation authorities have set down specific procedures regarding air navigation at airports when runway visual range (RVR) and

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heights of cloud bases are below certain thresholds. Contrary to planes with an Instrument Landing System on board (ILS) pilots operating on Visual Flight Rules (VFR) cannot land in such conditions. But also flights following Instrument Flight Rules (IFR) are negatively affected by very low visibilities. The minimum time between consecutive landings and between take-offs is increased. Many hub aerodromes are already working at their maximum capacity and low visibilities can therefore cause dramatic delay problems. But these delays are not restricted to the airport on which low visibilities occur. These delays upset meticulous schedules throughout the day, acting like a domino-effect and resulting in diversions, cancellations and missed connections. If aircraft have to land on alternate airports due to low visibilities passengers have to be transferred to the original destination by alternate forms of transport, which causes additional cost. Also crew management is influenced by low visibilities: after a certain amount of time at work pilots have to take a break. If there are delays pilots are not allowed to take part in their next scheduled flight because they would exceed their working time limit. As a consequence the crew has to be exchanged - a different crew has to be organised and transported to the airport, which, of course, can cause logistic problems.

Figure 1.2: Reduced ceiling and visibility can lead to delay or cancellation in all categories of aviation; (Homepage: Office of the Federal Coordinator for Meteorology) Using highly accurate visibility forecasts some of these economic costs could be reduced. The problem is that when it comes to forecasting very low visibilities, no reliable methods are available. Visibility is not included in numerical models. Without a multitude of sensors, forecast aids and vast local experience it is almost impossible for the forecaster to give precise details about the timing and density of newly forming fog. A detailed and reliable visibility forecast could help to make aviation safer and to reduce environmental harmful emissions, which are unnecessarily emitted by aircrafts circling for long periods of time above aerodromes because of airport capacity problems caused by low visibilities. Therefore, a precise forecast could save large amounts of money.

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1.1 Outline Chapter 2 deals with the METAR code, runway visual range and the effects of low visibility for Vienna International Airport. Fog and dust as meteorological phenomena, typical values and the creation and dissipation mechanisms are discussed in Chapter 3. Chapter 4 gives details about Vienna Airport, where it is situated and the position of the measuring instruments. Also, the effects of low visibility on flight operations are discussed. An overview of different methods to improve RVR forecasts is presented in Chapter 5. Chapter 6 is characterized by the analysis of METAR code data. Chapter 7 includes case studies of some low RVR events at Vienna International Airport. Chapter 8 presents conclusions from this study.

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2. RVR and visibility 2.1 Visibility For obvious reasons, visibility is a crucial parameter for aeronautical operations. If the visibility is below the approved minimum aircraft and flight crew certification, low visibility can prevent aircraft from utilizing a runway. Together with ceiling, visibility determines whether or not to close the airport. The definition of visibility for aeronautical purposes is: (ICAO Homepage) “Visibility for aeronautical purposes is the greater of:

a) The greatest distance at which a black object of suitable dimensions, situated near the ground, can be seen and recognized when observed against a bright background;

and b) The greatest distance at which lights in the vicinity of 1 000 candelas can be seen and identified against an unlit background.”

The two distances have different values in air of a given extinction coefficient, and the latter varies with the background illumination. The basis for the two definitions above is obtained from two different laws: Koschmieder’s Law for objects and Allard’s Law for lights. Koschmieder’s Law (Koschmieder, 1924) is applicable to RVR during daytime conditions only. It estimates the visibility of black objects and is defined by:

Ct = e-σR ⇒ ln(Ct)/(- σ) = R R = RVR (m) σ = atmospheric extinction coefficient (m-1) Ct = contrast threshold, which is taken as 0.05

Allard’s Law (Allard, 1876) estimates the visibility of lights and is defined by

Et = (I/R2) e-σR ⇒ ln(Et R2 / I)/(- σ) = R Et = visual threshold (lx) R = RVR (m); σ = atmospheric extinction coefficient (m-1) I = runway light intensity (candelas) The visual threshold Et(lx) is approximated by log Et = -5.7 + 0.64 log B B = background luminance (cd m-2).

There are many phenomena that affect visibility. More than one third of low visibility problems occur during episodes of low ceiling. Another important visibility reducer is, of course, fog, which is responsible for about one third of all visibility problems (U.S.

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National Transportation Safety Board Statistics). But apart from these two main factors there are many other phenomena that contribute to the reduction of visibility. Clouds are one of these phenomena. Within clouds there is a wide range of visibilities. In thin clouds visibilities can be one kilometre, whereas in heavy rain in-cloud visibilities may be reduced to only a few metres. Stratus clouds can be a great danger for aircraft. Once inside this cloud visibility may fall off to zero. Especially if the clouds are low, landing and take-off pose high risks to inadequately equipped aircraft. Also, precipitation can reduce visibility. Rain droplets on the windshield may be a problem but they are more an irritation for the pilot than a serious visibility limiter. Usually, even heavy rain does not reduce visibilities to fewer than two kilometres. This changes when we are talking about snow fall. Light snow fall can reduce visibility below 5km, in heavy snow and blowing snow fall visibility may drop to a few metres. Near industrial areas or forest fires visibility is reduced due to smoke and other pollutants. Especially in low-level inversions these pollutants can cause great reductions of visibility. Another problem going along with smoke pollution is that these pollutants can act as condensation nuclei, which favours the formation of fog. Volcanic smoke and ash cannot only reduce visibilities but they can be sucked in the engines of a plane and damage the compressor fan blades. Blowing dust and snow or sandstorms are some more phenomena that reduce visibility, but they do not occur very often in such strength that they can cause serious visibility problems. Moderate wind can lift dust particles only about one metre, which does not affect visibility at eye level. Drifting snow can sometimes make runways hard to see, however. Generally, highest values of visibilities can be found in dry, cold air of maritime origin and in foehn situations. Visibilities can vary, depending in which direction the observer is looking. Therefore, two procedures for reporting visibility are in use: (National Weather Association, 2005; American Meteorological Society, 2005; Meteorological Service of Canada, 2005)

1) Prevailing visibility: this is the maximum visibility that applies for at least half the horizon and it is the visibility that is considered representative of visibility conditions at the station.

2) Minimum visibility: this is the lowest visibility measured in any direction. Normal meteorological measurements are made at the ground but they give almost no information about the visibility from points above the ground. During landing it is the slant visual range that is most important. It is the visibility from the aircraft down to the ground. The prevailing visibility may be quite different from the slant visual range. For example, if a shallow layer of fog is present, slant visual range may be larger than horizontal visibility. On the other side, the slant visual range from within a cloud may be less than the prevailing visibility at the ground. Air-to-ground or vertical visibility means the visibility from the aircraft to the ground, which, of course, like slant visual range can also differ from the prevailing visibility for the same reasons. The difference between slant visibility and vertical visibility becomes obvious when looking at Figure 2.1.

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Figure 2.1: Vertical Visibility and Slant Visual Range; Air-to-air visibility is not drawn in this picture but it would be a horizontal line from the aircraft into its flight direction; Meteorological Service of Canada, 2005)

When flying on top of a shallow fog layer the ground and runway are visible. As soon as this fog layer is entered during approach, forward visibility and slant visibility are lost. 2.2 Measuring Runway Visual Range Not so long ago, Runway Visual Range was measured through human observations, which is a very slow and laborious method. The observer had to enter the runway in use or stand within specified distances from the landing runway, count the number of runway lights visible and report the results to the air traffic control. On smaller airports this method is still in use but to ease the workload of air traffic control instrumental methods are nowadays common. The use of instrumental RVR-measurement systems is regulated by the International Civil Aviation Organisation (ICAO). Category II and Category III instrument runways must be equipped with automatic RVR instruments, whereas for Category I runways this is only recommended. The exact definitions and requirements for the different runway categories can be found in chapter 2.4.1. For automatic RVR measurements there are different visibility sensors, which are the key instrument in a RVR system, available: transmissometers, forward scatter sensor and backward scatter sensors. When measuring runway visual range measuring errors may occur. Positive and negative RVR errors are not equally significant. As aircraft operating on VFR or pilots that have no training on IFR must not land or take-off under certain circumstances (low visibility), reporting a higher RVR-value than actual can lead to a reduction of safety. Reporting a value lower than actual does not influence safety but operations might be affected if the error reduces the RVR below the minimum value for the category of operation. In this case the approach will not be allowed. 2.2.1 Transmissometers A transmissometer consists of a light transmitter and a light receiver, which measures the attenuation of the transmitted light on its way to the receiver. The more particles

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(fog, dust, precipitation) are in the atmosphere, the less light reaches the receiver. A computer then calculates the runway visual range. The measurement range of a transmissometer is linked to its base (distance between the transmitter and the receiver). Usually, transmissometers have a measuring range from 25 or 50m minimum to a maximum 1500 or 2000 metres, which is far too short to measure the required ten kilometres. Therefore, double-base transmissometres are in use to cover a larger range of measurement.

Figure 2.2: Transmissometer (from Vaisala-Homepage) 2.2.2 Forward scatter sensor The forward scatter sensor is a more economical alternative to the transmissometer. Its construction can be relatively compact: the transmitter and the receiver can be mounted on the same mast. The forward scatter sensor measures the light that is diverted from the transmitted beam. This measurement is based on the assumption that the diverted fraction of light represents the total loss of light. Unfortunately, this is not quite correct for all weather conditions as some obscurants can also absorb light or the angular distribution of scattering is not uniform. The scattering of fog, for example, is strongly peaked in the forward direction. Therefore, a forward scatter sensor may receive more (or less) scattered light than is actually obscured by particles in the atmosphere. As there are many different obscurants (snow, rain, fog, haze, smoke, dust, sand…) of different sizes found in the atmosphere, it is impossible to calibrate the sensor for every situation in this great variety of particles. Thus, before using a forward scatter sensor the ICAO strongly recommends to verify the sensor against a reliable source, such as a transmissometer for all weather conditions that prevail in the region where it will be used. 2.2.3 Backward scatter sensor Backward scatter meters are generally more sensitive to the types of scattering particles (fog, dust, rain, snow, sand) and thus the ICAO recommends avoiding them. This is why they will not be described in detail in this thesis.

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2.2.4 Sources of error Apart from calibration errors or errors that occur due to instrumental defects, some other phenomena can lead to wrong measuring values of RVR. The various instruments are influenced by the same phenomenon differently because of the different measurement methods. Spider webs can cause additional optical reflections, which leads to measured RVR-values that differ from actual values. Also, insects entering the measurement area change the detected signal and thus erroneous RVR-values are reported. Therefore, plants, which obviously attract insects, should not be present near the instrument. Moreover, plants also reflect light, which can lead to errors. Snow will also affect the measurement. The white surface increases the amount of scattered light picked up by the receiver of a scatter-meter. Also, snow that is not removed from the measurement-site can cause errors when it drifts or blows into the scattering volume. Another problem occurring with snow is drifting or blowing snow that obstructs the optical head of the sensor. Usually, sensors are equipped with a heating for this reason but in some conditions the blocking of the sensors cannot be removed. This causes a dangerous situation, as the amount of scattered light received at the sensor is reduced significantly and therefore the runway visual range will be overestimated. Therefore, the instrument should be installed not too low above the surface and snow should be removed from around the sensor and from the instrument itself. 2.3 RVR regulations and impacts on air traffic As a main parameter in landing and take-off operations, runway visual range is, of course, closely connected to regulations and flight planning. Visibility is not only important at the departure airport but it is also an essential parameter at the destination airport and for the choice of alternate airports. Especially pilots on visual flight rules need continuous updates of visibility information. Otherwise they will find themselves suddenly at instrument meteorological conditions (IMC). The differences between visual flight rules and instrument flight rules will be described in the next chapter. 2.3.1 Instrument flight rules (IFR) and visual flight rules (VFR) For visual flights visual meteorological conditions (VMC) are necessary, meaning that visibility, ceiling and distance to clouds are better than or equal to a minimum value. Pilots following visual flight rules (VFR) are fully dependent on their vision in order to navigate or to keep safe distances to obstacles, such as mountains or other aircraft. Instrument flights are possible also under IMC. These conditions include visibility and height of cloud bases below a minimum. The aircraft must be equipped and certified for instrument flights and the pilot must have an instrument rating. Although weather conditions for instrument flights can be much worse than for visual flights, there are still minimum conditions that must be met in order for the aircraft to take-off or land. Depending on the technical equipment of the aerodrome and the instruments of the

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aircraft, three different categories of weather minima are distinguished. These values are listed in table 2.1 and are relevant for instrument flights. Usually, modern airports are equipped with a Category III Precision instrument landing system. Pilots following instrument flight rules maintain a much closer contact to air-traffic control (ATC) in order to keep a safe distance to other aircraft. Apart from the equipment of the aircraft and aerodrome, the true air-speed of the aircraft is a main factor for the required weather conditions under which an aircraft will still be able to fly. In the same weather conditions a helicopter might still be allowed to fly, whilst an aeroplane with a true air-speed of several hundred km/h will have to stay at the ground.

ILS Visibility RVR decision height

CAT I not less than 800 m not less than 550 m not lower than 60m / 200ft

CAT II not less than 350 m lower than 60m / 200ft but not lower than 30m / 100ft

CAT III A not less than 200 m lower than 30m / 100 ft or no decision height

CAT III B less than 200 m but not less than 50 m

lower than 15m / 50 ft or no decision height

CAT III C no limitation no limitation

Table 2.1: Categories of precision approaches; At the decision height the runway must be visible during the approach. Otherwise the pilot has to interrupt the approach and eventually land at an alternate airport. ILS: Instrument Landing System; RVR: Runway Visual Range, (Austrocontrol, 2002) If the RVR and the decision height do not fall within the same category, the operation will be in the category with the lower minimum. When the weather minima at the aerodrome are not fulfilled, approaching aircraft will have to be diverted to alternate airports. For take-offs these weather minima are not that important. Here, only the runway visual range is significant – without considering the height of cloud bases. 2.3.2 Effects of low visibility/ceiling and valuable forecasts on air-traffic Low visibility and ceiling can lead to dangerous situations in air traffic. Accident records compiled by the U.S. National Transportation Safety Board over the period 1989 though 1998 indicate that aircraft incidents in which adverse ceiling and visibility were cited as contributing factors claimed the lives of 1685 people (averaging 168 per year) within the U.S. general aviation and charter/air taxi communities (Herzegh et al., 2003).

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However, reduced visibility and the following safety actions (increased time-intervals between landings or take-offs, diversions…) are also a central economic problem. When ceiling and visibility at a destination airport are forecast to be low at a flight’s scheduled arrival time, its departure may be delayed in order to minimize traffic congestion and related costs. One example is San Francisco International Airport which is adversely affected by marine stratus due to its location along the coast. During periods of marine stratus, the aircraft arrival rate is decreased by up to 50 percent as parallel approach procedures cannot be utilized.

Figure 2.3: percentage of total delay contributed by different groups for Newark International Airport (EWR); A Ground Delay Program (GDP) is an attempt to incur the delay on the ground; C&V stands for ‘ceiling and visibility’. (Allan et al., 2001)

An examination of the causes and effects of flight delays at the three main airports serving New York City (Figure 2.3) concluded that a correctly forecast timing of a ceiling and visibility event could be expected to result in a savings of approximately $480000 per event at La Guardia Airport.

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A Ground Delay Program (GDP) is an attempt to incur the delay on the ground, at the originating airport, instead of holding in the air. If the GDP is cancelled too soon, or the airport acceptance rate (AAR) is too low, then holding will still be the result. On the other hand, if the GDP is continued beyond the necessary time (often the case when low ceiling or visibility events end), or the airport acceptance rate is too low, then unnecessary (avoidable) ground delay will result (Allan et al., 2001). Based on a related study, the U.S. National Weather Service estimated that a 30 minute lead-time for identifying cloud ceiling or visibility events could reduce the number of weather related delays by 20 to 35 percent and that this could save between $500 million to $875 million annually (Valdez, 2000). Additional consumption of fuel is the result of holding patterns. Aircraft are directed into holding when a landing is not possible immediately as a result of capacity problems at the airport. Some busy airports are already working at their maximum capacity and an additional distraction caused by low visibility conditions could lead to a crisis situation at the airport.

Figure 2.4: Schematic presentation of measures taken during disturbances of air traffic; a slot is a fifteen-minute time-span during which an aircraft is required to start. New slots are assigned when part of the air space is reaching capacity, old slots lose their validity. (Sasse, 2000) Figure 2.5 shows a simple example of the classic queuing situation where the weather reduces the effective capacity of an airport for some finite time. This simple queuing model can be used to address both air traffic control/airport reductions in effective terminal capacity and traffic flow management actions by interpreting:

1) The effective capacity as the minimum of the air traffic control/airport constraints on the traffic flow and the flow rate imposed by FAA traffic flow management decisions, and

Planned Route

Departure Aerodrome

Destination Aerodrome

Alternative Route

Alternate

Holding Pattern

Slot Assignment (Holding Backs) Flight Cancellations Airlines Reactions

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2) The effective duration as the sum of the actual weather event duration and the time period over which an insufficient number of aircraft are available to land due to traffic management holds.

Figure 2.5: Queuing model for delay when adverse weather reduces the effective capacity of the airport. D = demand, Cw = capacity during adverse weather, Cv = capacity during VMC weather, and T = effective event duration (Evans, 1997). The effective duration of the weather event from the viewpoint of delay calculation is the actual duration of the weather plus any additional time in which the airport capacity is not fully utilized because the traffic flow managers did not have an adequate flow of planes available when the weather impact ended. To illustrate, if an actual weather event lasts for two hours and creates a situation in which a number of aircraft desiring to land at the airport are held on the ground at the respective departure airports, the delay event may be viewed as continuing until the ground hold aircraft are released and land at the destination airport. If the minimum flight time for the aircraft being held on the ground is one hour, then the effective duration is at least three hours. It can be shown that the accumulated delay for all the aircraft involved in the incident shown in Figure 2.5 is

∑ (delay to various aircraft) = 0.5 T2 (D-Cw) (Cv-Cw)/(Cv-D) The dependence of delays on the traffic density and traffic flow management procedures here is quite nonlinear. For example, we see that small increases in the effective capacity during a weather event, Cw, can produce larger proportional reductions in the accumulated delay because Cw appears both in differences (e.g., a small increase in Cw will result in a larger fractional change in each of the differences) and in the product of terms. (Allan et al., 2001)

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Accuracy and Value of a forecast In meteorology, skill-scores are used to evaluate the performance of a forecast. One of these scores is the Brier-Score – the mean square error of probabilistic forecasts. However, these skill scores cannot completely measure the value of a forecast. Small Brier-Scores are assumed to be an indication of a high quality forecast. Unfortunately, a small Brier-Score does not necessarily mean that the value of the forecast is high because the value of a weather forecast to the user is much more than accuracy. This problem is called the cost-loss ratio situation. The value of a forecast is closely connected with the potential saving of costs, whereas the accuracy of a forecast stands in close connection with skill-scores. A natural measure of the overall accuracy of probabilistic forecasting systems that produce two-event forecasts is the Brier score. The value of a forecast can be defined as the following: ‘If it is assumed that climatological forecasts would always be available to the decision maker, then the expected value of imperfect (perfect) forecasts can be defined as the difference in expected expenses between the situation involving climatological forecasts and the situation involving imperfect (perfect) forecasts.‘ (Murphy and Ehrendorfer, 1987) If there is the possibility of delays due to adverse weather, such as low visibility and/or ceiling, airline dispatchers must decide whether extra fuel should be loaded onto an aircraft. Of course, this extra fuel causes extra costs. This decision, which has to be made 1-2 hours before the plane’s departure, is usually based on TAFs (Terminal Aerodrome Forecast). Moreover, the decision about alternate airports, which is also dependent on a weather forecast, has to be made before the departure. In a study by Keith and Leyton (2002) these TAFs where compared to statistical probabilistic forecasts of low visibility/ceiling. It had already been shown that such alternative forecast methods showed higher skills than TAFs at short-range lead times. Keith tried to find out whether these statistical weather forecasts posses more economic value than traditional forecasts. A database of operating costs for 18 daily flights during the time period form April 2002 to May 2003 (resulting in a total number of approximately 7500 flights) was obtained from a commercial airline. For each of the 18 flights the use of the probabilistic forecasts resulted in lower costs than the traditional TAFs (average savings per flight: $23K over these 14 months). Projecting these figures over one year and all flights of a commercial airline, approximately $50M could be saved due to reduced operating costs. Another study, focused on the potential benefit of improved accuracy in aerodrome forecasts (Friedrichs and Lancaster, 2003) indicates that such an improvement in aerodrome forecasts could reduce operating costs for airlines significantly. In this study TAFs (which are often imperfect forecasts) at different Canadian airports were compared to perfect TAFs. Different airline decisions and operations (Table 2.2) before and after departure are dependent on weather forecasts. Each choice made has an impact and each impact has a cost. Therefore, if perfect TAFs were available costs could be avoided, especially at busy airports: the greater the number of arrivals, the greater the cost impact and hence potential savings.

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Pre-departure choices Post-departure choicesCancel the flight Divert to the filed alternateDelay the flight Return to the originAdd more fuel and depart Fuel stop en routeSelect another, or multiple, alternate(s), if necessary

Attempt a landing at the destination, miss the approach and continue to the filed alternate

Table 2.2: Decisions during pre- and post-departure phases of flight In the study, the perfect forecast represents the option case and provides and indication of the potential magnitude of impacts, such as diversions, that would be avoided. The corresponding reduction in operating costs represents the potential value of perfect forecasting accuracy. However, even perfect forecasts would not eliminate the impacts due to weather, since bad weather, even when perfectly forecast, would impact on airport operations. It is the accurate knowledge of the occurrence of poor weather that allows flight operators to plan for these conditions and therefore mitigate some of their negative effects. The key finding of the study indicates that the value of improving the accuracy of Canadian aerodrome forecasts to 100 percent accuracy is approximately $12 million (Canadian dollar). This is considered a conservative estimate because downstream effects were not considered. If these downstream effects were included as well then the economic impact would be about twice as large. The savings of costs can be attributed to three key aspects of airline operations: fuel burn or payload substitution, number of diversions and number of fuel stops. The first represents approximately 60 percent of the potential savings. This benefit originates from 30000 fewer flights requiring added fuel, translating into 4.5 million fewer kilograms of fuel burned. Fewer diversions yield about 30 percent of the total benefit. The probability of diversions due to weather is very low but the consequence when it happens is high. The analysis indicates that almost 700 fewer flights would be diverted with perfect TAFs. The reduced number of fuel stops comprises the remaining ten percent of potential savings. 370 fuel stops could be avoided if perfect TAFs were present. The avoidance of flight cancellations and delays represents about one percent of the total benefit. In some instances, the number of cancellations and delays may, in fact, increase as a result of more accurate aerodrome forecasts but with a corresponding reduction in the number of diversions and missed approaches. Instead of striving for improvements across the board that may not be easily realized and cost effective, it would be more efficient to focus on those areas where improvements generate the greatest potential value to the air carriers. The most important area of improvement involves reducing the number of false alarms. The magnitude of impact is due to the combination of the high cost of carrying extra fuel for an unnecessary alternate and the frequent occurrence of false alarms. Considerable potential value of aerodrome forecasts is lost by the traditional method of providing weather information in TAFs in categorical form (a binary yes-no product). Forecasters can use probabilities in TAFs for some elements like fog. For example they can say “PROB30” for the occurrence, which means a 30 percent

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chance of occurrence. However, some airlines are obliged by company regulations to carry the full fuel requirement equivalent to a forecast of 100 percent confidence. So the use of PROB 30 is redundant and the forecast is effectively completely categorical. In another study by Keith (Keith, 2003) forecasters at three different Australian forecasting offices were asked to estimate their confidence, to the nearest 10 percent, that the weather at five lead times will be below the special lowest alternate minimum (SLAM). One interesting outcome of this study is the difference in forecasting tactics between the different forecasters. Some forecasters have a very high hit rate that goes along with a high false alarm rate. On the other side, some forecasters have a comparably small hit rate but also a small false alarm rate, which means that the forecaster is less cautious than a forecaster with a high hit rate. In this study also a cost analysis for Qantas Airways was done. A flight from Singapore to Melbourne and a shorter flight were analysed. The saving of costs due to the use of probabilities is less for the shorter flight than for the longer, international flight. This is because of the relatively high false alarm cost, that is, the cost of carrying fuel over a long distance unnecessarily. The amount of benefit depends on three main factors:

1) The ability of airlines to specify the miss cost and false alarm cost for every flight. An example is a flight from Tokyo to Perth operated by Qantas with a Boeing 767. This flight is near the limit of endurance for a B767. If the Perth TAF has an alternate requirement and the flight is carrying a full payload, the pilot must land at Darwin to take on extra fuel. So the false alarm cost increases significantly due to payload considerations.

2) The willingness of regulators to allow airlines to plan fuel requirements from a probabilistic forecast. For this to happen, it would be necessary to prove that probabilistic TAFs provide a commercial benefit to airlines and do not impact negatively on safety.

3) The ability of forecasters to achieve reliability (= frequency of hits = hits/(hits + false alarms)) with their probability estimates. Forecasters can learn and their probability estimates can become more reliable.

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3. Fog as a meteorological phenomenon This chapter deals with the basics concerning fog and it gives an overview of the different mechanisms that lead to the formation of fog. Additionally, this chapter shows how different atmospheric conditions, but also different surface conditions, impact on the fog formation process or on already existing fog. Also, the different types of fog are discussed. Lastly, it explains how the different types of fog dissipate. Most of the information given in this chapter comes from several different sources: Kraus (2001), Liljequist and Cehak (1984), Cotton and Anthes (1989), and Stull (2000). The various authors use different fog classifications. The fog classification in this thesis is based on Cotton’s and Anthes’ classification, which they give at the beginning of their chapter “Fogs and Stratocumulus Clouds”. 3.1 Basics The variability of water vapour in the atmosphere is very high, values range from 0 to 30 g/kg (0% to 4%). Moreover, water vapour is the only substance in the atmosphere, which exists in all three phases: water-vapour, ice and water. Due to changes between phases (condensation, evaporation…) large amounts of energy are “used” or “freed”. Also, in the radiation balance water-vapour plays an important role. As a greenhouse gas, water-vapour changes the emission or absorption of long-wave radiation in the atmosphere. All gases in the atmosphere contribute to the total pressure. The pressure associated with one specific gas in the atmosphere is called partial pressure. The partial pressure of water vapour is called vapour pressure. Usually the letter “e” is used as a symbol for water vapour. Generally, the phrase “the air contains water vapour” is used (moist air is compared with a sponge), which is not correct because water vapour is not dissolved in the atmosphere. Theoretically air can hold any proportion of water vapour. However, a threshold called ‘saturation humidity’ prevents the water vapour to exceed a temperature-dependent limit. In the real atmosphere this threshold is rarely exceeded by one percent. It is the result of the equilibrium between condensation and re-evaporation. In the atmosphere there is a continual exchange of water molecules between the liquid water and the air. When the processes of evaporation and condensation balance each other we are talking about equilibrium. The equilibrium value (saturation) of vapour pressure over a flat surface of pure water is denoted by the symbol es. If the liquid water temperature increases, then evaporation will increase and therefore temporarily exceed the condensation rate. The number of water molecules will increase until a new equilibrium is reached. Thus, the saturation humidity increases with temperature: warmer air can hold more water vapour. Air can either be saturated, unsaturated or supersaturated. If an air parcel is saturated, then the air contains the above mentioned threshold of water vapour. The actual water vapour pressure equals the saturation water vapour pressure. If the actual vapour pressure is smaller than the saturation pressure then the air is unsaturated. Air can also be slightly supersaturated. This happens when there are no surfaces present where water vapour can condense. Such a situation can occur in clean air with no cloud condensation nuclei. Moreover, a fast decrease of the

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threshold value can cause super-saturation. In this case condensation does not remove water vapour fast enough. In meteorology different measures of humidity are in use. One of these measures is the relative humidity (f). It is the ratio of the actual vapour pressure and the saturation water pressure. Values are given in percent:

f = 100 * e/es Unsaturated air has values of relative humidity less than 100 %, saturated air has a value of exact 100 % and supersaturated air has values greater than 100 %. Another important humidity measure is the dew-point temperature (Td). With respect to ice dew point is called frost-point temperature. This temperature indicates the value to which air must be cooled (at constant pressure) to reach saturation. When dew point is reached relative humidity is 100 % - the air is saturated or actual vapour pressure equals the saturation vapour pressure. Thus, the dew-point depression (the difference between actual temperature and actual dew-point temperature) is a measure of the relative dryness of the air. 3.2 Fog, mist and haze Reduced visibility near the ground is caused by tiny particles called aerosols suspended in the air. Usually, people use the words fog, mist and haze for phenomena that reduce visibility. In meteorology, exact definitions are used for all these terms. Fog means that visibility is reduced to less than one kilometre. Relative humidity is close to 100 percent but in very low temperatures (-30°C to -40°C) relative humidity (in relation to water) can decrease to values of 80% to 70%. If the nuclei reducing the visibility to these low values are not water droplets but ice crystals, the phenomenon is called ice fog. Also mixed fogs, consisting of liquid water and ice crystals, are possible. Ice and mixed fogs can only exist at negative temperatures, whereas water fog can exist at both – positive and negative – temperatures. Mist has the same chemical make up as fog (water droplets or ice crystals) but it has visibilities greater than one kilometre and less than 5 kilometres. Relative humidity must be greater than 80 percent, otherwise we are talking about haze. Haze has visibilities less than 5 kilometres and a relative humidity less than 80 percent. Visibilities are reduced due to moistened dust particles. Haze has a yellowish or blueish hue. Due to the size of fog droplets (two to eighteen micrometers) light is scattered independently of direction in fog. 3.3 Typical parameters for fog cases Fog forms through condensation or sublimation of water-vapour on condensation nuclei. These nuclei are very important for fog formation. Without them fog would only form at very high rates of super saturation. But usually, near the ground there are enough nuclei present. A thick fog with visibilities of 100 meters has a liquid water content of 0.1 – 0.2 g m-3. LWC increases, if the temperature decreases due to

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adiabatic expansion of air as a consequence of vertical motion, due to radiation cooling or due to turbulent heat exchange with the environment. Locally, advection and vertical movements may also influence the liquid water content. Some values concerning fog can be found in table 3.1. Liquid Water Content (LWC) 0.05-0.2 g/kg (max. 1g/kg)duration minutes to daysvertical velocity 1-10 cm/shorizontal velocity max. 1 m/sabsolute turbulence lowturbulence relative to average wind speed highcooling due to moist abiabatic lifting 0.2 K/h (for lifting rate of 1 cm/s)cooling due to long wave radiation flux-divergence 1-4 K/h Table 3.1: values of fog Fog consists of particles of various sizes. They are said to be polydispersed. The majority of droplets have sizes from 2 to 18 µm. Nevertheless, also smaller droplets (0.1 µm) and larger crystals (100 µm) are possible. The number of droplets (or crystals) in 1 cm3 air also varies, depending on the type of fog. Values range from 0.5 to 100 droplets for advection fogs, from 50 to 860 for radiation fogs, from 70 to 500 for evaporation fog. These figures were obtained for fogs of moderate intensity. Variations in the intensity of fogs occur because of the fluctuations of radiation, turbulence and deposition. The details of these phenomena and their influence on fog are still unknown, although some theories exist. 3.4 Fog formation As mentioned above, condensation nuclei play in important role in the process of fog formation. As fog is located in the boundary layer transport of heat and moisture from the underlying surface is very important. Often, fog formation is dependent on the presence of a stable layer near the ground. At the upper boundary of the fog there is often a temperature-inversion. This inversion can be responsible for the formation of fog but the fog itself can also intensify the inversion. In the former the inversion stops the moist air from moving into higher atmospheric levels and therefore is responsible for the accumulation of water vapour and water droplets in the layer below the inversion. In the latter the inversion is intensified due to long-wave radiation from the top of the fog into the atmosphere. This radiation, which comes close to the black body radiation if the fog is thick enough, results in cooling of the upper boundary of the fog.

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3.4.1 Different mechanisms for fog formation (From Liljequist and Cehak, 1984) a) Cooling of air until temperature equals dew point

• The surface cools due to the emission of long-wave radiation. Layers of air in the boundary layer lose energy due to radiation and thus cool down until temperatures come close to the dew point.

• As warm air moves over cold surfaces it cools down. Energy is lost to the ground, owing to heat conduction and radiation.

• If air is forced up a hill rising air is cooled adiabatically. Air must rise to the lifting condensation level to form fog.

b) Adding moisture

• Water-vapour is added to the air due to evaporation of rain droplets. • Water-vapour is added to air due to evaporation from the ocean, lakes or

rivers, in particular where the water is warmer than the air. c) Mixing of air masses with different temperatures

Figure 3.1: schematic picture of mixing of two air parcels Two moist but unsaturated air masses (see Fig. 3.1) with different temperatures (T1 and T2) and the specific humidity q1 and q2 are mixed. The temperature changes linearly, resulting in a temperature Tm that lies between the temperatures of the original air masses. The saturation line (given by the Clausius Clapyron equation) is not a straight line but a curve. Therefore, saturation can be reached through the mixing of two unsaturated air masses (M lies in the saturation area). The difference between qm and qk condensates and becomes visual as mixing-fog.

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3.4.2 The role of different parameters in the fog formation-process (From Cotton and Anthes, 1989) • The impact of radiative processes During night time the earth’s surface loses energy through infrared-radiation, which also causes a cooling of the air adjacent to the ground. The radiation-flux-divergence (figure 3.2) plays an important role after fog has already formed. Due to this divergence, stability at the upper boundary of the fog increases. On the other side, stability decreases in and below the stratus layer. The resulting turbulent vertical mixing of fog-air with clear, almost saturated air below the fog layer leads to a diffusion of the fog towards the earth’s surface (The consequence of mixing different air masses can be saturation, as discussed above). The loss of energy and the following cooling at the upper boundary of the fog increases the liquid water content and as a consequence, visibilities decrease.

Figure 3.2: radiation flux divergence: when T3 < T2 (and ε3 < ε2 ): air parcel in the middle gets less energy from the air above than it “gives” the air parcel above. This leads to a loss of energy and thus cooling. As a consequence spread is reduced and fog can form. • The role of dew The humidity that is needed for the deposition of dew comes from the air. This effect is responsible for the nocturnal dew-point-inversions, which can have a vertical extent from metres to several hectometres. Fog can start forming at the dew-point inversion and grow downwards to the ground, but it can also form first at ground level. It is still unknown, which are the determining factors for these processes. Whether the

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deposition of dew intensifies or weakens the forming of fog depends on the rate of dew deposition compared to the rate of loss of energy due to radiative cooling. The deposition of too much dew will prevent the forming of fog, or helps to dissolve it. After sunrise temperatures at the ground start rising and therefore, dew starts evaporating into the air. This effect can keep the fog alive for several more hours. Finally, despite the evaporation of dew, solar heating is strong enough to make the fog lift. The fog dissipates first at ground level, leaving a stratus cloud. This effect is called “fog lifting”. • Impact of turbulence on fog Turbulence plays an important role in the process of fog formation. However, it is still not known whether turbulence promotes the formation of fog or not. On the one side, turbulence increases the moisture-flux to the ground, which weakens the formation of fog. If there is no turbulence the moisture will remain in the air and therefore favour the formation of fog. On the other side, vertical turbulent mixing can have a positive effect on the formation of fog. This is true if vertical turbulent mixing goes along with radiative cooling. Moreover, the increasing turbulence after sunrise favours the mixing of the different fog layers, resulting in denser fogs after sunrise, especially at ground levels. • The role of the deposition of droplets (precipitation) Generally, fog does not produce precipitation because fog droplets are very small. Only a few droplets have a radius greater than 20 µm, (compared to cloud droplets in cumulus clouds: 40 – 50 µm or rain droplets: millimetres) and therefore have only tiny masses. If droplet sizes increase due to coalescence (droplets of different size have a different speed in the fog and thus can collide and merge, which results in greater droplet radii and heavier droplets) droplets will start to deposit in the form of drizzle. This reduces the liquid water content of the fog. A fog with different droplet sizes will dissipate easier due to this effect. Therefore, such a fog is called colloid-labile. If all the droplets in a fog have almost the same size the fog is called colloid-stabile: droplets with similar sizes are moving with the same velocity within the fog and therefore have a smaller chance to collide with other droplets. Fogs with a small number of cloud condensation nuclei have a small liquid water content because a small number of cloud condensation nuclei results in big droplets, which tend to deposit easily. On the contrary, polluted air with many cloud condensation nuclei forms a fog that consists of many small droplets. These small droplets are light and can therefore stay in the air, resulting in a dense fog. • Impact of soil properties on fog Different surfaces cool at different rates, depending on the thermal conductivity. Highly conductive surfaces cool more slowly. The conductivity of soil is very much dependent on its moisture content – wet soil cools faster than dry soil. Grass influences the formation of fog in two different ways. Soil that is covered with grass cools faster than bare soil because grass has low values of heat capacity. On the other side, grass-covered soil does not cool as much as uncovered earth

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because of less emitted infrared-radiation. The net-effect is a faster cooling of air above grass-covered soil in relation to uncovered soil. Moreover, snow cover influences the formation of fog. As the saturation pressure over ice is smaller than over water (For instance, at T = -10°C the saturation pressure with respect to ice is 2.6 hPa, and with respect to water 2.87hPa), the conditions are not favourable for water fog formation over snow surface. If temperature decreases, the air reaches its saturation state earlier with respect to the snow surface. Thus, as soon as relative humidity reaches 91 % (at a temperature of -10°C) sublimation on the snow surface begins. This sublimation impedes the condensation of the water vapour and thus, the forming of fog droplets. The most favourable conditions for fog formation over snow cover are observed at temperatures close to zero. In this case the saturation pressure over ice and over water is almost the same, while the cooling from the snow surface serves as a strong fog forming factor. Consequently, fogs over snow cover occur at temperatures close to 0°C (from –5°C to +5°C). Also, moist soils favour the formation of fog. Therefore, especially after rain in the evening, fog is very likely. • The role of clouds The advection of clouds over the fog layer can also have an influence on fog. Due to long-wave radiation from clouds to the surface, temperatures start to increase due to the change in the radiation balance. This increase in temperature can lead to the dissipation of the fog. In a cloudless night the loss of energy is much higher than in a night with clouds. The effect on the net-radiation balance on an overcast night is the greater the closer the cloud bases are to the fog’s upper boundary. Thus, high and thin cirrus-clouds do not have an effect on fog, whereas more opaque medium or low clouds can impede the formation of fog or lead to the dissipation of already existing fog. Another important factor in the fog-formation-process is the temperature-gradient between the earth’s surface and the air. If the temperatures of surface and air are almost the same then the dissipation of the fog is difficult. If there is a cloud layer over the fog and a large temperature gradient between the surface and air (warm surface and colder air temperature) then the dissipation of the fog is much more likely. The sensible heat flux from the surface into the air leads to a warming of the air and therefore to the dissipation of the fog. • The role of wind speed For the formation of radiation fog calm winds are favourable, whereas advection fogs form at higher wind speeds. Generally it can be said that the higher the wind speed the larger the vertical extent of the fog. 3.5 Various types of fog Due to these various fog-forming processes many different types of fog exist. Sometimes fogs form through the combination of different mechanisms and it can be difficult to order this specific fog event into one of these groups. However, in the

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following chapters I will present the various types of fog that exist and how they form and dissipate. The following fog categorization, which is used by Cotton and Anthes (1989), will also form the bases for the fog classification in this thesis: “Fog is normally categorized into four main types:

A) Radiation fog i. ground fog ii. high inversion fog iii. advection-radiation fog iv. upslope fog v. mountain valley fog

B) Frontal fog i. prefrontal (warm front) ii. postfrontal (cold front) iii. frontal passage

C) Advection (mixing) fog i. sea fog ii. tropical air fog iii. land and sea-breeze fog iv. steam fog (“Arctic sea smoke”)

D) Other i. ice fog ii. snow fog

3.5.1 Radiation fog For the formation of radiation fog several factors are favourable. The most important one is a negative radiation balance. This negative balance is obtained through the emission of long-wave radiation from the earth’s surface into space during the night. Thus, a clear sky is very important for the formation of radiation fog. Another important factor is that the surface near air-layer is already moist. Moreover, weak or calm winds favour the formation of this type of fog. As wind creates turbulent mixing, calm or light wind maximize cooling at the surface. Faster winds and/or drier air would delay the onset of fog. Also, a strong temperature-gradient between day- and night-time is an advantage. After rain in the evening and following clearing of the sky and weak winds, fog is very likely. Wet soil is another factor that favours the formation of radiation fog. Often, fog forms in high pressure fields with small horizontal pressure gradients at the synoptic scale. Through the nocturnal loss of energy, the surface cools and as a consequence also a shallow, surface-near air-layer is cooled until dew point is reached. The rate of surface cooling by radiation is about 1°C per hour (during a clear night). Owing to the cooling water vapour in the surface-near air begins to condense onto objects as dew, resulting in the drying of these lowest meters of the atmosphere. The deposition of dew is maintained because of moist transport from the air to the surface due to weak turbulence. Continued cooling increases the stability in this layer, thus it can become resistant to turbulence. Finally, turbulence near the surface ceases altogether and so the formation of dew at the surface is stopped. For many nights, fog never happens because night ends before the temperature of the air can cool down to the dew-point temperature.

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Ground-fog can only be one to two metres deep, as it happens often above meadows, snow covered soil or in valleys; but its extent can also reach values of some hundred metres, depending on the thickness of the surface-inversion. The fog starts forming near the surface and then grows into higher levels of the atmosphere. The depth of fog is estimated by the height, where the nocturnal temperature profile crosses the initial dew-point temperature. When the nocturnal air cools, liquid water content increases, resulting in the reduction of visibility. The lowest visibility (densest part of the fog) will generally be in the coldest air, which is initially at the ground. At this stage the young fog did not yet have the time to develop and mix (Fig. 3.3 (a)). Fog density decreases smoothly with height, due to the temperature increase with height. When the fog becomes optically thicker and denser, it reaches a point where the surface is so obscured that cooling through infrared-radiation is no longer possible. The height of maximum radiative cooling moves upward into the fog, away from the surface (Fig.3.2). Cooling of air within the nocturnal fog causes air to sink. Convection, which turbulently mixes the fog, is the consequence. The fog becomes more uniform in the vertical. The liquid water content is now distributed over the fog layer. In this fog, the temperature decreases with height at the moist adiabatic lapse rate (Fig. 3.3 (b)). The infrared-cooling at the upper boundary of the fog can strengthen and maintain this fog. Therefore, this well-mixed fog can persist well into the morning. The combined effects of absorption of solar radiation in the interior of the fog and long-wave-cooling at cloud top can cause the boundary layer to warm and the fog base to lift from the ground. This fog is then reclassified as a stratus cloud.

Figure 3.3: (a) Initially, the fog is denser and colder at the ground; (b) Well-mixed fog that is denser and colder at the top due to infrared radiative cooling; (c) Heating during the day can modify the fog causing the base to lift and the remaining elevated fog to be less dense (Stull, 2000) In winter valley fog can be observed frequently. It often goes along with high pressure fields. Drizzle is a phenomenon that occurs with valley fog occasionally. Above the valley fog layer warm temperatures and very high visibilities can be

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expected in the relatively dry air. The base of valley fog is often above the observer, which means that below valley fog visibilities can reach values of more than one kilometre. To be exact, valley fog is more a stratus cloud than a fog. Sometimes this cloud can touch the ground. A schematic picture of valley fog is drawn in figure 3.4.

Figure 3.4: Schematic picture of a valley fog: an even cloud layer (Stratus or Stratocumulus clouds) is present below the inversion. The base of the clouds can range from only a few metres to some hundred metres above the ground but it can also touch the ground. (m=mixing ratio, Θ = potential temperature) (Homepage Universität Bochum, 2005) When an air parcel moves up sloping terrain its temperature decreases due to adiabatic cooling. As soon as the air parcel reaches the lifting condensation level (LCL) condensation starts and fog forms. This fog is called upslope fog. As stability of the air layer decreases during the lifting process, stability should be very high at the beginning of the lifting. Otherwise convective clouds (Cumuli) will form instead of upslope fog. 3.5.2 Advection (mixing) fog Advection fog forms when warm and moist air moves over a cold surface, such as soil after a cold weather period in winter (snow covered ground) or cold water (lake, sea and river) in summer. In the latter case a very dense fog with extremely low visibilities can form. An example for this fog type is “Newfoundland-fog”: it forms when the warm and moist air from the Gulf-Stream moves over the Labrador-Stream. The reason for the cooling is a sensible heat flux from the warm air to the cold surface. The air cools and as a consequence water-vapour condensates. Often the wind is strong at the beginning of the fog-formation process. Due to turbulent mixing the fog base can lift from the surface and become a low stratus cloud. Advection fog can also occur when an existing fog is advected from one area to another place. Often, fog forms in cold air above a wet surface (for example marsh). Through wind this cold air with already existing fog droplets is moved to a different place. Many car drivers know this type of fog because it is happening quit often that dense fog patches are moved over highways, causing mass-collisions with sometimes more than a dozen cars involved. Also, fog coming from the sea with the sea wind is advection fog.

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Steam fog, forming through adding water-vapour, is also an advection-fog type. Sometimes it is possible to see lakes and rivers steaming in winter. In summer this effect becomes visible over streets or forests after precipitation. Cold air moves over relatively warm and humid surfaces (unfrozen lakes during early winter, wet asphalt). Through conduction the cold air near the surface is warmed. Water-vapour is added due to evaporation. However, this thin layer of air near the surface is unsaturated. It is then mixed with the colder air higher above the surface due to turbulent eddies. Thus, as discussed above (Chapter 3.4.1), fog can form through this mixing process. Once formed, advection fog experiences radiative cooling from the upper boundary of the fog. Due to this cooling the fog becomes denser and longer lasting and it can evolve into radiation fog. 3.5.3 Frontal fog Frontal fog can form in connection with warm fronts but also with cold fronts. In the latter case, rain fog is formed when the cold air, after the cold front has passed, is moved over warm and humid surfaces, such as lakes. Then steam fog will form, as discussed in details in Chapter 3.5.2. Frontal fog is also formed through the mixing of two different air masses that are both relatively humid. The detailed processes that occur have already been discussed in Chapter 3.4.1. In smaller scales this fog can also be observed when opening a window. Cold and moist air can stream into the room, mixing there with the warm and humid air. Another example for this fog type is the condensation of breath-air in winter. Frontal fog in connection with warm fronts is more frequent. A “normal” warm-front looks like the one drawn in figure 3.5 (a). But a warm-front during the winter-season can have a different look, as can be seen in figure 3.5 (b).

Figure 3.5 (a): normal warm front: on the left hand side the warm air moving to the right is visible. On the right hand side is cold air. Arrows indicate the circulation at the front. As a consequence of the lifted air clouds form: Ns (Nimbostratus), As (Altostratus) and Ci (Cirrus); (Homepage Universität Bochum, 2005)

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Figure 3.5 (b): warm-front in winter with the typical cold part (blue area) behind the front; the horizontal extent of the front can reach values of several hundred kilometres. (Homepage Universität Bochum, 2005) In figure 3.5 (b) there is the warm air, again, on the left hand side of the picture, moving to the right. In winter it takes some time for surface near air layers to warm because of the cold surfaces that are present during the cold season. This effect causes a relatively cold air-layer near the surface just behind the warm-front (indicated with the blue colour and the black arrow), whereas the air that is higher above the surface is already warm. When precipitation from the warm air above falls into this cold surface-near layer, the warm droplets start to evaporate into the cold air. Thus, water-vapour is added to the cold air-layer. Mixing of the cold, humid air with the warm and humid air above leads to condensation – fog is forming. Usually, this fog type does not last very long, as it moves along with the front. 3.6 Dissipation of fog As mentioned in the introduction, attempts were made to dissipate fog artificially during World War II. This example shows how important it was and still is for aviation operations to have good visibilities at an airport. To make a valuable visibility forecast it will be necessary to know and understand the mechanisms that lead to the dissipation of fog. 3.6.1 Different mechanisms for fog dissipation In the fog-dissipation-process the following mechanisms play an important role: a) Dissipation as a result of warming

• Earth-surface and the fog itself absorb incoming solar radiation • Fog is moving above a warming surface • Air is moving downwards a slope

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b) Dissipation as a result of the removal of droplets or water-vapour

• Water-vapour can be withdrawn from an air parcel through the condensation on cold rain or on a cold surface

• Water-vapour can also be withdrawn from an air parcel through the sublimation on snow-crystals

c) Dissipation as a result of turbulent mixing 3.6.2 Dissipation of advection fog Advection fog can be persistent since the synoptic situation, which originally created this fog, does not change very fast. Advection fog will be dissipated through the passage of a front and a change of air mass and increasing wind-speed or change in wind direction. Thus, the foggy air is replaced by cold and dry air that cannot be further cooled by the underlying surface. At the upper boundary of the fog, an increase in wind speed results in the entrainment of warmer and drier air from above the fog. Near the surface, stronger winds cause mixing of the surface-warmed air with the fog above. Both phenomena favour the evaporation of fog droplets and therefore the dissipation of fog. Moreover, the advection of a broken or overcast mid-level or low-level cloud cover over the fog top can be responsible for the dissipation of fog. These clouds alter the radiation balance: radiative cooling at fog top is reduced, resulting in a slow down of droplet formation, which can stop the fog formation process or even dissipate the existing fog. Generally speaking, advection fog is dissipated also through the same mechanisms that can dissipate radiation fog, which will be discussed in detail in chapter 3.6.3. 3.6.3 Dissipation of a well-mixed fog Stratified fogs (Fig.3.3. (a)) that are optically thin have relatively low values of albedo (0,3 to 0,5). Due to this thin layer, sunlight can easily reach the surface and warm it, which as a consequence warms the fog layer and leads to evaporation of fog droplets and finally to the dissipation of the fog. Well mixed and optically thick fogs (Fig. 3.3. (b)), such as an old radiation fog or advection fogs, have higher values of Albedo (0,6 to 0,9). As a result most of the incoming solar radiation is reflected. However, the little part of the solar radiation that is not reflected off the fog is absorbed in the fog layer reducing relative humidity. On the other side, infrared-cooling from the fog top continues. This cooling at the top of the fog versus the warming in the interior of the fog reduces stability in the fog layer, leading to convective processes. Especially at the bottom boundary of the fog evaporation of droplets starts – the base of the fog is lifted. With the thinning of the fog layer the warming process accelerates, resulting in the complete dissipation of the fog. Sometimes the warming through the sun is insufficient to fully dissipate the fog. Then, during night-time, the bottom of the fog lowers back to the ground. Moreover, the settling of fog droplets favours the dissipation of fog. When the settling rate is higher than the formation rate the fog will thin and finally dissipate. For

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example, a settling rate of 1cm/s (valid for average fog droplets) would dissipate a fog layer with a depth of about 30 metres within an hour, if the fog maintenance processes are not present. Of course, in the real atmosphere these maintenance processes are working against the settling of fog droplets, resulting in a far slower dissipation of the fog.

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4. Vienna International Airport As this thesis deals with low visibility and ceiling at a specific airport it is useful and necessary to give some information about Vienna International Airport – where it is situated, the position of the different measuring instruments or how low visibility influences flight procedures at this specific airport. 4.1 Description of the site Vienna Airport is situated some 20 kilometres south-east of Vienna’s inner city. About 30 kilometres south east of the airport there is Lake Neusiedel with its surrounding moist land areas. The “Arsenal Tower”, where wind speed and direction as well as temperature are measured, is situated 13 kilometres off Vienna Airport in direction 290°. The following map (figure 4.1) shows this area: the airport is situated where it says “Albern” in the map.

Figure 4.1: Map of the surroundings of Vienna Airport; the airport is situated where it says “Albern” in the map

Figure 4.2 shows part of an Aeronautical Information Publication-map (AIP-map) of Vienna Airport, its runways and the surroundings. The green lines and dots in this map mark the position of the anemometers and the RVR measuring instruments, which are situated along the runways (two green dots that are connected by a green line and marked by letters). The letters A, B and C indicate the position of the RVR measuring instruments.

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The other two AIP-maps (figure 4.3 and 4.4) show RW16/34 and RW11/29 in detail. So it is possible to see the exact position of the different RVR measuring instruments along the runways:

• RVR11/29 A: northwest end of the RW • RVR11/29 B: halfway down the RW • RVR11/29 C: southeast end of the RW • RVR16/34 A: north end of the RW (situated in a depression of the ground) • RVR16/34 B: halfway down the RW (situated in a depression of the ground) • RVR16/34 C: south end of the RW (situated in a depression of the ground)

In this thesis RVR11/29 A is referred to as RVR11, RVR11/29 C as RVR29, RVR16/34 A as RVR16 and RVR16/34 C as RVR34. On some occasions (missing data at other RVR measuring instruments), RVR 11/29 B is used instead of RVR11/29 A or C and RVR16/34 B is used instead of RVR16/34 A or C. This is then explicitly mentioned in the text. Also, the position of the four wind-sensors is visible in these zoomed maps:

• Anemometer RWY11: southwest of RVR11/29 A • Anemometer RWY29: south of RVR11/29 C • Anemometer RWY16: east of RVR16/34 A • Anemometer RWY34: east of RVR16/34 C

Near RVR16/34 A the following additional measurements take place: temperature, dew point, relative humidity, precipitation. These measuring instruments are not marked in this AIP map. The areas shown in figure 4.3 and 4.4 are the two shaded (red) areas in figure 4.2.

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Figure 4.2: AIP-map Vienna Airport; the two red shaded areas mark the areas that can be seen in detail in figures 4.3-4.4; green colours mark RVR and wind measuring sites

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Figure 4.3: Zoom of AIP-map (RW 11/29); green: RVR and wind measuring sites

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Figure 4.4: Zoom of AIP-map (RW 16/34); green: RVR and wind measuring sites

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4.2 Low visibility procedures Low visibility procedures (LVP) are specific procedures at an aerodrome for the purpose of ensuring safe operations during Category II and Category III precision approaches (table 4.1; see also Chapter 2) and low visibility take-offs.

ILS Visibility RVR decision height

CAT I not less than 800 m not less than 550 m not lower than 60m / 200ft

CAT II not less than 350 m lower than 60m / 200ft but not lower than 30m / 100ft

CAT III A not less than 200 m lower than 30m / 100 ft or no decision height

CAT III B less than 200 m but not less than 50 m

lower than 15m / 50 ft or no decision height

CAT III C no limitation no limitation

Table 4.1: Categories of precision approaches; At the decision height the runway must be visible during the approach. Otherwise the pilot has to interrupt the approach and eventually land at an alternate airport. ILS: Instrument Landing System; RVR: Runway Visual Range, (Austrocontrol, 2002)

ATC applies special safeguards and procedures for low visibility operations that will become effective during specified weather conditions. These procedures are intended to provide protections for aircraft operating in low visibility and to avoid disturbances to the ILS signals. Generally, during approach the ILS signals from the different aircraft disturb each other. This does not matter as long as visibility conditions are good. However, as soon as approaching aircraft are dependent on ILS it is necessary to increase distances between aircraft to avoid disturbances of ILS signals. As low visibility conditions are often present during morning and evening hours which also happen to be the rush hours at aerodromes, these increases in distance lead to capacity problems at the busiest times of the day. During the period where LVP will become likely or are already in force, TWR shall provide special safeguard to aerodrome traffic and comply with the relevant procedures. ATC low visibility procedures will become effective in two stages in relation to weather conditions specified below. Stage I: Low visibility procedures stage I will be activated, if RVR for touchdown zone is 1200m or less and / or cloud base respectively vertical visibility is 300 ft or less. During this stage, CAT II / III approaches are possible on request, but using same procedures as applied for LVP stage II. All actions during this stage have to be cleared by the relevant ATC unit depending on air traffic plan for delaying action.

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Stage II: LVP stage II will be activated, if RVR for touchdown zone is 600m or less and / or ceiling respectively vertical visibility is 200ft or less. Arriving aircraft are vectored so as to ensure an intercept of the ILS at least 8 nautical miles from threshold. ATC issues a clearance for ILS approach regardless of what category is flown. Together with the approach clearance the surface wind direction and velocity and the actual RVR values will be transmitted. If weather criteria are better than for LVP stage II, procedures for LVP stage I are applied and announced via RTF or ATIS (Automatic Terminal Information Service). Low visibility take-off: A low visibility take off is given when runway visual range is less than 400 metres. Information regarding malfunction and downgrading of the approach procedure are: During approach, immediately after occurrence the information in table 4.2 will be relayed, if necessary, together with a downgrading of the approach category:

Failure or lack of DowngradingRVR assessment system or failure of display / transmissiometer of both touchdown and midpoint

CAT I

Secondary power supply for the aerodrome lightning system CAT I

LLZ out of CAT I / CAT II tolerance CAT IATC-ILS monitoring device CAT IWindinfromation not available CAT IFarfield monitor CAT IILLZ standby transmitter CAT II

Table 4.2: Table of failures with necessary downgrading of the approach category (from Austrocontrol, 2003) A change of operational status, if caused by a failure expected to last more than one hour, will be promulgated by NOTAM (Notice to Airmen). Pilots will be notified of shorter term deficiencies by ATC (ATIS and / or RTF). 4.3 Forecasting fog at Vienna International Airport At Vienna airport a study was undertaken to find out, which weather situations typically favour the formation/dissipation of fog and low stratus. Table 4.3 and table 4.4 show a summary of the outcome of this study (Fritzl).

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● in the middle of a surface High pressure over central Europe

● VRB or weak wind from NE to SE

● NE-wind and spread (difference between temperature and dew point temperature) close to zero: fast fog formation but only shallow fog likely

● W-/NW-/ and N winds rarely produce fog (wind >5kt); similar for NE winds and dry air advection

● When Southerly to Westerly winds at 5000ft exceed 35kt

● For low temperatures (< -5°C) fog forms late, usually after 00:00

● Shallow (dense) fog during NE-wind (3-6 kt): wind direction change to E/SE leads to increase of visibility (but usually at around noon) due to the lifting of fog (transition to stratus)

● Advection of dry or unstable cold air from NW to NE

● Strong low level inversion / temperature around zero or just below

● Higher pressure in the E usually prevents mixing of stable boundary layer

FOG

formation

favourable

unfavourable

dissipation

favourable

unfavourable

Table 4.3: Factors that favour formation/dissipation of fog in Vienna (Fritzl, 2002)

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• Centre of surface High pressure to the NE or SE and Föhn in the northern Alps; When SE-winds prevail at altitude, Stratocumulus is more likely; 48 hours before passage of cold front usually very low base (200ft AAL)

• Lowering of base when there is a strong inversion and a decrease of humidity above

• Weak cold air advection in near surface layers

favourable

• Weak gradients in the rear of a trough after RA/SN was observed

LOW STRATUS

• Dry or unstable cold air advection from NW to NE

formation

unfavourable • When near surface winds >160° and little wind shear with height

• ST-layer less than 1000ft in thickness

• Lifting of base during weak inversion and weak decrease of humidity above

• (almost) no lowering of base between 03:00 and 06:00

favourable

• strong warm advection of warm air at 850hPa (lowering of tops)

• ST-layer thicker than 1000ft, from 1500ft upwards the ST-layer remains constant

• Lowering of base between 03:00 and 06:00 and no lifting of base between 06:00 and 09:00

dissipation

unfavourable

• Wet surface or fresh snow Table 4.4: Factors that favour formation/dissipation of a low stratus deck in Vienna (Fritzl, 2002)

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4.4 Impacts of low RVR on air traffic at Vienna International Airport A study by Peer (2003) deals with the various weather parameters that cause aircraft arrival and departure delays at Vienna International Airport. One of the first steps of the analyses of METAR code data (METAR codes are given out half hourly) from the year 2002 reveals that the weather phenomenon reported most in December was mist (255 times) followed by rain (171) and snow fall (138). Freezing mist was reported 133 times and freezing rain 53 times. Then freezing drizzle (44), fog (36), drizzle (23) and freezing fog (15) follow. Fog patches, shallow fog and freezing fog patches were reported 7, 5 and 1 times, respectively. The weather conditions that have occurred most frequently during the whole year 2002 are mist (reported 1323 times) and rain (reported 1253 times). All in all, 17539 MEATR codes were investigated in this study. A detailed analysis was then done for the columns “surface visibility” and “clouds” in the METAR code form. Figures 4.5 and 4.6 show distributions of different visibility categories during individual seasons. The categories are 50-200m, 250-400m and 500-900m. During winter, two of the lowest visibilities are presented with percentages above half (Figure 4.5). 74% of all cases of reported visibilities are below 200m and 65% of visibilities between 500 and 900m. The percentages are in relation to the total number the named visibility class was reported. As example: during the three winter months December, January and February, the visibility class “500-900m” was reported 108 times (Figure 4.6); which is 65% (Figure 4.5) of all reports for that category issued during 2002.

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season

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tive

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renc

e (%

)

50-200m250-400m500-900m

Figure 4.5: relative distributions of visibilities (<1000m) during the individual seasons (from Peer, 2003)

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Figure 4.6: total number of reported visibilities below 1000m in MEATR code form during 2002 at VIE (from Peer, 2003) Low visibilities in winter are expected because of the frequent occurrence of e.g mist, fog or snowfall. During summer, those low categories are completely absent. Spring is a “smooth” month with low percentages. Low visibilities may only be produced during rain or mist. In autumn with weather conditions such as mist and the maximum of reported number for rain and drizzle the percentages are little higher. Furthermore it is interesting to put weather phenomena in relationship with aircraft delays. In the study by Peer (2003), the distribution of departure delays was related to the occurrence of selected weather conditions. The increased number of cases of departure delays during the summer months is similar to the number of reported thunderstorms. Both have a maximum in July with 133 reported cases of departure delays and 47 reported cases of thunderstorms. May and September show low values for both thunderstorms and fog. Departure delays have their minimum values with 22 reported cases in May and 19 in September. High values of departure delays in February and December are associated with a high number of reported fog conditions. In summary, this study revealed that there is a close relationship between weather and delays. These delays then lead to additional costs for airlines, as already discussed in chapter 2.4.2. More precise forecasts could help airlines to minimize costly replanning and permit them to take mitigation actions.

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5. Short-term forecasts of low ceiling and visibility Although numerical modelling of meteorological processes continues to improve in spatial and temporal resolution, there still exist processes that cannot be forecast in these models. Physical processes that are not completely understood or those beyond the resolution of the models still require alternative methods for their analysis and prognosis. Therefore, various forecasting techniques have been developed. The installation of additional instruments, for example special radars, or different statistical forecasting methods such as MOS (Model Output Statistics), is the outcome of studies dealing with low visibility/ceiling and forecasting these phenomena. 5.1 High density surface weather observations In a study by Vislocky and Fritsch (1997) several methods of generating very short term (0-6 h) forecasts of ceiling and visibility were investigated and compared:

1) An observations-based system (OBS-based) is one in which the potential predictors consist of weather observations from a network of surface stations along with climatological terms. In this forecast method, the future weather conditions are predicted through a time-lagged statistical relationship with a network of surface weather observations at the initial time and several climatological terms. To reduce the enormous number of potential predictors to a reasonable level without sacrificing forecast accuracy, several pilot studies were undertaken. The outcome of these studies was that the optimal number of stations to consider in the network increases with the lead time. For a 1-h forecast, predictor observations at the 10 stations closest to the forecast site (including the forecast site itself) were optimal. For 3- and 6-h forecasts, observations at the 25 and 40 closest stations respectively, were optimal. As a result of these pilot studies, the final predictor set considered for the OBS-based system consisted of observations of the predictand variable at the forecast site and the 10, 25 and 40 nearest surrounding sites for the 1-, 3- and 6-h forecasts, respectively. In addition, spatially smoothed values for the predictors were also included in the final predictor set. Lastly, several climatological terms, such as sine and cosine of day of year, were incorporated into the final list of potential predictors.

2) In the traditional MOS-based approach potential predictors consist of 3 factors

being: Nested Grid Model (NGM) output, the latest observation from the forecast site and thirdly, climatological variables. MOS is a method of optimizing performance by post-processing Numerical Weather Prediction (NWP) output and surface observations. In the MOS procedure, observations and surface weather (e.g. predictands such as ceiling and visibility) are correlated to NWP model forecasts (e.g. predictors such as relative humidity, vertical velocity, and precipitable water) and climatological variables (e.g. station climatology, solar day) by using multiple linear regressions. For many

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predictands, the latest surface observation (at the forecast site), prior to the distribution of the NWP model output, is also used as a potential predictor in the MOS scheme. Not surprisingly, the observation-based predictors are typically the most important terms in short-term MOS forecast equations and are often selected first by the regression procedure. Given the relative importance of the local observation of the MOS scheme, and the competitiveness of the most recent observation, it is reasonable to expect that very short term MOS forecasts could be improved by also including weather observations from an entire network of stations surrounding the forecast site. However, to conform with current operational practice, only those surface observations from the forecast site at the appropriate initial time (0300 or 1500 UTC) were utilized as potential predictors.

3) Persistence climatology possesses potential predictors, which consist of the

latest observation of the predictand variable at the forecast site and several climatological terms. Here, a future weather variable for a given location is predicted through a time-lagged statistical relationship with the initial value of that from the forecast site and several climatological terms. Persistence climatology is the climatology of the relationship between an initial condition and the presence or absence of that condition at the forecast time. In former studies it was shown that simple persistence forecasts are highly competitive with subjective National Weather Service (NWS) categorical forecasts of ceiling and visibility at short lead-times. Since persistence climatology represents the expected value of the future observation (based on a long history of data) given the current observation (and climatological terms), it is a more difficult competitor to beat than the simple persistence method mentioned above.

The purpose of the study by Vislocky and Fritsch was to determine if an observations-based guidance system that uses a network of surface observations as the primary source of predictive information can be used to improve very short-term forecasts of aviation weather parameters. Each forecast method was tested on independent data for 25 stations in the eastern United States. Two parameters (ceiling and visibility) were forecast for eight thresholds, three lead times (1, 3, 6 h) and two initialization times (0300 and 1500 UTC). Least squares multiple linear regression was used to develop the predictive equations for each forecast method. Predictors were added one at a time to the model by selecting the term that, when combined with the variables already in the model, contributes the most additional reduction of variance. This selection procedure continues until the next best predictor to be added does not reduce the variance beyond a level deemed significant by the developer (0.001 F-test significance level). Probabilistic forecast equations were then developed for several thresholds of ceiling height and visibility for each forecast technique. These thresholds were based on values significant for aircraft operations. Forecasts were then generated for each forecast method across the various combinations of predictand, location, initial time and lead time. Next, these forecasts were verified and finally skill scores were computed for the OBS-based and MOS-based methods that represent the percentage improvement (reduction) of the mean squared error over persistence climatology. The results can be summarized as follows.

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Both the OBS-based and MOS-based forecast methods show substantial improvement over persistence climatology, even at the 1-h projection. This is particularly encouraging, since the use of persistence as a forecast technique provides a formidable benchmark at very short lead times. Improvements of the OBS-based system over persistence climatology averaged nearly 12 %. The percentage improvement typically increased with lead time. Additionally, the OBS-based system also outperformed the MOS-based technique at the 1- and 3-h projections. Performance of the OBS-based and MOS-based systems was similar at the 6-h projection, which appears to be near the crossover point when the NGM guidance becomes more important than the observations in terms of predictive input. In addition, with an OBS-based guidance system, the automated procedure can be designed to provide updated predictions every hour as new observations arrive. Leyton and Fritsch (2003) investigated the potential improvements in observations-based statistical forecasting systems incorporating a higher-density of surface weather observations. The region of interest was an area spanning from the Great Plains to the western Great Lakes and from central Minnesota to southern Missouri. While this region contains a high-density network of observing sites, this has not always been the case. Prior to 1996 there were only 11 sites across the entire state of Iowa and its immediate vicinity. However, beginning in the spring of 1996, many more observing sites were commissioned throughout the state, ultimately resulting in nearly 50 hourly reporting sites. This large increase in resolution makes this region a natural candidate for testing whether or not higher resolution yields significantly more accurate forecasts of ceiling and visibility. In the investigation the following strategy was employed. Initially, a baseline forecasting system was developed using only data from the stations available prior to the increase in observing sites (for 10 stations across Iowa and Southern Minnesota). Secondly, an alternative forecasting system was developed for the same 10 sites using all available data, including the high-density observations. Finally, the results of this new forecasting system were compared to the results of the baseline system. All stations within the domain were considered as potential predictors in the development of the forecast equations. However, with such a high number of stations in the region, an enormous number of potential predictors are available. Thus, a method had to be developed to pare this number to a more reasonable one without sacrificing forecast accuracy. This was done in pilot studies. The main results from these pilot studies were similar to those found by Vislocky and Fritsch (1997): the optimal number of nearest neighbours to be considered in equation development increases with lead time. Moreover, observations of the predictand variable at the forecast site and its nearest neighbours offered by far the most predictive information. In this study several climatological terms were also included. Consistent with the work of Vislocky and Fritsch (1997), the performance of the baseline and alternative (i.e., high-density) OBS-based forecast systems were compared to the performance of persistence climatology. An analysis of the forecast equations revealed that typically 10-20 predictors were selected for the 1-h projections, 15-25 for the 3-h projections, and 20-35 predictors for the 6-h projections. For the 1- and 3-h forecast, the most valuable predictor was the most recent observation of the predictand variable at the forecast site. The results of the study can be summarized as follows:

• Both the baseline and high-density forecasting systems show substantial improvement over the performance of persistence climatology

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• For a 1-h (3-h) forecast, the high-density forecasting system provided an additional 2%-4% (0%-1.5%) reduction in mean square error to that of the baseline forecasting system; a 20%-25% (0%-8%) increase in skill.

• The lack of improvement over persistence climatology for all predictand thresholds with lead times of 6 h indicates that low visibility and ceiling events typically do not persist for more than 3-5 h in this region. Because these events are not typical of climatology, persistence climatology forecast converge toward climatology forecasts, resulting in persistence climatology forecasts being a more formidable competitor.

In figure 5.1 the percentage improvement of the mean squared errors (MSE) over persistence climatology are plotted for both the baseline forecasting system and the high density forecasting system when considering a 1-h lead time (3-h lead time respectively).

Figure 5.1: left hand side: Summary of 1-h forecast improvement right hand side: Summary of 3-h forecast improvement (Leyton and Fritsch, 2003) Overall, the results indicate that it is possible to provide more skillful short-term weather forecasts than those obtained from current observations-based forecast methods. A higher density of weather observations reduces the chances of mischaracterizing an event and, therefore results in more accurate probabilistic categorical forecasts. Finally, a careful cost-benefit analysis is then necessary to identify whether the money saved by improved forecasts warrants the money spent on the construction of additional observation sites, knowing that a majority of the sites may not be selected because they provide little additional independent data. 5.2 High-frequency surface weather observations The impact of high frequency surface weather observations on short-term probabilistic forecasts of ceiling and visibility is investigated in a study by Leyton and Fritsch (2004). It is possible that additional forecast skill is gained if higher-frequency weather observations existed in addition to the hourly observations. Such observations became available in the mid-1990s as observations at 5-min intervals were recorded and archived at selected Automated Surface Observing Systems (ASOS) around the United States.

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Figure 5.2 (top) presents an example of high-frequency fluctuations in visibility at Bridgeport (BDR) from 17:00 to 22:00 UTC 27 February 2000. The solid line depicts the visibility conditions provided by the 5-min observations, while the dashed line depicts the conditions provided by the hourly observations. The trace of hourly observations assumes that conditions at top of the hour remain constant through that hour. Obviously, different decisions could be made, depending on which set is available. Figure 5.2 (bottom) presents a similar situation for Vienna Airport, using 1-min and half-hourly observations. Naturally, such a high variability in visibility as shown in figure 5.2 is not always present. On the other hand, hourly (or half-hourly) observations do not always represent the true character of an event. An automated system that captures the variability of an event would likely improve the decision making process.

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Figure 5.2: top: Trace of visibility conditions at BDR from 17:00 to 22:00 UTC 27 Feb 2000, using 5-min observations (solid) and hourly observations (dashed); (Leyton and Fritsch, 2004) bottom: Trace of visibility conditions at LOWW from 04:00 to 10:00 11 Jan 2000, using 1-min observations (solid) and hourly observations (dashed); Trace of hourly observations assumes that conditions do not change between observations;

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As low ceilings and visibilities plague the north-eastern United States in the cold season and as a relative high density of stations with high-frequency observations are available, the New York City area was chosen for this study. In this study a baseline forecasting system was developed, in which only standard hourly observations from the forecast site and other neighbouring stations were used for making 1-h forecasts. However, the need to make a 1-h forecast can arise at any time, not just at top of a given hour. If only hourly observations are available, the most recent observations may become less reflective of current conditions as that hour progresses. Therefore, an alternative forecasting system was developed, utilizing high-frequency weather observations. A network of surface observations was used as predictors in a multiple linear regression technique to produce forecast equations. The technique identified those predictors that were significant in forecasting a low ceiling and low visibility. Linear regression was utilized to reduce the pool of potential predictors to a manageable size, followed by logistic regression to derive the final forecast equations. Because the high-frequency dataset spanned only 5 yr, a cross validation technique was used to create multiple independent datasets. Equations were then derived from each of five dependent datasets and tested upon the corresponding independent dataset. For each high-frequency observation site, a single forecast equation was derived for all hours of the day and each five predictands (three ceiling and two visibility thresholds). Lead times of 1h, as well as 5-55 minutes were considered. Skill scores were computed that represent the percent improvement of the mean-square error (MSE) over persistence climatology by the probabilistic forecasts. For lead times shorter than 1 hour, skill scores were also computed that represent the percent improvement of the mean squared error over persistence. This study revealed that the inclusion of high-frequency observations with standard hourly observations results in additional forecast skill. Because the need to make a forecast can arise at any time, 1-h forecasts were initialized at times other than the top of the hour, these additional observations reduced the MSE by an additional 2% - 4% over the baseline system. For 1-h forecasts made at 15, 30, and 45 minutes past the hour, this reduction in MSE gradually increased to 5%-10%, 10%-15%, and 14%-18%, respectively. Moreover, the availability of these observations allows for forecasts with lead times less than 1 h. With as little as a 5-min lead time, the rapid-update forecasting system reduced the MSE by 0%-3% over persistence climatology and 1.5% -4% over persistence. As the lead time increased, the reduction in MSE over persistence climatology and persistence increased, such that with a 55-min lead time the reductions were 11.5%-19% and 21%-24%, respectively. In general, these results indicate that more skilful short-term weather forecasts can be attained by incorporating high-frequency surface observations in current observations-based forecast methods. An updated observation every 5 min reduces the likelihood of mischaracterizing an event, and therefore results in more accurate probabilistic categorical forecasts. In addition, the utilization of high-frequency observations addresses two limitations in observations-based forecasting – forecasts can only be updated at the top of the hour, and 1-h forecasts my be too coarse for time-sensitive phenomena, such as low ceiling and low visibility.

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5.3 Fuzzy logic in the prediction of ceiling and visibility Most of this information in this chapter was derived from various articles by Hansen (Hansen, 2000; Hansen and Riordan, 1998; Hansen, 2005). Also, information from articles by Murtha (1995) and by Hicks (Hicks et al., 2003) were used in this thesis. 5.3.1 Basics “Fuzzy logic is a superset of Boolean logic dealing with the concept of partial truth – truth values between ‘completely true’ and ‘completely false’. It was introduced by Dr. Lotfi Zadeh in the 1960’s as a means to model the uncertainty of natural language.” (Free On-line Dictionary of Computing, http://foldoc.doc.ic.ac.uk/foldoc); It is an extension of fuzzy set theory that was developed approximately 40 years ago. In meteorological systems, the use of fuzzy logic began about ten years ago. The term fuzzy actually refers to the gradual transitions at set boundaries from false to true. The intent of fuzzy set theory was to alleviate problems associated with traditional binary logic, where statements are exclusively true or false. Fuzzy logic allows something partially to be true and false. It is currently used in many different fields: business, system control, electronics and traffic engineering. But there are also many fuzzy logic based systems that deal with environmental data: agriculture, climatology, ecology, fisheries, geography, geology, hydrology, meteorology, mining, natural resources, oceanography, petroleum industry, risk analysis, and seismology. The technique can be used to create solutions to problems based on vague, qualitative, incomplete or imprecise information. A simple example is the following: Is a man who stands 171 centimetres considered to be tall? Traditionally a threshold is defined, above which the man of a certain height is considered a member of the tall set and under which he is not. Using this logic with a threshold of 170 cm (above which a person is considered tall), e.g., one would conclude that two people who are of nearly identical height, 169 cm and 171 cm, fall into opposite categories of height, one short and the other tall. This is not how people think. Fuzzy logic allows the man to be part of the tall set and the medium set, and possibly even a short set. He may be considered to a larger degree a member of the medium set than he is of the tall set. A man who stands 190 centimetres will be to a higher degree a member of the tall set. The advantages of fuzzy logic are that fuzzy logic is effective for encoding knowledge from domain experts. For instance, such knowledge can control recognition of similarity between two weather situations. Additionally, three other important characteristics of fuzzy logic systems are worth mentioning:

1) Fuzzy sets are essentially math-free 2) It is not required that the variables are independent and normally distributed 3) The relationship between input and output can take any form – it need not be

linear

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A database query example illustrates the difference between fuzzy logic, which operates with linguistic, realistic variables, and classic logic, which operates with Boolean, discrete variables. Suppose a marketing business is interested in identifying employees who have high potential. It could search its employee database for all employees who are young and who have high sales. Two approaches to querying the database are crisp range based and fuzzy set based. The crisp approach is depicted in Figure 5.3 (a) - (c). With the crisp approach, one specifies a discrete range based query as follows:

Young ⇔ age ≤ 25 years High sales ⇔ sales ≥ $500,000 per year

If there is an employee who is 26 years old who averages $1 million per year in sales, the crisp search would fail to identify this employee. This employee has “zero membership” in the overlap of the specified crisp sets. Yet, most people would reasonably think that this person is young and has high sales. The fuzzy approach is presented in Figure 5.3 (d) - (f). With the fuzzy approach, one specifies a fuzzy set based query, in which fuzzy sets determine degree of membership in the sets young and high sales.

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Figure 5.3: Crisp sets and fuzzy sets. Functions for dual membership in two sets. Arrows show how a 26-year old million-dollar-selling employee is accorded different levels of membership in the set “young with high sales” by crisp sets and by fuzzy sets. Using crisp sets, membership equals zero, whereas using fuzzy sets membership equals 0.9. The latter membership is more consistent with how people think.

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The dual membership in the sets of those “young with high sales” for the 26-year-old, million-dollar-selling employee is calculated as follows.

max{µyoung, µhigh sales} = max{0.9, 1.0} = 0.9 The employee has 0.9 degree of membership in the specified fuzzy sets. This is consistent with the view that most people would have that this person is young and has high sales. Another simple example is the one drawn in figure 5.4. In this figure Fuzzy logic and the traditional approach to a problem are again compared against another. The two lines drawn describe the similarity of temperatures. Whilst the fuzzy set gives information about the degree of similarity, the classical theory loses information about the degree of similarity. In this example the traditional approach uses two defined thresholds between which two temperature values are considered to be similar. Outside this defined area the temperatures are considered to be not similar. In fuzzy logic there is no such sudden break, which makes it a very helpful and practical tool for the solution of meteorological problems: in the radiation fog formation process light winds can be favourable. If, for example, wind speeds of 3 kt are considered to be favourable for the formation of fog, also higher or lower wind speeds will still contribute to the development of fog, only to a lower degree.

Figure 5.4: Fuzzy Set Theory versus Non-fuzzy set In general, a problem to be solved is referred to as a system. System inputs are those variables that determine the solution to the problem. In the radiation fog example system inputs are the values of dew point, dew point spread, the rate of change of the spread, the wind speed and the sky coverage. These system inputs are readily available, and their values are typically considered when a forecaster decides if or when radiation fog will develop. System output in the current example is the probability of radiation fog within a certain period of time in the near future. The system inputs are categorized into physically significant domains called fuzzy sets. Fuzzy sets are simply qualitative descriptions of the chosen domains of the inputs, each of which is thought to have a specific effect on the output. A detailed fuzzy-logic example is given in the Appendix D.

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The determination of the fuzzy sets is derived solely from experience. A developer of a fuzzy system must choose these categories such that reasonable system outputs will be obtained. This is the critical process of tuning the system. It is here where the experience of the system developer becomes very important. The fuzzy sets are quantitatively defined by membership functions. These functions are typically very simple functions that cover a specified domain of the value of the system input. The functions are generally trapezoids, although simpler functions such as triangles and even delta functions are often used. Each value of system input will belong to at least one fuzzy set and very likely more than one fuzzy set. This is possible because during construction the neighbouring fuzzy sets are made to overlap. 5.3.2 Fuzzy logic for short term weather forecasts In the USA a fuzzy logic forecast system produces automated ceiling, visibility, wind and weather forecasts for 465 terminal locations. These forecasts are generated upon arrival of each new surface observation (METAR) and are therefore always current. Inputs to the forecasts include the latest METAR, NGM (Nested Grid Model) MOS, AVN (Aviation Model) MOS, LAMP (Local AWIPS MOS Program), and additional output from the RUC (Rapid Update Cycle) model. The fuzzy logic forecast system consists of eight fuzzy systems, including four for ceiling and four for visibility. The fuzzy systems were developed from a sample of forecast verification data for 465 forecast locations, using forecasts issued at 00 UTC, 06 UTC, 12 UTC and 18 UTC and subsequently verified 3, 6, 9 and 12 hours after issuance. After gathering the developmental input/output data a systematic approach was employed to develop the fuzzy systems. Use of the fuzzy systems requires the latest METAR and guidance data as inputs. Weighted outputs from the eight fuzzy systems produce the final ceiling and visibility forecasts. Real-time verification statistics for almost a two-year period indicate that the fuzzy logic based ceiling and visibility forecasts have shown improvement not only over guidance forecasts from numerical models, but over official National Weather Service (NWS) forecasts as well. Each ceiling and visibility forecast is verified by “category error”, which is defined as the absolute value of the forecast MOS category minus the observed MOS category. For the period September 2000 through July 2002 over 8 million forecast were generated and verified. The fuzzy forecasts showed lower root mean squared errors (RMSE) than persistence, all the other products and the official NWS TAFs (figure 5.5).

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Figure 5.5: Ceiling root mean square error, in MOS categories (Hicks, 2003) Verification statistics were computed for forecasts made during the same period when Instrument Flight Rule ceiling and/or visibility conditions were either forecast or observed. The results for IFR ceiling conditions are shown in table 5.1, and results for IFR visibility conditions are shown in table 5.2. For IFR ceiling conditions, performance of the fuzzy forecasts was very similar to the TAF for probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI). The fuzzy forecasts did have a slightly overall score with CSI of 0.26.

Reliability = FOH = hits / (hits + false alarms) Probability of Detection = POD = hits / (hits + misses) False Alarm Ratio = FAR = false alarms / (hits + false alarms)

IFR ceiling conditions POD FAR CSIpersistence 0.40 0.61 0.24NGM MOS 0.42 0.64 0.24AVN MOS 0.36 0.61 0.23LAMP 0.53 0.76 0.20RUC 0.13 0.68 0.10fuzzyCV 0.32 0.41 0.26TAF 0.31 0.45 0.25

Table 5.1: Comparative verification for IFR ceiling events (3, 6, 9, 12 hrs); for explanation of abbreviations see text); (Hicks, 2003)

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IFR visibilit conditions POD FAR CSIpersistence 0.30 0.71 0.17NGM MOS 0.40 0.71 0.20AVN MOS 0.28 0.70 0.17LAMP 0.36 0.67 0.21RUC 0.11 0.74 0.09fuzzyCV 0.14 0.37 0.13TAF 0.23 0.53 0.18

Table 5.2: Comparative verification for IFR visibility events (3, 6, 9, 12 hrs); for explanation of abbreviations see text); (Hicks, 2003) For IFR visibility conditions, the critical success index of the fuzzy forecasts (0.13) was a little worse than the TAF and the guidance forecasts, except for the RUC. The lower probability of detection (0.14) was the main reason for the lower CSI. However, the false alarm ratio (0.37) of the fuzzy forecasts was the best overall, indicating when the fuzzy forecast calls for IFR visibility conditions, they will probably occur. (Hicks, T., 2003) 5.3.3 Fuzzy case-based prediction of ceiling and visibility Case-based reasoning (CBR) is a method for solving problems by remembering previous similar situations and reusing information and knowledge about that situation. The original, basic idea is simple: A case-based reasoner solves new problems by adapting solutions that were used to solve old problems. (Riesbeck and Schank, 1989) Weather patterns repeat themselves—this is the basic idea behind the weather prediction technique called analogue forecasting. Analogue forecasting is a meteorological form of CBR. Analogue forecasting is based on the principle that the more similar the current weather situation is to a past weather situation, the more similar the upcoming weather will be to that which followed the past weather situation. In its strongest form, this principle implies deterministic weather prediction. However, in the real-world, chaos prevents determinism. Small differences between the initial states of two systems tend to grow exponentially over time and the two systems become increasingly dissimilar. Persistence climatology (PC) is a weather prediction technique that combines the best qualities of two basic weather prediction techniques: persistence forecasting and climatologically forecasting. PC is a form of analogue forecasting and thus a version of CBR. Aamodt and Plaza (1994) describe CBR as a four step process:

• Retrieve the most similar case or cases. • Reuse the information and knowledge in that case to solve the problem. • Revise the proposed solution if necessary.

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• Retain the parts of this experience likely to be useful for future problem solving.

These steps are illustrated in figure 5.6:

Figure 5.6: CBR-cycle (Hansen, 2000) Fuzzy logic is especially useful for CBR because: “CBR is fundamentally analogical reasoning, analogical reasoning can operate with linguistic expressions, and fuzzy logic is designed to operate with linguistic expressions. Fuzzy logic emulates human reasoning about similarity of real-world cases, which are fuzzy, that is, continuous and not discrete. For example, using fuzzy sets elicited from a weather forecaster who is experienced at comparing and evaluating similarity between weather cases, fuzzy logic emulates the forecaster at the task of recognizing good analogues.” (Hansen, 2000); The CBR process of matching cases can be carried out by the fuzzy logic process of measuring the degree of similarity of cases. One problem with representing weather cases according to the membership of those cases’ attributes in crisp categories is that such categories may not accurately reflect the level of similarity between cases, as illustrated in figure 5.7.

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Figure 5.7: Crisp categories may not accurately reflect the level of similarity between cases. Such categorization may produce counterintuitive results. For example, the values of points A and B are similar and the values of points B and C are dissimilar, but points A and B fall into different categories and points B and C fall into the same category (Hansen, 2000) CBR and fuzzy logic both deal with how to determine degree of similarity, but they tend to use different approaches. CBR commonly deals with features, geometry, and structure, whereas fuzzy logic deals explicitly with uncertainty and ambiguity expressed intentionally by humans when they are asked to describe similarity. Fuzzy words describe uncertainty intentionally and fuzzy sets represent the intended uncertainty. Fuzzy CBR is a type of CBR that uses fuzzy methods to represent and compare cases, and to form solutions. The foundation of the fuzzy k-nearest neighbour (k-nn) technique is the k-nn technique. The definition of k nearest neighbours is trivial: For a particular point in question, in a population of points, the k points in the population that are nearest to the point in question. Finding the k-nearest neighbours reliably and efficiently can be difficult. How “nearness” is best measured and how data is best organized are challenging, non-trivial problems. The WIND-1 system (Hansen, 2002) is a forecasting system that consists of two main parts:

• A large database of weather observations: the database is a weather archive of over 300,000 consecutive hourly weather observations.

• A fuzzy k-nn algorithm: the algorithm measures the similarity between temporal cases, past and present intervals of weather observations. The algorithm is tuned with the help of a domain expert - a weather forecaster experienced in noting similarities between cases.

This system combines fuzzy logic with case-based reasoning. A flowchart of how this system works is shown in figure 5.8. In table 5.3 the differences between classic case-based reasoning and fuzzy-case-based reasoning are shown.

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Figure 5.8: Fuzzy case-based reasoning flowchart (Hansen, 2000)

Classic CBR Fuzzy CBR Uses abstract indexing rules to determine classes

Describes cases with their numerical dimensions and nominal types.

Revises the case memory with tested solutions

Uses a large case base of continuously accumulating actual cases.

Attempts to repair solutions in a potentially endless loop.

Bases solution on a weighted median of numerous similar cases and associates a confidence in the solution based on the spread of those cases.

Perform the series of operations: Proposed Solution → Test → Failure Description → Explain → Predictive Features → Indexing Rules.

Obtains knowledge about predictive features through knowledge acquisition from domain expert who explains similarity with fuzzy words.

Table 5.3: Differences between classic CBR (Case Based Reasoning) and fuzzy CBR. (Hansen, 2000) Five sets of experiments were conducted. In each set of experiments the parameters of WIND-1 were systematically changed and the resultant effects on forecast accuracy were measured. The fixed parameters (independent variables) are: the attribute set, the number of analogues used to make forecasts, the size of the case base, and the fuzzy membership functions (i.e., level of fuzziness in the similarity-measuring sets). The outputs (dependent variables) are, for each individual forecast,

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forecast values of cloud ceiling and visibility, and, for each set of experiments, a summary of the accuracy of all the forecasts made. In each set of experiments, 1000 simulated forecasts are produced. For purposes of comparison, the same 1000 randomly chosen hours are used in each set of experiments. During the forecast process, the outcome of the present case is hidden from WIND-1. WIND-1 produces a forecast for the present case based on the outcomes of the k-nn in the case base, the k most analogous past cases for the present case. After the forecast process, the accuracy of the forecast is verified by comparing the forecast with the then unhidden outcome of the present case. From the frequencies of these outcomes, three meteorological statistics are calculated: Reliability (i.e., Frequency of Hits, or FOH), Probability of Detection (POD), and False Alarm Ratio (FAR).

Reliability = FOH = hits / (hits + false alarms) Probability of Detection = POD = hits / (hits + misses) False Alarm Ratio = FAR = false alarms / (hits + false alarms)

The first four sets of experiments serve a dual purpose. First, they test the contribution of individual components of the system. Second, they suggest how to adjust these components in order to maximize the accuracy of the system. The last set of experiments pits the system against a competitive prediction technique, persistence forecasting. The outcome of this last experiment is shown in figure 5.9.

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Figure 5.9: Accuracy of system compared to benchmark technique, persistence. Graphed values are accuracy of prediction for each hour in the 0-to-12-hour projection period. System configuration: k=16, length of case base = 35 years. (Hansen, 2000) As can be seen in the figures above, the experimental results suggests that the proposed analogue forecasting method has the potential to be more effective than persistence for short-term prediction of visibility and ceiling. Simulated analogue forecasts were more reliable and had fewer false alarms than the persistence-based forecasts for the six hours of the forecast period. All in all, the fuzzy case-based method produced forecasts that were 2% more reliable and which had 4% fewer false alarms than persistence-based forecasts.

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6. Data analysis The METAR-Report, issued by airport met offices in half –hourly intervals, includes (apart from many other parameters, such as temperature, dew-point…) RVR (Runway Visual Range) data (see Annex C). After the number, showing the RVR, there might be a letter: “U”, “D” or “N”. These Trends tell the user (pilots…) in which way the RVR was changing within the last ten minutes. “U” stands for “Upwards” (RVR was improving), “N” for “No change”, and “D” for “Downwards”. But very often users tend to mis-interpret these letters. They think that the letter is an indicator for the RVR tendency in the near future. 6.1 Number of Cases In the first step of the data analysis we used METAR-Code data of Vienna Airport. The goal was to find out, if there are enough cases of fog to investigate them using various statistical techniques. Vienna airport has four RVR measuring sites: RVR11, RVR16, RVR29 and RVR34 (see figures 4.2-4.4). This is necessary, as Runway Visual Ranges are largely determined by local effects. So the data vary strongly, depending on where they come from. For example, RVR11 registered 12 cases of more than 3 RVR measurements below (or equal) 1500m in succession for the year 2003, RVR16 38 cases, RVR29 12 and RVR34 43 cases. Due to these local differences, we will investigate all data for the different RVR measuring sites separately. The outcome is shown in the following figure 6.1. It shows the distribution of cases with RVR measurements below (or equal) 1500m that last a certain time span for Vienna Airport, years 1990-2003. The events the events that last longer than 15 hours are put together into categories: category “15-19.5h” includes all events where the number of RVR measurements below (or equal) 1500m in succession lies between 30 and 39 (including 30 and 39).

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Figure 6.1: distribution of cases with RVR measurements below (or equal) 1500m that last a certain time span; related to the total number of METAR code reports during the investigated time period (239679 METAR code reports); Vienna Airport 1990-2003

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It is obvious that there are enough cases for further investigations for this airport. These figures show that most of these cases are short lasting (only half an hour). It can also be seen that there is a big difference in the number of RVR events reported at the four different measuring sites. It is also interesting that at measuring site RVR34 almost no long-lasting RVR events occurred whilst at the other measuring sites events that last 40 hours and more occurred. It is no surprise that most of these cases are found in January, February and from October until December. Many of these cases appear in the early morning hours between five to seven a.m. local time. At Vienna airport RVR16 registered 18 out of a total number of 38 cases in these early morning hours in the year 2003. 6.2 Trends To find out if RVR tendencies follow a typical pattern we used a decision tree (Figure 6.3). The data used include all cases of RVR where there were more than 3 RVR measurements below (or equal) 1500 m in succession. First, we compared the first RVR measurement with the second, than the second with the third. As outcome we obtain 9 different categories, which are shown in the following decision tree: RVR1 is the first RVR number, 2 the second RVR number and 3 the third RVR number in the row.

Figure 6.3 decision tree for the analysis of the three RVR measurements below (or equal) 1500 m in succession; RVR1 is the first RVR-value in the row; letters A to I stand for the different categories; colours indicate the various combinations of the different categories The letters A to I stand for the different categories. The three categories A, E, I are the most important ones. Category A includes all cases where there was a continuous increase of RVRs during an event, E includes all cases with no change at all, and category I includes all cases with a continuous decrease of RVRs.

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We put category C and G together in one category, as they include similar cases: category C includes cases with an increase and a following decrease of RVRs. Cases in category G show the exact opposite tendency. Thus, this new category (CG) includes all cases that show a change of tendency. Category B, D, F, and H were also put together. This new category now includes all cases where the first RVR is equal to the second (respectively the second equal to the third) and the second is different to the third (respectively the first different to the second). Figure 6.4 shows how many cases are included in different categories. In this figure the different categories are still separated. However, in figures 6.5 and 6.6 the categories are put together. Again, this investigation was made for Vienna airport, years 1990-2003 for the four different measuring sites.

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Figure 6.4 Number of cases in the different categories; the letters stand for the different categories that were created; for example, “A” includes all cases of RVR measurements below (or equal) 1500 m with a continuous increase; (for more details see the decision tree in figure 6.3.); Vienna Airport, 1990-2003

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Figure 6.5 Number of cases in the different categories; the letters stand for the different categories that were created; for example, “A” includes all cases of RVR measurements below (or equal) 1500 m with a continuous increase; (for more details see the decision tree in figure 6.3.); in this figure, the categories with a change of tendency (C and G) are already put together; the same was done for categories B, D, F and H; Vienna Airport, 1990-2003 It is surprising that category A (increase) does not include more cases although also RVR cases with only two RVRs in a row and the third RVR =-99 were also used. For all RVR measuring sites category E includes most cases. So it is very probable to have no change of RVRs if there was no change between the last two RVRs. Also category CG (change of tendency) and category BDFH include many cases. Going a step farther and putting these categories together it is obvious that most RVR do not follow a specific pattern, as can be seen in figure 6.6. This figure shows the percentages of the cases that are included in the different categories. About ten percent of all cases show an increasing tendency, even less cases (5% to 8%) a decreasing tendency. There are many cases where the runway visual range equals the two preceding RVR-values, but most cases (35% to 48%) show no clear tendency.

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Figure 6.6: Number of cases in the different categories; the letters stand for the different categories that were created; for example, “A” includes all cases of RVR measurements below (or equal) 1500 m with a continuous increase; (for more details see the decision tree in figure 6.3.); in this figure, the categories with a change of tendency (C and G) are put together with categories B, D, F and H; numbers are percentages of cases in the different categories related to the total number of reported tendencies in the METAR code form; Vienna Airport, 1990-2003

It is interesting that category A (increase) includes more events than category I (decrease). This indicates that on average decreases happen slightly faster than increases. Now it is interesting to find out if the “Ns” (no change of RVR), “Us” (upward tendency of RVR) and “Ds” (downward tendency of RVR) in the METAR-Code follow a specific scheme. For this investigation we used Data of Vienna Airport for the years 1996 to 2004. We did not use data before 1996 because RVR-Tendencies were only introduced in 1996. After the first glimpse on the data it was obvious that most of the trends were “Ns” (unchanged RVR). The following table (Table 6.1) confirms this subjective impression. All “Ns” (unchanged RVR), “Ds” (decreasing RVR) and “Us” (increasing RVR) were counted for the different RVR measuring sites.

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RVR11 RVR29 RVR16 RVR34No change 973 3211 3264 361Upward tendency 110 528 422 63Downward tendency 110 479 445 68"no change" in combination with RVR of 1500m 639 1684 1941 271Percentage of "N" appearing with RVR = 1500m 60 52 60 75

Table 6.1: Total number of the three trend symbols “N” (No change of RVR), “U” (increasing RVR) and “D” (decreasing RVR); the second last row shows the number of “N” that appear in combination with RVR values of 1500 m; the last row represents the percentages of “N” (related to the total number of “N”) that appear with RVR values of 1500m Table 6.1 again shows the differences between the four RVR measurement sites: RVR34 only registered 492 trends, whilst RVR16 registered 4151. It is also shown that most of the trend symbols are “Ns” (No change) and that the number of “Us” (upward tendency) and the Number of “Ds” (downward tendency) are almost equal. Looking at the data it was also obvious that most “Ns” appear with a RVR of 1500m. To confirm this all “Ns” were compared with the RVR. The outcome of this is shown in Table 6.1, fourth line. The percentage of “Ns” appearing with a RVR of 1500 is shown in the last line of Table 6.1: about two third out of all “Ns” go along with a RVR of 1500m. It is also interesting to find out how many “Ns”, “Us” or” Ds” are followed by none, a second, a third,…N, U or D. These frequency distribution curves are shown in Figures 6.7- 6.9. Due to the fact that RVRs also show an upward tendency if there is no RVR (RVR>1500m) after a certain number of RVRs, Figure 6.7 and Table 6.2 also contain “Us” that are followed by no RVR-tendencies at the upper limit of the measuring range (RVR>1500).

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Figure 6.9: this figure shows how often “D” (decreasing RVR) was reported in succession; for example 2 ½ h means that there were 5 “U” reported in succession in the METAR code form; numbers are absolute frequency of “D” in the various categories; Vienna 1996-2004 Usually, “U” (increasing RVR) and “D” (decreasing RVR) are not reported longer than 2.5 hours in succession. However, there can be found one value in figure 6.9, which is very surprising. First, we assumed that an error occurred during calculation these values. Then we found out, that 18 “D” in a row really happened: on November 11th, 2002 in the morning hours. METAR code data for this special event can be found in the Appendix E. To compare the “Us” (increasing RVR) and “Ds” (decreasing RVR) with each other, the frequency of occurrence of “U” (respectively “D”) over a certain time period without any interruption is shown in Table 6.2 and Table 6.3. For example: whenever there is one “U” (the first reported U in a row) at measuring site RVR29 then the probability that “U” will also be reported half an hour later is 22.5%.

RVR11 RVR29 RVR16 RVR341/2 h 53,6 54,5 55,2 33,31 h 28,1 22,5 20,4 41,31 1/2 h 3,6 3,6 3,6 4,82 h 0 0,8 1,2 02 1/2 h 0 0,2 0 1,6

Table 6.2: Number of “U” (increasing RVR) in succession (Numbers are percentages: frequency of “U” that are reported without any interruption over a certain time period in relation to the total number of “U”)

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RVR11 RVR29 RVR16 RVR341/2 h 62,7 67,7 63,4 77,91 h 15,5 11,3 10,8 8,81 1/2 h 0,9 2,7 2,7 1,52 h 0,9 0,4 0,4 02 1/2 h 0 0 0,2 0

Table 6.3: Number of “D” (decreasing RVR) in succession (Numbers are percentages: frequency of “D” that are reported without any interruption over a certain time period in relation to the total number of “D”) Although the total numbers of “Us (increasing RVR) and “Ds” (decreasing RVR) are nearly equal (Table 6.1), the percentage of two “Us” in succession (plus all those “Us” that stand at the very end of a fog-case) is about twice as high as the percentage of two “Ds” in succession. This means that there are fast decreases of RVRs and slower improvements of RVRs. It is interesting that the percentage of two “U” in a row at site RVR34 is about twice as high as at the other measuring sites. 6.3 Cases with a fast decrease of RVRs In the following section all fog cases with a fast decrease of RVRs (cases, with a drop of RVR from more than 1500 m to values below 500 m within half an hour) and where there are 3 (1 1/2h) or more reported RVR values below 500 m in succession are treated. These special cases are called in this thesis “RVR event”. It is interesting to find out, what these cases have in common and what happened before this rapid decrease of RVRs. The goal was to find one or more meteorological parameters that can be used as a predictor for low visibilities. For this task, temperature, dew point temperature, spread, wind speed and direction, cloud cover and hour of the day as well as various combinations of these parameters were investigated. The outcome is not very encouraging. Not a single one of these parameters seems to be a reliable predictor. Even if “ideal” weather conditions (conditions that seem to be perfect for the formation of fog) are present, low visibilities are unlikely and thus the false alarm rate in the forecasting process will be very high. 6.3.1 Hour of the day Firstly, we tried to find out at which times these rapid RVR decreases usually occur (figure 6.10).

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Figure 6.10: Hour of the day of rapid RVR decreases; hour “1” means that the first RVR value below 500 m was reported at either 01:20 or 01:50. The numbers in the figure are the number of cases with a rapid decrease of RVR at certain times related to the total number of fast decreases of visibility at the four different measuring sites; Vienna 1990-2003 Most fast visibility changes happen in the early morning hours, especially between 06:00 and 07:00. After 07:00 almost no fast drops of RVR can be expected. Then, at 19:00 the number of fast decreases of RVR starts to increase again. Then, we related the time of these rapid RVR events to the time of the sunrise of the various RVR events. This can be seen in figure 6.11.

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Difference to Sunrise

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-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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Figure 6.11: Rapid RVR decreases related to sunrise; values are the frequencies of RVR decreases at certain times before or after sunrise related to the total number of fast drops of RVR; negative values mean that the RVR event occurred before sunrise; for example “0” means that the rapid decrease of RVR happened at the following time span around sunrise: -0.5h <= RVR event < 0.5h (between half an hour before and after sunrise); Vienna 1990-2003 More than three hours after sunrise rapid RVR decreases are very unlikely to happen. Most fast RVR decreases occur around sunrise. One hour after sunrise is also a very critical phase. With increasing distance to sunrise (earlier or later in the morning) the probability of RVR events decrease. Four hours before sunrise shows relative high values for RVR11. More than four hours before sunrise RVR events are very unlikely, especially at measuring sites RVR11 and RVR34. The reason for this is unknown. 6.3.2 Dew point differences Figure 6.12 (top) shows a frequency distribution for temperature minus dew-point-temperature for these special cases. For each case the temperature and dew-point-temperature data relate to measurements taken one hour before the RVR was below 500m. This is to find out if it is possible to make a RVR forecast with temperature and dew point temperature data for the next hour. Figure 6.12 (bottom) shows the distribution of absolute dew point temperature values before a fast decrease of RVR. Also here data relate to measurements taken one hour before the fast decrease of RVR.

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Figure 6.12: top: Frequency distribution curve for temperature minus dew-point-temperature; data relate to measurements taken one hour before the RVR event; numbers are absolute numbers of cases in the different temperature categories related to the total number of RVR events; Vienna 1990-2003 bottom: relative frequency of dew point temperature before a fast decrease of RVR; data relate to measurements taken one hour before the RVR event; numbers are absolute numbers of cases in the different temperature categories related to the total number of RVR events; Vienna 1990-2003

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It can be seen that there was a dew-point-difference of 0°C or 1°C for most cases. One might assume that it is possible to use this effect for a short-term-RVR forecast. But this is not so easy: on the one side it is obvious that nearly all cases of a fast decrease of RVRs have a dew-point-difference of less than 2°C one hour before. But on the other side the conclusion that there will be low RVRs if there is a low dew-point-temperature would be wrong for many other cases. Not every case of such a low dew-point difference produces low RVRs, as can be seen in Table 6.4. Figure 6.12 (bottom) shows that a high number of fast decreases of RVR shows values of dew point temperature of zero degrees Celsius. The reason for this can be wet snow.

RVR11 RVR29 RVR16 RVR34 FAR(T=0°C) 99,91 99,84 99,67 99,86 FOH(T=0°C) 0,09 0,16 0,33 0,14 FAR(T=1°C) 99,96 99,90 99,81 99,92 FOH(T=1°C) 0,04 0,10 0,19 0,08

Table 6.4: False Alarm Ratio (FAR) and Frequency Of Hits (FOH) for values of spread of 0°C and 1°C; FAR shows the percentages of all measurements of spread = 0°C (respectively 1°C) that are followed by no reduction RVR event; FOH shows the percentages of all measurements of spread = 1°C that are followed by a RVR event Table 6.4 shows the number of cases where there was a low dew-point-difference (less than 2°C) but no RVR event (which would lead to a false alarm if the forecast was only based on this parameter) or RVR less than 500m (which would lead to a hit if the forecast was only based on this parameter) one hour afterwards. It can be seen that low differences between temperature and dew point temperature with no following RVR event happen quite often. Dew-point differences of 0°C and no reduction of RVR afterwards have values greater than 99 percent. For dew-point differences of 1°C these numbers are even higher. So dew-point differences are not the right method to forecast RVRs, if they are not combined with any additional information. At least, it is nearly for certain that after high dew point differences (spread greater than 2°C) no RVRs below 500m will appear. 6.3.3 Wind The next analysed parameters will be wind direction and wind speed. The goal is to find out if these parameters can be used to predict fast decreases of RVR. The wind data relate to measurements taken one hour before this fast decrease of RVRs. For wind direction the following wind direction categories are used:

“NW” (wind from 200-340°) “SE” (wind from 70-150°) “V” (variable direction) “Rest” (wind from any other direction).

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Figure 6.13: relative frequency of wind direction categories preceding rapid RVR decreases; data relate to measurements taken one hour before the RVR event; number of RVR events with a certain wind direction before the RVR event related to the total number of a certain wind direction reported; “NW”: wind from 200°-340°; “SE” = wind from 70°-150°, “V” = variable direction and “Rest” = wind from any other direction; Vienna 1990-2003 Looking at Figure 6.13 it becomes obvious that wind directions vary strongly before a fast decrease of RVRs. It is surprising that the NW component is higher than the SE component for RVR34 and that for RVR11 winds from the SE are observed most. Generally, there is no dominant direction that could be used as a predictor for low RVRs Much better correlated to low RVRs are wind speeds. It is no secret that high wind speeds are a fog killer and that low wind speeds, on the other side, favour the formation of fog (Cotton, 1989). For wind speeds the following categories were defined:

Category 1: 0-2kt Category 2: 3-6kt Category 3: 7-10kt Category 4: above 10kt.

Figure 6.14 shows the relative frequency of the different categories for the different wind speed categories for the various RVR measuring sites. For this task the number of RVR events with a certain wind speed before the events was related to the total number of a certain wind speed reported.

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0

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RVR11 RVR29 RVR16 RVR34

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< 3kt3-6 kt7-10 kt> 10kt

Figure 6.14: Frequency distribution of wind speed categories; number of RVR events with a certain wind speed before the RVR event related to the total number of a certain wind speed reported; Vienna 1990-2003 For hardly any case wind speeds exceed 6kt. Except for measuring site RVR11, the percentages of wind speed lower than 3kt are twice as high as the percentages of category two. So it is not very probable that low RVRs will occur if there are high wind speeds one hour before. The following table (table 6.5) makes clear, how low the probability of a fast decrease of RVR is and thus how high the false alarm rate (FAR) would be if only wind speed or wind direction were used as the only predictor in the fog forecasting process. For example, only 0.3% out of all wind speed cases with speeds less than 3 knots are followed by a fast drop of RVR!

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FOH RVR11 RVR29 RVR16 RVR34 < 3kt 0,04 0,16 0,30 0,18 3-6 kt 0,04 0,06 0,11 0,06 7-10 kt 0,01 0,01 0,02 0,01 > 10kt 0,00 0,00 0,00 0,00

FAR RVR11 RVR29 RVR16 RVR34 < 3kt 99,96 99,84 99,70 99,82 3-6 kt 99,96 99,94 99,89 99,94 7-10 kt 99,99 99,99 99,98 99,99 > 10kt 100,00 100,00 100,00 100,00

FOH

RVR11 RVR29 RVR16 RVR34 NW 0,01 0,01 0,03 0,02 SE 0,04 0,02 0,05 0,01 V 0,02 0,14 0,17 0,11 Rest 0,02 0,05 0,12 0,08

FAR RVR11 RVR29 RVR16 RVR34 NW 99,99 99,99 99,97 99,98 SE 99,96 99,98 99,95 99,99 V 99,98 99,86 99,83 99,89 Rest 99,98 99,95 99,88 99,92

Table 6.5: Frequency distribution of wind speed (respectively wind direction) categories; number of RVR events with a certain wind speed (respectively wind direction) before the RVR event related to the total number of a certain wind speed (respectively wind direction) reported; FAR = False Alarm Ratio; FOH Frequency of Hits; Vienna 1990-2003 Apart from that, it is also interesting in which way wind speed and wind directions together are correlated to a fast decrease of RVRs. For this investigation the wind-speed- and wind-direction-categories from above were combined. The outcome of this are 16 new categories (the cases in the four wind speed categories were divided into the four wind direction categories), that can be seen in Table 6.6. Numbers represent the absolute number of cases in the different categories.

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RVR11 < 3kt 3-6 kt 7-10 kt > 10kt NW 0,09 0,02 0,00 0,00 SE 0,00 0,10 0,03 0,01 V 0,03 0,00 0,00 0,00 Rest 0,03 0,04 0,00 0,00 RVR29 < 3kt 3-6 kt 7-10 kt > 10kt NW 0,09 0,03 0,01 0,00 SE 0,09 0,05 0,00 0,00 V 0,18 0,07 0,00 0,00 Rest 0,15 0,07 0,01 0,00 RVR16 < 3kt 3-6 kt 7-10 kt > 10kt NW 0,53 0,09 0,00 0,00 SE 0,27 0,11 0,01 0,01 V 0,24 0,07 0,00 0,00 Rest 0,40 0,15 0,06 0,00 RVR34 < 3kt 3-6 kt 7-10 kt > 10kt NW 0,18 0,07 0,00 0,00 SE 0,00 0,02 0,00 0,01 V 0,16 0,04 0,00 0,00 Rest 0,28 0,10 0,03 0,00

Table 6.6: Combination of wind speed and direction: numbers show the number of RVR events with a certain wind direction and wind speed one hour before the fast drop of RVR; number of RVR events with a certain wind speed and direction before the RVR event related to the total number of a certain wind speed and direction combination reported; Vienna 1990-2004 Winds from the Southeast favour fog formation because of the Lake Neusiedl. Winds from the South transport wet air from the lake northward. For this effect higher wind speeds are necessary. So fast decreases of RVRs correlated with winds from the SE usually go along with wind speeds of 3-6kt. In the next step plots of wind vectors for all RVR events with a fast decrease of RVR are created. The direction of the vector gives the wind direction and the length of the vector the wind speed. In these plots, a vector that points to the North (360°) indicates a wind direction of 180° (wind from the south). Thus, plots are created that show which “way” the wind took before it arrived at Vienna Airport. Of course, this is not absolutely correct, as the wind measurements taken at Vienna Airport do not represent wind speed and direction some kilometres away. However, this method makes it possible to “see” how wind speed and direction changed with time before a fast decrease of RVR. Figures 6.16 (a-d) show these wind vector plots for the different RVR measuring sites. The end-point of all the different vectors represents Vienna Airport (point zero in the diagrams). The last vector (the one that “ends” at Vienna Airport) is the measurement of wind speed and direction when the fast decrease of RVR occurred. The next vector in the row is the measurement taken half an hour before this change in RVR. All in all there are eleven vectors, the last representing wind speed and direction ten METAR code reports before the fast decrease of RVR: this means that

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the last vector in each row consists of values of wind speed and direction taken five hours before the RVR event. In the plots, variable wind speeds are not included (code -99 in the METAR code form: wind speed is in that case below 3 kt; see also Annex C), instead values of wind speed are set to zero. For additional explanation see figure 6.15.

Figure 6.15: explanation for figure 6.17 (a-d): the red arrow marks Vienna International Airport; the blue arrows are measurements of wind speed (in knots) and direction At measuring site RVR34 there is one case with very high wind speeds. This leads to a very obscure presentation of the data. Therefore, this RVR event cannot be displayed fully in the plot.

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Figures 6.16 (a-d): Wind speed and direction plots for fast decreases of RVR for the four measuring sites in the following order: RVR11, RVR29, RVR16, RVR34; for further explanation see also Figure 6.16 and text; for view reasons events with very strong winds cannot be displayed in these plots totally

It is interesting that before a fast decrease of RVR at measuring site RVR11, south-easterly winds are dominant and no north-winds are present. At the other three measuring sites the graphics look different. Due to the higher number of fast decreases of RVR, the presentations look more chaotic. Despite this chaos it is possible to see that many different wind directions are possible before a fast decrease of RVR. This is because fog can form for different reasons (see Chapter 3). Different weather situations also influence wind speed and direction. 6.3.4 Clouds Clouds also have an impact on fog formation. They are an important factor in the radiation-balance. The following definitions are used in the METAR-Code (see also Annex C) for total cloud cover: SCL…Sky clear (0/8) FEW…1/8-2/8 SCT…scattered (3/8-4/8) BKN…broken (5/8-7/8) OVC…overcast (8/8) VV0…vertical visibility not recognizable For this thesis we defined new cloud categories. All in all 25 conditions were necessary to include all different variations of cloud amount and height (these conditions can be found in Annex F). The outcome is three new categories that combine cloud conditions in different heights and with different cloud cover:

Kat1: 5/8 – 8/8 (OVC) Kat2: 3/8 - 5/8 (cloudy) Kat3: 0/8 - 2/8 (SKC)

Kat3 includes all these combinations with little (or no) cloud cover at low levels and possible high clouds. Kat1 includes overcast (or almost overcast) cloud conditions in low altitudes. Table 6.7 shows the relative frequency of cloud cover for Vienna 1990-2003. The data relate to measurements taken one hour before the RVR was below 500m. In this figure the climatology of cloud cover was included, meaning that the total number of occurrence of the cloud categories was counted. Then the number of cases in the different categories before a RVR event was related to this total number.

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followed by RVR event (FOH) RVR11 RVR29 RVR16 RVR34 OVC 0,03 0,07 0,15 0,07 cloudy 0,01 0,03 0,04 0,01 SKC 0,03 0,04 0,07 0,03 followed by no RVR event (FAR) RVR11 RVR29 RVR16 RVR34 OVC 99,97 99,93 99,85 99,93 cloudy 99,99 99,97 99,96 99,99 SKC 99,97 99,96 99,93 99,97

Table 6.7: Relative frequency of cloud cover preceding rapid RVR decreases; climatology of cloud cover is included, meaning that the total number of occurrence of the cloud categories is counted; then the number of cases in the different categories before a RVR event is related to this total number; data relate to measurements taken one hour before the RVR event; SKC=(almost) clear sky and eventually high clouds, cloudy=3/8-5/8, OVC=(almost) overcast sky especially in low levels; if cloud cover was the only predictor values could also be interpreted as Frequency of Hits (FOH) and False Alarm Rate (FAR); Vienna 1990-2003 It can be seen that fast decreases of RVR often go along with a clear sky. The probability that a clear sky will be followed by a reduction of RVR is much higher than in the other two categories. The safest category is category two. The fact that category one (overcast) shows higher values than category two indicates that before the occurrence of fog, a low stratus deck is present relatively often. The difference between RVR16 and the other measuring sites is greatest during clear sky conditions. This is because RVR16 is situated in a depression of the ground. Thus, a stable boundary layer can already form at RVR16 whilst there are still higher wind speeds present at the other measuring sites. This special situation leads to more fog events at RVR16. 6.3.5 Soil Wet soil favours the formation of fog. To find out to which degree this theory is fulfilled at Vienna Airport, the number of fast decreases of RVR events with wet soil was counted. As soil properties are not given in METAR reports, precipitation information was used to find out if the earth’s surface was wet or dry before a RVR event. When slight or moderate precipitation was reported within the last 24 hours before the RVR event more than six times, soil was defined to be wet. When heavy precipitation was reported once within the last 24 hours before the RVR event, soil was also defined to be wet. Otherwise we assumed that the soil was dry. At measuring site RVR11, wet soil was present three times (out of 24 events) before a fast decrease of RVR, at site RVR29 six times (out of 44 events), at site RVR16 eight times (out of 87 events), and at site RVR34 eleven times (out of 50 events). This shows that a wet soil or precipitation is not a very dominating factor for the formation of low visibilities at Vienna Airport.

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6.3.6 “Ideal” conditions Finally, all these weather conditions were combined to find out how many fast decreases of RVR show “ideal” weather conditions before the RVR event. For this task “ideal” weather conditions were defined as:

Spread = 0 or 1 ° Celsius, Wind speed is equal or lower than 3 kt, Cloud conditions of cloud category 3 (clear sky or almost clear) Wet soil

A second “ideal” weather conditions category was also defined. This category is almost the same as the one above. Only the “wet soil” category was exchanged by a category that includes all these events where dew point temperatures ranged between -2°C and 2°C:

Spread = 0 or 1 ° Celsius, Wind speed is equal or lower than 3 kt, Cloud conditions of cloud category 3 (clear sky or almost clear) -2°C ≤ dew point temperature ≤ 2°C

Then the number of RVR events that show these “ideal” weather conditions half an hour to three hours before the RVR event was related to the total number of fast decreases of RVR. The outcome is shown if figure 6. 17 (numbers are percentages).

RVR events with "ideal" conditions

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Figure 6.17: top: Relative frequency of “ideal” conditions before a RVR event, numbers are percentages in relation to the total number of RVR events; Vienna 1990-2003 bottom: same as top, only “wet soil conditions” replaced by absolute dew point temperature When interpreting these figures it should be noticed that these “ideal” weather conditions only occurred 344 (705 respectively for the second “ideal” conditions category) times out of all METAR code reports within the years 1990 to 2003. Often one METAR code report with “ideal” weather conditions is followed by one or more reports with “ideal” weather conditions. Thus, when defining a row of “ideal” weather conditions as one “ideal-weather conditions-event”, the number of occurrence of “ideal” weather conditions is reduced to 124 (285 respectively) events. In a final step we counted the number of “ideal” weather conditions that show any decrease of RVR below 500m within the next three hours: out of all “ideal” weather conditions (=124 events, 285 respectively), 4 percent (4 respectively) are followed by low RVRs at measuring site RVR11, 15 (19 respectively) percent at RVR29, 25 (22 respectively) percent at RVR16 and 10 (5 respectively) percent at RVR34. Finally, we also included the time of the day in these “ideal” conditions category. Only those “ideal” conditions that happened between 19:00 and 07:00 were counted as “ideal”. This change in the definition reduced the number of “ideal” conditions only slightly, from 285 to 247. This indicates that most “ideal” conditions occur during the night and early morning hours. The number of reported rapid RVR events did not change at measuring site RVR11 and RVR34 but was reduced a little at RVR29 and RVR 16. The numbers for these three different “ideal” conditions categories can be found in table 6.8.

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RVR 11 RVR29 RVR16 RVR34(A) 124 4.03 15.32 25.00 9.67(B) 285 3.86 19.30 22.46 5.26(C) 247 4.45 19.03 23.08 6.07

followed by RVR below 500m (%): FOHTotal number of "ideal" conditions

RVR 11 RVR29 RVR16 RVR34(A) 124 95.97 84.68 75.00 90.33(B) 285 96.14 80.70 77.54 94.74(C) 247 95,55 80.97 76.92 93.93

followed by no RVR event (%): FARTotal number of "ideal" conditions

Table 6.8: Ideal conditions followed by RVR below 500m (respectively no RVR event: bottom); numbers are number of “ideal” conditions that are followed by a fast drop of RVR in relation to the total number of “ideal” conditions; percentages can also be interpreted as Frequency of Hits (FOH); the difference to 100% gives the False Alarm Ratio (FAR), which is very high!

(A) Spread = 0 or 1 ° Celsius, Wind speed is equal or lower than 3 kt, Cloud conditions of cloud category 3 (clear sky or almost clear) Wet soil (B) Soil moisture replaced by dew-point temperature (C) Like (B) but also time of the day included

This shows again the differences between the different measuring sites. These numbers also show the problems that arise when forecasting RVR. Even if numerical models predicted meteorological parameters, such as temperature, humidity, wind speed and cloud cover perfectly, forecasting fog events would still be a great challenge. Although weather conditions for the formation of fog seem to be ideal, the probability of low RVRs is still relatively low. In the best case, the false alarm rate would still be 75%! This also shows that developing a fuzzy logic system would be very hard, as the membership functions of fuzzy logic systems are based on the probabilities of fog occurrence under certain circumstances. 6.4 Cases with a fast increase of RVR Here, a fast increase of RVR is referred to as a case that has RVR values below 500m and where the next or the next but one value is greater than 1500. Creating the same plots for fast increases of RVR as for cases with a fast decrease of RVR is not so useful because when there is fog, spread is generally very low and no cloud data are available (when there is fog, clouds above the fog are usually not visible). Only the time of the day of the occurrence of the fast increases of RVR and wind data can be analyzed the same way as it was done for fast decreases of RVR. 6.4.1 Hour of the day To find out at which times these rapid RVR increases usually occur we investigated the data and counted how many fast visibility changes occurred at a certain hour. The outcome of this is shown in figure 6.24. In this figure hour “1” means that the first

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RVR value above 1500 m was reported at either 01:20 or 01:50. The numbers in the figure are the number of cases with a rapid decrease of RVR at certain times related to the total number of fast increases of visibility at the four different measuring sites.

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Figure 6.24: Hour of the day of rapid RVR increases; numbers are the number of RVR events at certain times of the day related to the total number of RVR events; Vienna 1990-2003 It can be seen that almost no rapid visibility increases happen during the late morning hours and the afternoon. Most of these increases happen during the early morning hours. 6.4.2 Wind The wind data relate to measurements taken one hour before this fast increase of RVRs. For wind speed, again the same categories as for a rapid decrease of visibility were used:

Category 1: 0-2kt Category 2: 3-6kt Category 3: 7-10kt Category 4: above 10kt.

Figures 6.18 and 6.19 show the relative frequency of the different wind speed categories for the various RVR measuring sites one hour before respectively half an hour after the fast increase of RVR.

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Wind Speed Categories

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ent < 3kt

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Figure 6.18: Frequency distribution of wind speed categories one hour before the fast increase of RVR; numbers are the frequency of events with certain wind speeds related to the total number of fast increases of RVR; Vienna 1990-2003

Wind speed categories

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Figure 6.19: Frequency distribution of wind speed categories half an hour after the fast increase of RVR; numbers are the frequency of events with certain wind speeds related to the total number of fast increases of RVR; Vienna 1990-2003

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There is a break after the second category for all RVR measuring sites: for hardly any case wind speeds exceed 6kt before a fast increase of RVR and for most cases wind speeds are below 3kt. Wind speeds greater than 10kt are not present at all. This is what we expect as during high wind speeds no fog would be present and visibilities would be better anyway. Thus, the same plot was created for the time one measurement before the fast increase of RVR. The outcome was, apart from some slight differences, the same. Then, analog plots were created for measurements that were taken half an hour after the fast increase of RVR. There, most cases have wind speeds between 3 and 6 kt and also wind speeds greater than 10kt are present. As a consequence the wind direction category “variable” is not present so often anymore. In the same way as for fast decreases of RVRs, plots of wind vectors for all RVR events with a fast increase of RVR are created. Figures 6.21 (a) – (c) show these wind vector plots for three RVR measuring sites (RVR11, RVR29 and RVR16). RVR34 is not included as the plot looks very similar to the one of RVR16. The origin of all the different vectors represents Vienna Airport. In these plots, a vector that points to the North (180°), e.g., indicates a wind direction of 180° (wind from the south).The first vector (the one that “starts” at Vienna Airport) is the measurement of wind speed and direction five measurements after the fast increase of RVR occurred. The next vector in the row is the measurement taken two hours after this change in RVR. The last vector represents wind speed and direction five measurements before the fast increase of RVR. All in all there are eleven vectors. The change of colour of the arrows indicates the change between “future” and “past”. The red arrows are the ones that represent measurements taken before the increase of RVR, the blue ones measurements taken after the increase of RVR. The first blue vector after the red ones represents the measurements that reported the first high value of RVR (see figure 6.20).

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Figure 6.20: explanation for the wind speed and direction plots in figure 6.27; the last blue arrow represents Vienna Airport; the red arrows are measurements taken before the fast RVR increase, the blue ones after the increase In the plots, variable wind speeds are not included (code -99 in the METAR code form: wind speed is in that case below 3 kt; see also Annex C), instead values of wind speed are set to zero.

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a)

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Figures 6.21 (a-c): Wind speed and direction plots in the following order: RVR11, RVR29, RVR16; RVR34 is not included as it is very similar to the plot of RVR16; for further explanation see also Figure 6.27 and text; for view reasons events with very strong winds cannot be displayed in these plots totally At RVR11 changes of wind speed before a fast increase of RVR can be seen for the events with “blue winds” from the North. One event with southerly winds shows a clear increase of wind speed after the increase of RVR. These events are marked with green circles. For many other events no change of wind speed or direction can be found. Also at measuring sites RVR29 and RVR16 some changes of wind direction or speed before a fast increase of RVR can be seen. It is interesting that westerly and south-westerly wind directions are not present.

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7. Case studies 7.1 Introduction For some cases with a fast decrease and increase in RVR, a detailed investigation is made. For this purpose I use high-frequency observations, namely one-minute observations. These observations include much more information than is currently used in meteorological routine. Also Leyton and Fritsch (2004 – see also chapter five) use high frequency surface weather observations although they utilize in their study only five-minutes observations. In the following detailed investigation several different plots are discussed. In the first step I plot the development of RVR with time. In the same plot I include computed trend-lines but also values of temperature and dew point. The trends are plotted in half-hourly intervals. They include the period one hour before and one hour after such an half hourly interval, meaning that the trends are computed for a two-hour period and projected onto the central value (this is the value that lies in the centre of the two-hour time-span). These trend lines give an indication about the predictability of RVR by the use of RVR trends. Next, I create plots that include the development of RVR, temperature, dew point, wind speed and direction and vertical visibility with time. In these plots, also wind speed and direction measured at the Arsenal are included. For view reasons only values in ten-minute time steps can be used for the wind speed and direction plots and for some cases not the same time interval is used as for the one-minute-temperature plots. Wind speed and direction are represented in these plots by vectors (giving wind components u and v). In these plots the wind axes are red and on the right hand side, temperature magenta (also right hand side), RVR black (left hand side) and vertical visibility magenta (left hand side). Vertical visibility of 10000 ft means that vertical visibility was 10000 ft or greater. Moreover, I create plots that include the RVR-values of the half-hourly central values and also an average over the two-hour periods. For this purpose I use a simple arithmetic mean. This is to find out to which degree the central value corresponds with an average over time. For the same half-hourly intervals I plot the mean over the two-hour period and the associated standard deviation to find out how the variability of RVR changes with changes in RVR. Also, I compute and then plot two different means in five-minute time steps. The first mean includes the RVR of the current observation and the two RVR values before this observation. The second mean includes the RVR values ten, eleven and twelve minutes before the observation. This mean is currently used for the tendency in the METAR-code form (the letters N, D and U – see also Annex, where the METAR code is discussed in detail). These two means are then compared against each other to find out how representative and useful these tendencies in the METAR code really are. For view reasons most of these plots are not included in this chapter. These plots can be found in the appendix G. As short wave radiation coming from the sun is an important factor in the fog dissipating process, I include the time of the sunrise and sunset for each case. This time can be found under the temperature, dew point temperature and wind speed plots.

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7.2 Cases 7.2.1 20011003: radiation fog Before looking at the details of this event it is important to note that for this event a different RVR measuring site is used for RVR34. The data of measuring site RVR16/34 C is omitted because this instrument provides values of exactly 2000m for the whole period. Maybe a measuring error occurred. If the data are correct then the question is raised why visibility at this site was not reduced although this is the site that often has lowest visibilities. However, for detailed analyses a RVR event of values that are all equal over the whole time period is not interesting and therefore for this RVR event analyses RVR16/34 C is substituted by RVR16/34 B measurements. This measuring instrument is situated halfway down RW 16/34. This event is a good example for a radiation fog event. Before the formation of fog the sky is clear. No cloud deck is hemming the long wave radiation into space. Figure 7.1 shows the development of RVR, temperature and dew point temperature with time, using one minute observations. The red lines are the trend for a two hour period. The plots included in this figure also show the development of RVR (RVR11), temperature, dew point temperature vertical visibility (to detect an eventual cloud cover) and wind speed at Vienna Airport and the Arsenal Tower with time, but with ten-minute time steps. So the difference of using data with different resolution can be seen in the plots.

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Figure 7.1: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c) - e): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29, RVR16, RVR34

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f): ten-minute observations of RVR34/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport Sunrise: 04:25; Sunset: 17:01 (UTC) For this RVR event there is one minor decrease of RVR before the main decrease of visual range. This decrease happens only at RVR11 between 02:00 and 03:00 on October 3rd, 2001. During this short lasting reduction of RVR, the spread is about 1°C. The main reduction of visibility occurs between 03:00 and 06:00. This decrease of RVR goes along with a fast drop of temperature from 14°C to 12°C in less than one hour. Also, spread is reduced to almost zero degrees Celsius. The decrease of RVR happens at about the same time as sunrise. Firstly, visibility is reduced to just above 500m on RVR16, but only for a relatively short time. Then all the other measuring sites record a reduction of RVR with lowest values at RVR11 (RVR below 500m). The decrease of RVR at all measuring sites stands in close connection with low wind speeds and a change of wind direction from SE to NE. Before and after the RVR event, wind speeds exceed 6 kt. Low wind speeds are present earliest at RVR11 and RVR29, whereas at RVR16 and RVR34 wind speeds are not reduced that much. The increase of RVR goes along with another change in wind direction: from NE to SE. At the Arsenal Tower, which is situated in the Inner City of Vienna, this change happens earlier than at the wind measuring sites at the Airport. When looking at the temperature curve it is surprising that temperature does not fall between 01:00 and 03:00. On the contrary, just before 03:00 temperature does even increase a little bit. Then, suddenly it drops about two degrees Celsius, as mentioned above. When looking at the trend lines for this plot it can be seen that the trend lines do not follow the real RVR plot very well. Also, mean and central value do not fit that well. The plot of the two different means and the difference between them reveals that the difference is greatest, where there is a change in RVR. 7.2.2 20011008: radiation fog This RVR event is also an example for a radiation type event. Before the event the sky is clear, at some stage few clouds are present. This RVR event happens between 21:00 on October 8th, 2001 and 24:00. Again, the following plots (figure 7.2) show the development of the different meteorological parameters with time.

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Figure 7.2: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower d) – f): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29, RVR16, RVR34 Sunrise: 04:32; Sunset: 16:51 (UTC) The first minor decrease of RVR happens at RVR11 at about 20:00. At this time no other measuring instrument reports a reduction in visibility. Just before 21:00 also RVR29 and RVR16 measure a decrease of runway visual range. These decreases are only short lasting and values are not as low as at RVR11. At RVR34 the first signal in RVR occurs after 21:00. It is followed by a short increase of RVR. The final decrease is interrupted by a very short increase of RVR. Values of RVR below 500m are present until 24:00. Then a fast increase of RVR happens, which is followed again by two minor decreases. After 02:00 in the morning RVR stays constantly high. The increase of RVR occurs at almost the same time at all measuring sites but the decreases of RVR after 00:00 only happen at RVR34. At RVR11 the fast decrease happens already before 21:00. At this measuring site low values of RVR are present longest. At RVR16 and RVR34 the fast decrease is not as well marked as at RVR11 or RVR29. Especially at RVR16 the abrupt fall of RVR is disturbed by several irregular increases and decreases of RVR. During the whole RVR event, temperature and dew point stay above twelve degrees Celsius. When the first minor decrease happens at RVR11 temperature is about 16°C and dew point about 15°C. Then both decrease slowly. This decrease is

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interrupted by a fast drop of temperature and dew point temperature at just before 21:00 and some small increases and drops of temperature and dew point. Also during the RVR event temperature and dew point temperature keep decreasing. Just before 21:00 the spread goes down to zero. It increases a little bit afterwards but it remains low for the rest of the RVR event. Before the event, winds come from the SW to SE. Then a change in wind direction to NW and then N in combination with lower wind speeds happens, leading to the decrease of RVR. During the event, the N-winds stay low. With the increase of wind speed and a change of direction to North-Westerly winds, RVR increases. Only at RVR34 the difference in wind speeds before, during and after the event is not as clear as at the other measuring sites. At the Arsenal Tower the changes in wind direction and speed happen earlier than at the airport. The red trend lines follow the real observations well. Mean and central values correspond well. Standard deviation is, of course, highest where there is a high variability of RVR. The difference between the two means is lowest where RVR shows low variability. 7.2.3 20020205: SE winds This RVR event occurs between 02:00 and 03:00 at the beginning of February and lasts for several hours. It shows a rapid decrease of RVR at all RVR measuring sites at the same time. However, especially at the two sites RVR16 and RVR34 the RVR fluctuates strongly before the final decrease of RVR beyond the 500 metres mark at 02:00. This again shows what a local phenomenon fog is; the RVR may be reduced drastically at one place whereas some hundred metres away RVR is still greater than 2000 metres. Before the decrease of RVR the sky is clear, which enables the surface and thus also the near surface layers to cool down. Also wind plays an important role in this RVR event: When looking at figure 7.3 some interesting detail, which would be lost at a higher time-resolution, becomes visible. During the afternoon of February, 4th 2002 the spread is relatively high. Although the spread decreases in the evening it is not before 02:00 that RVR falls rapidly and stays below 500 metres for several hours. Before this drastic reduction of RVR some minor and short-lasting decreases of RVR occur at the measuring sites RVR11 and RVR29. On RVR16 and RVR34 these” before-midnight decreases” of RVR are already longer-lasting but before 00:00 RVR improves again to 2000 m and is not reduced until the final decrease at about 02:00. This fast reduction of RVR goes along with a drop of temperature to about 0°C. Before this fast decrease of RVR, temperature and dew point and also the difference between them varies strongly with time. After this rapid decrease of RVR the variability of RVR, temperature and dew point lessens and the spread remains constantly at 0°C. On February, 5th 2002 at 12:00 temperature raises above zero degrees and with it also RVR and its variability increase.

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Figure 7.3: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c)-e): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29, RVR16, RVR34 f): ten-minute observations of RVR34/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport g): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower Sunrise: 05:44; Sunset: 16:33 (UTC) Another interesting detail is that wind speeds are low (4 kt and lower) blowing from the N but the fast decrease of RVR goes along with an increase of wind speed to 6 kt and more and a change of wind direction to SE. At measuring site RVR34 the earlier

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change of wind direction from N to SE leads to the first decrease of RVR. With another change of direction back to N, RVR increases but only to decrease again with the next wind direction change to SE. Wind speed stays above 4 kt for most of the time during the RVR event. During this period the wind blows from the South-East, advecting moist air from the Lake-Neusiedl region. The two-hour trends fit the real RVR data well, especially where the variability of RVR is low of course. Also the two-hour mean and the central value, which is the value of RVR at a certain time, fit best where there is a low variability of RVR. Standard deviation is greatest where there is a high variability of RVR. The difference between the two means is greatest where there is a change of RVR. 7.2.4 20020317: strong SE wind During the night from March 16th, 2002 to March 17th, 2002 a RVR event occurs which stands in close connection with strong winds. At the beginning of this RVR event the first reduction of visibility happens at 21:00 at RVR16 but then nothing else happens for some more hours. Around 00:00 several short lasting decreases of RVR occur at RVR11. At the other measuring sites the borders between these different decreases and increases vanish and there is one marked, longer lasting RVR event during this time. A period of good visibilities follows before fog forms again and reduces RVR for several hours at all measuring sites. This happens at around 02:00. Low visibilities last until 06:00. At this time an abrupt increase of RVR occurs. At the beginning of the night temperature has values of about five degrees Celsius; dew point temperature is about four degrees Celsius. As the night evolves, temperature and dew point decrease slowly until 23:00. Then, a sudden reduction of temperature and dew point temperature follows, with temperature reaching almost zero degrees and spread being reduced to almost zero. At this time the first longer lasting decrease of RVR happens. Just after 00:00 temperature and dew point temperature start to increase again until they reach about five degrees Celsius at two a.m. Then, variability of temperature, dew point temperature and RVR, is very low.

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Figure 7.4: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c)-e): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29, RVR16, RVR34 f): ten-minute observations of RVR34/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport g): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower Sunrise: 04:34; Sunset: 17:34 (UTC) During this RVR event wind plays an important role. Before the decrease of RVR, wind direction varies more than during and after the event. Wind speeds are low before the event, values stay mostly below five knots. Then, at about 02:00, wind speeds increase to about five to ten knots. RVR decreases at the same time. The increase in wind speeds goes along with less variation in wind direction – wind speed

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only varies between 120° to 150°. Shortly after 06:00 wind speed increases to above ten knots. Before this change in wind speed, RVR already starts to increase. But at the Arsenal Tower wind speeds increase before visibility gets better at the Airport. In this RVR event the red trend lines also follow the real observations of RVR well. During the first reduction of RVR mean and central value differ much, whilst during the second decrease of RVR, which is longer lasting, the two-hours mean and the central value are almost the same. Standard deviation is higher during the first decrease of RVR than during the second one. The difference of the two means is again lowest where RVR does not change. During the phases of increase and decrease of RVR, the two means differ much more (the difference can be more than 1500m). 7.2.5 20000207: strong S- and SW-winds, also during RVR event (atypical) Before having a closer look at the plots of this RVR event it is necessary to note that during this RVR event problems with the dew point sensor heating occurred and that therefore the incorrect data was substituted by calculated dew point temperatures. Dew point was calculated through relative humidity and therefore at relative humidity of 100% calculated dew point temperature and actual temperature are equal. As a consequence the blue line, which represents dew point temperature, cannot be seen in the plots where there is a relative humidity of 100%. Moreover, no values of RVR are available for measuring site RVR11/29C, which is named in this thesis RVR29. Therefore, this measuring site was substituted for this RVR event by site RVR11/29B, which is situated in mid-position along the runway. This RVR event happens between 03:00 and 10:00 on February 7th, 2000. Between 03:00 and 04:00 the first reductions of RVR occur at RVR16 and RVR34. The main RVR event happens shortly after 06:00 – after sunrise. At this time RVR is reduced at all measuring sites for several hours. RVR increases first at RVR11 and RVR29, RVR16 and RVR34 follow about half an hour later. During the RVR event RVR changes most at RVR11 and RVR29, whilst at RVR16 and RVR34 low values are stable for a longer time period. During the night spread is zero. Temperature decreases slowly and reaches a minimum value of about 1°C at about 07:00. Apart from RVR11 the rapid decrease of RVR coincides with this temperature minimum. The temperature starts to rise. When temperature has reached almost four degrees Celsius, RVR increases at all measuring sites.

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Figure 7.5: a)-b): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 and RVR29 c): ten-minute observations of RVR29/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport d)-e): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR16 and RVR34 f): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower Sunrise: 05:41; Sunset: 16:36 (UTC) During the RVR event wind speeds are relatively high – at RVR34, RVR16 and RVR29 occasionally even higher than before and after the RVR event. Wind speeds of more than eight knots are present during this event. Wind direction varies between 170° and 190°. In this case these high wind speeds as well as the fact that RVR starts to decrease when temperature is already increasing after sunrise, are very interesting. At the Arsenal Tower wind speeds are very low direction varies between SE, weak N-winds and stronger NW-winds at the end of the event. The trend lines follow the actual RVR data well. There is almost no difference between mean and central value at RVR11 and RVR29. At RVR16 and RVR34 the difference is a little bit greater. Compared with other RVR events standard deviation is relatively low during the currently discussed event, especially at RVR16 and RVR34. At these two measuring sites the difference between the two means is also low during the RVR event. Only where there are changes of RVR, the difference is greater.

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7.2.6 20000111: relatively strong NW winds before RVR event Before looking at this RVR event in detail it is necessary to note that no data are available on January 11th, 2000 between 10:32 and 14:43. This is why the plots end at 10:00. Moreover, values of dew point temperature are not correct during the event when relative humidity is high (because of heating problems of the instrument). That is why dew point temperature was computed through the actual temperature and relative humidity. As a consequence, for relative humidity values of 100% the calculated dew point temperature and the actual temperature have the same values. Thus, in the plots the dew point line (blue) vanishes because the temperature line (magenta) is plotted exactly over the yellow line. This event starts shortly before sunrise at RVR34 but at all other measuring sites RVR is reduced after sunrise. During this RVR event, RVR does not decrease below 1800m at RVR11 and below 1600m at RVR29 whilst at RVR16 and RVR34 runway visual range is reduced to less than 500m. RVR is first reduced at 05:00 at RVR34. At the other measuring sites the decrease of RVR starts later – at about 06:00. At RVR16 and RVR34 there are several fast decreases and fast increases of RVR. The RVR events only last about half an hour. From 00:00 on, temperature are below zero degrees Celsius. During the night spread is about two degrees but between 04:00 and 05:00 it suddenly decreases to zero. At this time the first reductions of visibility occur. After that temperature increases by about one degree. Then, at 06:00 temperature suddenly falls from -2°C to about -7°C within one hour. After 07:00 temperature starts to increase. This increase does not happen constantly – the temperature curve has two peaks. Especially the second peak, that happens at about 08:00, goes along with high visibilities.

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Figure 7.6: a)-c): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11, RVR29 and RVR16 d): ten-minute observations of RVR16/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport e): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR34 f): ten-minute observations of RVR34/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport g): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower Sunrise: 06:07; Sunset: 15:58 (UTC) Before the RVR event wind speeds are between six and eight knots. Wind comes from the NW to N. These wind directions usually do not produce fog at Vienna Airport (see chapter 4.3). As soon as wind direction changes and wind speed lessens, fog forms. At the Arsenal winds come from the NW the whole time, also during the RVR event. At the airport, during the RVR event, wind speeds are low – values are below four knots - and wind direction changes to SE but is occasionally interrupted by weak northerly winds. Wind speeds become low earliest at RVR34 – at about 04:00. During this RVR event the trend lines differ from the RVR data strongly. This is because this RVR event consists of several short lasting increases and decreases of RVR. Also, the mean differs much more from the central value than during longer lasting RVR events. Again, standard deviation is high where there are changes in RVR. The difference between the two means is only zero before and after the RVR event. During the event itself, the difference varies strongly. 7.2.7 20010209: fast increase of RVR after sunrise (due to short wave radiation) Before this RVR event there are few clouds present at 14000ft. The RVR event lasts from about 03:00 to 08:00 on February 9th, 2001. The first reduction of visibility happens at RVR11 shortly before 03:00. It is only short lasting and visibility is not reduced severely. Another two minor reductions of RVR follow before the final decrease of RVR below 500m at about 04:00. Shortly before 06:00 visibility increases to 2000m, but only to be reduced again to values below 500m several minutes

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afterwards. At the other three measuring sites the first visibility reductions are reported later: at about 04:00 at RVR29 and RVR16 and between 06:00 and 07:00 (after sunrise) at RVR34. At RVR16 and RVR29 several increases and decreases of RVR follow before the main RVR event occurs at about 06:00. At RVR34 there is no such variability of RVR before the main decrease of RVR. What all measuring sites have in common is that the last increase of RVR happens at the same time (at about 08:00). During the night temperature decreases slowly, after 01:00 faster, until it reaches zero degrees Celsius at about 03:00. At this time also the first reductions of visibility occur. Also spread decreases during the night from about five degrees at 22:00 to less than 1°C at 03:00. At about 07:00 temperature starts to rise and soon afterwards also spread becomes higher.

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Figure 7.7: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c)-e): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29, RVR16 and RVR34 f): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower Sunrise: 05:38; Sunset: 16:39 (UTC) Wind speeds are about five knots or lower, blowing from the North during the RVR event. Before the event wind speeds are a little bit higher with the same direction and at the end of the RVR event wind speed increases with a change of direction to SE. At the Arsenal Tower winds blow from the South before the event, change to W and then to NW during the event. At the end of the event wind direction changes to SW.

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The trend lines represent the actual RVR well where variability of RVR is low. Especially during the minor decreases of RVR, the trend lines show a negative trend at all times although there are several increases of RVR at this time. At RVR29 and RVR16 mean and central value differ most during the time before the main decrease of RVR. At RVR11 these two values coincide better. Standard deviation is again high where variability of RVR is high. The difference between the two means is low where RVR is constant over some time. This happens before the event and during the event itself at RVR11, RVR29 and RVR16. Where there are fast changes in RVR, the difference between the two means is high. 7.2.8 20021101: fast increase of RVR after sunrise (due to short wave radiation) This RVR event occurs also after sunrise - between 05:00 and 07:00 on November 2nd, 2002. The decrease and increase of RVR happen at the same time at all four measuring sites. After the RVR event some minor decreases of RVR occur at all measuring sites until 12:00. It is very interesting to look at the temperature evolution during this night. At 19:00 on November 1st, 2002 temperature is about 6°C. Then temperature increases to about ten degrees. After 24:00, temperature decreases again. Spread also decreases until it is reduced to zero degrees at about 02:00. Then the variability of temperature and dew point temperature weakens. Shortly after 06:00, temperature starts to rise.

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Figure 7.8: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29 d): ten-minute observations of RVR29/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport e)-f): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR16 and RVR34 g): ten-minute observations of vertical visibility/ft at Vienna Airport and wind speed/kt and wind direction at Arsenal Tower Sunrise: 05:07; Sunset: 16:09 (UTC) Wind speeds during the RVR event are lower than before and after the event. At 03:00 wind speeds reach values of more than eight knots, blowing from S to SE. During the event wind speeds are much lower with Northerly directions. At RVR 29 this N wind changes to SE for some 20 minutes, leading to a little increase in RVR. At the end of the RVR event, wind speed increases again with SE-direction. At the Arsenal Tower, wind directions are more SW than SE, lower during the event and turning to stronger SW at the end of the event. The trend lines represent the actual RVR values very well. At RVR16 and RVR29 there is not much difference between mean and central value. But also at RVR11 and RVR34 the difference is not very great. During the changes of RVR and during the RVR the difference between the two means is highest. Where RVR remains constant, the difference is zero. 7.2.9 20000103: unpredictable RVR development During this RVR event also problems with the heating occurred. Therefore, dew point temperature was again calculated through relative humidity and temperature. As a consequence, the blue dew point temperature line vanishes where relative humidity is 100%. Moreover, no data are available for the Arsenal Tower for this event. This RVR event is a very long lasting RVR event. It lasts throughout the daytime of January 3rd, 2000. During the event there is a high variability of RVR, especially during daytime. Apart from RVR29 there are two main decreases of RVR, the first

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lasts until about 09:00, the second starts at about 11:00 and lasts until 20:00. Especially at RVR34 these two decreases are strongly marked with a very low variability of RVR. At RVR11 the second reduction of RVR is shorter than at the other measuring sites. The highest variability of RVR is present at RVR29, when there is already the first longer lasting decrease of RVR at all other measuring site. Throughout the whole RVR event, spread is lower than 1°C. Only at around sunrise it increases a little bit but during the daytime it already starts decreasing again. Spread is reduced to zero degrees (relative humidity of 100%) before 12:00. Temperature has negative values during the whole RVR event. At the beginning of the RVR event, at about 01:30, the temperature curve shows a sudden fall from -3°C to -6°C. During the rest of the night, temperature decreases relatively constantly at reaches its minimum value of about -11°C just after sunrise. Then temperature starts to rise again. This constant increase is interrupted by a decrease of about 1°C at 09:00. Shortly after 12:00, temperature decreases again. At about 15:00, temperature starts to increase again. This is the time when the second main RVR reduction happens at RVR11. At 06:00 temperature starts to decrease again until 22:00. At this time a sudden temperature increase from -8°C to -1°C occurs. This increase goes along with an increase of RVR.

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Figure 7.9: a): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR11 b): ten-minute observations of RVR11/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport c)-d): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR29 and RVR16

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e): ten-minute observations of RVR16/m (black), temperature/°C (magenta), dew point temperature/°C (yellow) and wind speed and direction at Vienna Airport f): One minute observations of RVR/m, temperature/°C and dew point temperature/°C RVR34 Sunrise: 06:09; Sunset: 15:49 (UTC) Except for the first two hours after 00:00, there is no wind at RVR11 until 12:00. Then wind speed increases to values of more than five knots. At RVR29 higher wind speeds of ten knots are already present at the beginning of the RVR event. During the day, wind speeds are a little bit lower, which allows the formation of a denser and steadier fog. With another increase of wind speeds to more than ten knots in the first half of the following night, RVR increases, too. At RVR16 and RVR34 the evolution of wind speed with time is similar to that at RVR29. The main difference is that values of wind speed are lower at RVR16 and RVR34 during the first longer lasting reduction of RVR. From the beginning of the RVR event until the evening, winds come from the SW to SE, occasionally interrupted by weak N-winds. At 20:00, wind direction changes to the Northwest. Summarizing this case it can be said that this example shows one case with RVR developments that are impossible to predict. The trend lines fit the real RVR data very well at RVR34. At RVR29, where many changes of RVR occur at the beginning of the RVR event, this fitting is not as good as at the other measuring sites. Apart from RVR29, mean and central value correspond well during this RVR event. Especially at RVR34 the correspondence of these two values is great. Standard deviation is very low at RVR34 during the two long lasting RVR events. At the other measuring sites it is also low during the second period of low RVR but higher in the time before. Especially at RVR29 standard deviation has high values until the main decrease of RVR in the afternoon. The difference between the two means is lowest at RVR34 during the periods of low RVR. But also at the other measuring sites this difference is low during the second RVR event. When there are changes of RVR, which happens often at RVR29, this difference is much higher. 7.3 Time Lag Auto Correlations The next step is to compute the correlation coefficient between two time series. The first time series consists of the difference of the two means as described in the introduction. Then this difference is multiplied with three and added to the current value of RVR. Thus, a “forecast for RVR”, which is based on the change of RVR in the past, is created for a moment 30 minutes ahead. The second time series contains the measured values of RVR for this moment 30 minutes ahead. When correlating these two time series it is possible to find out to which degree a change of RVR in the past can help to forecast future RVR developments. This idea forms also the basics of Markov chains. In figure 7.10 the outcome of the computation of the correlation coefficients can be seen.

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Figure 7.10: Correlation coefficient between two time series: series one contains a “forecast for RVR”, which is based on the change of RVR in the past; the “forecast” is created for a moment 30 minutes ahead; the second time series consist of the measured RVR values for the moment 30 minutes ahead The figure above shows that the correlation coefficients vary strongly for the different RVR events. Even negative correlation coefficients are present. These correlations happen on January 11th, 2000 (RVR16 and RVR34) and on October 3rd, 2001 (RVR16). Also for the other measuring sites correlation coefficients are low for these two events. Highest values of correlation coefficients are present on November 1st, 2002 (RVR29 and RVR16). Values are higher than 0.80 for this event. Generally, the values differ much between the different events but they are relatively similar for the different RVR measuring sites for one event. Most correlation coefficients (52.8%) lie between values of 0.4 and 0.7, as can be seen in figure 7.11. More than half of the coefficients are below 0.6. This outcome shows that this method would not be very reliable if it was used for the prediction of RVR. Too many RVR events have too low values of correlation coefficients.

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8. Conclusions Forecasting fast decreases of RVR is a complex task. Many different processes can lead to the formation of dense fog. Often, a combination of processes is responsible for a fast decrease of RVR. Thus, looking only at one specific meteorological parameter is not a satisfying solution in the fog forecasting process. When forecasting fast decreases of RVR it is necessary to find out which one is the dominating factor in the current fog formation process and thus put the current case in the category where it fits best: Is it a radiation type fog? Are there any advective processes involved? Will clouds play a role in the current case (possible increase of RVR)? Will a warm front pass the site? If it is known which fog type will be expected, it will be necessary to predict the different meteorological parameters that will influence the current case. When the fog goes along with a front then a forecast of the time of the passage of the front has to be made. This can be done by the use of numerical models but also satellite images or radar can help to predict the timing of the front passage. If the fog will be of advection type it will be most important to get information about wind speed and direction not only at the site (airport) itself but also at the measuring sites in the vicinity. Mesoscale measuring networks and numerical models are a good tool to predict the probable development of wind speed and direction and thus the probable development of RVR. Clouds, especially when they are low, can also have an influence on the fog formation process. As discussed in chapter 3, they can inhibit the formation of fog or can lead to fog dissipation. Therefore it is necessary to create a cloud forecast in order to gain knowledge about probable RVR improvements and their timing. Especially for short term forecasts of RVR, satellite images can be of great help when forecasting the development of cloud cover for the next few hours. When a clear night is expected, a radiation type fog is very probable in cold season. Most fast decreases of RVR go along with clear sky or only few clouds. To forecast the timing of the formation of fog it is most important to gain information about the probable development of temperature and dew point. As soon as spread is minimized to almost zero, the situation gets critical. But a low spread is no guarantee for the formation of fog. Additionally, wind speeds should not be too high for the formation of fog (only if advective fog is expected, which goes along with SE winds at Vienna Airport). To forecast the development of temperature in the following night, it can be helpful to use the development of temperature in past cases. Currently, some research is done on this topic at Innsbruck Airport by Alexander Niederl for Innsbruck. For different cloud-categories, the trends of temperature developments for different times of the day and different months were calculated. With the help of these trends future temperature values can be estimated. The following figure shows an example of the first outcome of this study:

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Although the theories for forecasting fog events sound relatively easy, the praxis of predicting fog is a big challenge. In combination with low wind speeds, temperatures around zero degrees Celsius and wet soil, fog is very likely. However, these ideal weather conditions do not occur frequently. Even if they are present, low visibilities do not have to be the consequence. Thus, “overforecasting” will happen and consequently, the false alarm rate for these “ideal” weather conditions will be high. This also shows the limits of the use of numerical models in the fog forecasting process. Even if these models predicted meteorological parameters perfectly, predicting fog would still be an intimidating task. This study was undertaken to find meteorological parameters and thus a reliable method to forecast fast RVR changes. The outcome of the study seems to be discouraging. Instead of improving the process of forecasting RVR changes, it only shows again the difficulties that arise when it comes to forecasting fog occurrence. However, there is still some information about fog occurrence that can be gained by the studies undertaken: Before a fast decrease of RVR often a relatively fast drop of temperature and dew point happens. This is a sign for the presence of a stable boundary layer. Additionally, spread decreases. As long as the difference between temperature and dew point temperature is more than 2°C, a fast decrease of RVR in the next hour is very unlikely. When the fluctuations of temperature and dew point lessen, the situation becomes critical for the formation of fog. Often, tendencies of parameters are more important for forecasts than absolute values of various parameters, such as temperature.

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Sunrise seems to be a very critical phase. Often, fog forms at the time around sunrise. For the prediction of changes in wind speed and wind direction, the wind data from the Arsenal may be helpful. Often, such a change can be registered by these measuring instruments earlier as the instruments are situated at a height of ten metres above the ground. To produce more reliable forecasts of RVR events it will also be necessary to install additional temperature and dew point measuring instruments. Especially at larger airports, such as Vienna Airport, measuring temperature and dew point temperature at one site only seems not to be satisfying. Often, fast decreases of RVR cannot be explained with the development of temperature and dew point temperature if these parameters are measured too far away from where the fog has formed. The dangers and economic impacts involved with low RVR ask for further improvements in the fog forecasting process. This study underlines why this improvement is such great challenge. Fog is a very complex phenomenon and this thesis shows why it is so difficult to create reliable forecasts for low visibilities. Still no reliable method is available when it comes to the prediction of RVR events and thus forecasting RVRs remains a daunting task.

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Appendix A) Glossary of acronyms

AAR Airport Acceptance Rate

AIP Aeronautical Information Publication

ASOS Automated Surface Observing System

ATC Air Traffic Control

AVN Aviation Model

CBR Case Based Reasoning

CSI Critical Success Index

DME Distance Measuring Equipment

FAR False Alarm Ratio

FIR Flight Information Region

FL Flight Level

FOH Frequency Of Hits

GDP Ground Delay Program

GP Glide Path

ICAO International Civil Aviation Organization

IFR Instrument Flight Rules

ILS Instrument Landing System

IMC Instrument Meteorological Conditions

k-nn K Nearest Neighbours

LAMP Local AWIPS MOS Program

LCL Lifting Condensation Level

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LLZ Localizer

LOWW ICAO Location Indicator (last W for Vienna)

LVP Low Visibility Procedures

LWC Liquid Water Content

METAR Meteorological Routine Report

MOS Model Output Statistics

MSE Mean Square Error

NGM Nested Grid Model

NWP Numerical Weather Prediction

NWS National Weather Service

OBS Observations

PC Persistence Climatology

POD Probability of detection

RMSE Root Mean Squared Error

RUC Rapid Update Cycle

RVR Runway Visual Range

TAF Terminal Aerodrome Forecast

TWR Tower

UTC Co-ordinated Universal Time

VDF VHF Direction Finder

VFR Visual Flight Rules

VMC Visual Meteorological Flight Conditions

WIND-1 Weather Is Not Discrete – Version 1

WMO World Meteorological Organization

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B) Scores (Peer, 2003) For a better understanding of the descriptions of Skill scores in this part of the appendix, the following table might be helpful: Table B1: Three sorts of events BIAS: The BIAS indicates the average direction of the deviation from observed values, but may not reflect the magnitude of the error. A positive BIAS indicates that the forecast value exceeds the observed value on the average, while a negative BIAS corresponds to under-forecasting the observed value on the average. The BIAS should be expressed as the mean forecast value minus the mean observed value for the verification sample. RMSE: root mean square error This is a quadratic scoring rule which gives the average magnitude of errors, weighted according to the square of the error. It does not indicate the direction of the deviation. RMSE gives greater weight to large errors than to small errors in the average. FAR: false alarm ratio It is the number of forecast events which have not taken place. The ideal situation is the number of zero; the worst is the number of one. FAR falls into the category of verification measures that imply stratification by forecast and therefore as the name implies is sensitive only to false predictions of the event, not to missed events. The term False Alarm Ratio is usually used when referring to a contingency table for weather forecasts. The score can always be increased by under-forecasting the number of events but only at the cost of more missed events. POD: probability of detection It is the number of correct divided by the number of observed in each category. It is a measure of ability to correctly forecast a certain category and is sometimes referred to as “hit rate”, especially when applied to severe weather verification. Ideal situation is the value one. Like FAR, it is not a complete score. It is in the class of verification measures that imply stratification by observation and thus is sensitive only to missed events, not false alarms. Thus POD can be increased by a larger number of forecasts on the assumption that a greater number will then be correct usually at the cost of more false alarms.

observed

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CSI: critical success index This is a function of false alarm ratio and probability of detection. For this reason, CSI provides no unique verification information. Nevertheless, understanding its behaviour can help identify which component (FAR or POD) would be more beneficial to target in a warning strategy. When POD = 1 - FAR equal changes in FAR and POD produce an equal change in CSI. When POD is greater than 1 - FAR, CSI is more sensitive to changes in FAR, and when POD is less than 1 - FAR, CSI is more sensitive to changes in POD. FOH: frequency of hits (Reliability) This score gives the number of right forecasts relative to all the forecasts of the event. C) The METAR Code (Peer, 2003) As most of the data used in this work are derived from METAR code forms it is useful to give some information about the METAR code itself. The Meteorological Aerodrome Routine Report is an important aviation weather report. This aviation weather observation is carried out at half-hourly intervals (full hour + 20 and full hour + 50 minutes) at Vienna Airport. Smaller airports and special weather stations report only hourly, during daytime and sometimes only the most important elements such as wind, visibility and clouds. The METAR Code includes information of the last 10 minutes before the observation. Information is valid for the given time, the observation location and an area of 10 km around it. Schematic structure (order) of the METAR Code and description of its elements: Order: Code name / Location / Observation time / AUTO / Surface wind / Surface visibility / Runway visibility / Present weather conditions / Clouds / CAVOK / Temperature / Dew point / Pressure / Recent weather conditions / Wind shear / Remarks / TREND / Runway state = “=” symbolizes the end of the report. Weather elements that are not measured or reported do not appear in the code. Code name and location: Airports are identified by a four-position location indicator. Smaller observation stations are characterized by a five-position number. Examples: LOWW: Vienna; 11146: Patscherkofel Observation Time:

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This METAR-code element gives information about the date as well as the time of the observation. Example: LOWI 201050Z: observation at Innsbruck Airport on 20th of the current months at 10.50 o’clock UTC – Z stands for ‘Zero’ (zero-meridian). AUTO: Between 23.50 and 03.50 international airports create METAR codes automatically, including the following weather parameters: wind, visibility up to 3000 metres, runway visual range (RVR), weather conditions, temperature, dew point, QNH and clouds (only cloud height is measured with ceilometers without information about cloud amount). Surface wind: Wind direction and speed are an average of the last 2 minutes before the observation time. Wind direction is given in degrees at ten degree-intervals. Special information about wind speed or direction is added in the following groups: ‘G’: gusts – is included in the METAR code if the maximum values of wind speed are at least 10kt above the averaged wind speed of the last ten minutes. ‘VRB’: variable – is included if wind direction of the last ten minutes changed 60 degrees or more and wind speed is not higher than 3kt. ‘dddVddd’: variability of wind direction at higher wind speeds. ‘00000kt’: meaning that there is no wind at all. Examples: 17015G25kt 090V240: average wind direction of 170 degrees at an average speed of 15 kt; gusts with maximum values of 25 kt; wind direction is changing between 090 and 240 degrees. Surface visibility: Visibility is the farthest distance at which an observer can distinguish known objects. So visibility is measured by reference to objects with a known distance to the point of observation. At international airports prevailing visibility is reported, at smaller stations the lowest visibility. It there is a great difference between visibilities for different directions a second visibility group is added to the METAR code. Each visibility group is mentioned with its belonging direction. Examples: 1200SE 8000W: visibility of 1200 metres in direction south-east and a much lower visibility of only 8000m in direction west. 9999: visibility 10 km or more 99km: visibility 100 km or more Runway visual range (RVR): International Airports report RVR if visibility is below 1000m. RVR is defined by the maximum distance along the runway at which the runway lights are visible to a pilot at touchdown. Runway visual range may be determined by an observer located at the end of the runway, facing in the direction of landing, or by means of a

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transmissometre installed near the end of the runway. The measuring range of these instruments lies between 50 and 1500 metres. If RVR is outside of this range the following elements are used: R /M0050: RVR below 50 metres R / M1500: RVR more than 1500 metres At the empty space after the ‘R’ the runway, for which this visual range is valid, is named. Tendencies are not a forecast but a development of runway visual range during the last ten minutes before the observation time. For tendencies the following letters are used: U…Upward (increasing RVR during the last ten minutes) D…Downward (decreasing RVR the last ten minutes) N…No change Like variability in the wind group variability of RVR is named in a second group in the METAR code. Examples: R27/0400u: RVR on runway 27 400m, upward tendency R31/0700d 0400V0900: RVR on runway 31 700 metres, downward tendency; RVR is variable between 400 and 900m. Present weather conditions: This METAR code group contains all relevant weather conditions that influence air traffic. Present weather conditions means at the time of the observation at the station as well as in the vicinity (an area 10? km around the station). “VC” is used if the weather phenomenon occurs in the vicinity (meaning an area 20 km around the airport) but not at the airport itself. In the METAR code form abbreviations for the different weather conditions are used (see Examples). Intensity is described with “-“ (light), “+” (strong) and no sign (moderate). Examples: FZDZ: freezing drizzle +SHAN: strong snow showers BCFG: fog patches Clouds: The amount of clouds and the height of cloud base above aerodrome level (AAL) in hectofeet are reported. At international airports usually only convective cloud types (CB…Cumulonimbus; TCU…Towering Cumulus) are named, if necessary also other types (NS...Nimbostratus, e.g.). Normally, only three cloud groups are reported – exceptions are made: up to four cloud groups are reported when CB or TCU are present. Cloud amount: SKC………Sky clear: < 1/8 FEW………few clouds: 1/8 – 2/8 SCT……….scattered: 3/8 – 4/8 BKN……….broken: 5/8 – 7/8 OVC………overcast: 8/8

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Special forms: VV005: vertical visibility 500ft, exact cloud structure is not noticeable SCT///SC: cloud base not noticeable (because cloud base is below the station which is possible at mountain stations) BKNCI: Small stations do not report height of high clouds (Cirrus) Example: FEW070 SCT140: 1/8 to 2/8 cloud amount at 7000ft and 3/8 to 4/8 cloud amount at 14000 ft Cavok: This abbreviation is used for: Clouds and Visibility Okay, meaning surface visibility is 10 km or more, no significant weather phenomena are present, no clouds below 5000ft AAL or minimum sector altitude, no TCU and no CB. Minimum sector altitudes are different for each airport. They are maximum height of topography plus 1000 ft in an area of 25 NM around the navigation help of an airport. Example: BKN 6000ft: at LOWL (Airport Linz Hörsching) this will be reported as CAVOK (in Linz the minimum sector altitude is 5800ft AAL). At Salzburg Airport (minimum sector altitude of 8600 ft) this is not reported as CAVOK. Temperature and Dew point: Temperature and dew point are given to the nearest whole °C. For negative figures “M” (for Minus) is used in front of the number. Example: 27/08: temperature between 26.5 °C and 27.4°C and dew point between 7.5 °C and 8.4 °C QNH: QNH is the air pressure reduced to sea level (QFE is air pressures at elevation of the airport: it is important for finding out the actual height above the aerodrome) and has the dimension hectopascal. QNH is used for altimeter settings. Values are rounded down to the nearest lower whole hectopascal. Example: Q1002: Air pressure reduced to sea level (QNH) is 1002 hPa Recent weather conditions: If significant weather between the last and the actual observation occurred but is not present at the actual observation anymore then recent weather conditions are reported, using “RE”. Example: RETS: There was a thunderstorm between the last and the actual observation but it is not continuing at the time of observation. Special forms:

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If weather conditions reported in the present-weather-conditions-group and in the recent-weather-conditions-group are the same but with different intensities then this is a sign for a change in intensity between the last and the actual observation. Example: LOWL 271150 28007kt 9999 –Shra sct030 bkn 060 09/08 Q1018 RESHRA = At the last observation there was moderate or strong rain shower but now there is only light rain shower. Wind shear: This group gives warnings of observed or expected existence of wind shear for the concerned runway (e.g. RWY34) or for all runways at the airport (WS ALL RWY) as wind shear can be very dangerous for aircraft at take-off or landing. Remarks: Plain language additions are used only nationally or in MET REPORTs. They describe aviation relevant weather conditions, not contained in the preceding code such as mountains in clouds in special directions (MT NW INC) or local wind systems (FOEHNWALL). Trend: Trend is a two hour landing forecast for expected weather conditions, concerning wind direction, wind speed, visibility, weather phenomena and cloudiness. The Trend is valid for two hours, starting from the actual observation time. Example: LOWW 260720Z 14010kt…: Trend is valid until 0920 Detailed information about the Trend will not be given in this work because it is not relevant for the data analysis done in this Master Thesis. Runway state: This is the last group of the METAR code. It is reported if one ore more runways at the airport are affected by rain, snow, ice or melting water. This eight-digit-number gives details about the affected runway, type as well as horizontal and vertical extent of the deposit and friction coefficient. D) A fuzzy-logic example (Murtha, 1995) The following table shows the fuzzy sets used in this example. Dewpoint Spread Rate Wind Skydry unsaturated drying too light cloudymoderate saturated saturating excellent clearmoist very saturated too strongvery moist

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Table D1: Fuzzy sets in the radiation fog example (Murtha, 1995) The determination of the fuzzy sets is derived solely from experience. A developer of a fuzzy system must choose these categories such that reasonable system outputs will be obtained. This is the critical process of tuning the system. It is here where the experience of the system developer becomes very important. The fuzzy sets are quantitatively defined by membership functions. These functions are typically very simple functions that cover a specified domain of the value of the system input. The functions are generally trapezoids, although simpler functions such as triangles and even delta functions are often used. Membership functions for the radiation fog example are shown in the following figures.

Figure D1: Membership functions associated with the dew point are referred to as "dry", "moderate", "moist" and "very moist." (Murtha, 1995)

Figure D2: Membership functions associated with the dew point spread are referred to as "very saturated", "saturated" and "unsaturated." (Murtha, 1995)

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Figure D3: Membership functions associated with the rate of change of the dew point spread are "becoming saturated" and "drying." (Murtha, 1995)

Figure D4: Membership functions associated with the wind speed are referred to as "too light", "excellent" and "too strong." (Murtha, 1995)

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Figure D5: Membership functions associated with the sky condition are referred to as "clear" and "cloudy." (Murtha, 1995) Each value of system input will belong to at least one fuzzy set and very likely more than one fuzzy set. This is possible because during construction the neighbouring fuzzy sets are made to overlap. The determination of the system output follows from the evaluation of a set of predefined rules. The strength of a rule is derived from the corresponding degrees of membership of the system input. The higher degrees of membership result in corresponding rules which have more strength in the final evaluation process. The rule base is a set of rules in the If-Then form. For, example one rule could be: If (dry & unsaturated & drying & too light & cloudy) Then (low probability of fog) The rules must be constructed for each system and must rely on the experience of the forecaster. The total number of rules is the product of the number of fuzzy sets characterizing the system. Thus, in the current example the total number of rules is 144 (4x3x2x3x2). The system outputs are also defined by membership functions similar to the inputs. The sets for the current example are given in figure D6.

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Figure D6: Output membership functions associated with the probability of fog formation. Possible probabilities include "very low", "low", "medium", "high" and "very high." (Murtha, 1995) The output membership functions aid in determining a final value of the system output. The process is essentially the inverse of the evaluation of the degrees of membership. The following example will clarify this. For demonstration assume the following data: Dew point = 6°C Dew point spread = 1°C Rate of change of dew point spread = -1°C/h Wind speed = 7 knots Sky coverage = 2 tenths The first step is to determine the degree of membership in each of the fuzzy sets for each system input. The data of the example are already marked in figures D1 – D5 (red lines). For example this value of dew point allows it to be a 72% member of the “moist” set and a 48% member of the “moderate” set. It is a 0% member of the other sets shown. In this example only 16 rules will have strengths (meaning non-zero values). This is because of the membership assignment above: the dew point, spread, rate of change and wind each involve two fuzzy sets while the sky coverage only involves one set (2x2x2x2x1=16): 1. If (moderate & saturated & drying & excellent & clear) Then (low probability of fog) 2. If (moderate & saturated & drying & too light & clear) Then (low probability of fog) 3. If (moderate & saturated & saturating & excellent & clear) Then (high probability of fog) 4. If (moderate & saturated & saturating & too light & clear) Then (medium probability of fog) 5. If (moderate & very saturated & drying & excellent & clear) Then (medium probability of fog) 6. If (moderate & very saturated & drying & too light & clear) Then (medium probability of fog) 7. If (moderate & very saturated & saturating & excellent & clear) Then (very high probability of fog) 8. If (moderate & very saturated & saturating & too light & clear) Then (high probability of fog) 9. If (moist & saturated & drying & excellent & clear) Then (medium probability of fog) 10. If (moist & saturated & drying & too light & clear) Then (medium probability of fog)

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11. If (moist & saturated & saturating & excellent & clear) Then (very high probability of fog) 12. If (moist & saturated & saturating & too light & clear) Then (very high probability of fog) 13. If (moist & very saturated & drying & excellent & clear) Then (high probability of fog) 14. If (moist & very saturated & drying & too light & clear) Then (high probability of fog) 15. If (moist & very saturated & saturating & excellent & clear) Then (very high probability of fog) 16. If (moist & very saturated & saturating & too light & clear) Then (very high probability of fog) The next step is to determine the rule strengths. The strength of a rule is the value of its weakest or least valued antecedent. In the first rule above the values of its antecedents are, in order: 48% (moderate dew point), 66% (saturated spread), 33% (drying rate), 100% (excellent wind speed) and 100% (clear sky). The rule strength is then 33. The other rule strengths are evaluated similarly. The next step is to derive a system output, a probability of fog formation, from the rules. The value of the output is assigned to the value of the “most true”, or strongest, rule. Important in connection with the output are limiting values. How these limiting values for each output fuzzy set are used is indicated in figure D6. An individual output membership function is restricted in height by its corresponding limiting value. Then the remaining area of the membership function is calculated. The final system output is then calculated as the weighted average of the centroid of each membership function, with the area of the enclosed set as the weighting factor.

Output fuzzy set

very low low medium high very high

centroid 15 30 50 70 85

area 0 825 1536 1056 1254

Table D2: Centroids and weighting areas of output fuzzy sets (Murtha, J., 1995) The final system output is then simply calculated as: Si(centroid)i x (area)i / Si(area)i=60.3 This value is interpreted as a probability of radiation fog of 60%. E) RVR-Trend (see also chapter 6)

Date RVR29 RVR16 RVR29TREND RVR16TREND 200211132320 -99 -99 200211132350 -99 -99 200211140020 -99 -99 200211140050 1500 1500 D N 200211140120 1100 1500 U N 200211140150 650 1200 N D 200211140220 650 1100 U D

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200211140250 275 1100 N D 200211140320 350 1000 U D 200211140350 600 400 D D 200211140420 450 400 N D 200211140450 325 400 N D 200211140520 325 400 N D 200211140550 350 400 N D 200211140620 275 400 N D 200211140650 300 400 N D 200211140720 250 400 N D 200211140750 250 400 N D 200211140820 250 400 U D 200211140850 375 400 N D 200211140920 450 400 N D 200211140950 1100 400 U D 200211141020 1500 400 D D 200211141050 -99 -99

F) Cloud conditions (see also Chapter 6.3.4) For additional information see also Annex C (The METAR Code). B1-B3 (cloud cover) and H1-H3 (cloud height) stand for the three columns that are used in the METAR code form to encode cloud cover. A = B1=SKC B = B1=FEW C = B1=FEW & B2=SCT & H2<5000 & H3=0 D = B1=FEW & B2=SCT & H2>5000 & H3=0 E = B1=FEW & B2=SCT & H2<=5000 & B3=BKN & H3<=5000 F = B1=FEW & B2 =SCT & H2<=5000 & B3 =BKN & H3>5000 G = B1=FEW & B2 =SCT & H2>5000 & H2<=10000 & B3 =BKN & H3>5000 H = B1=FEW & B2 =SCT & H2>10000 & B3 =BKN & H3>=10000 I = B1=SCT & H1<=5000 & H2 =0 & H3 =0 J = B1=SCT & H1>5000 & H2 =0 & H3 =0 K = B1=SCT & H1<=5000 & B2 =BKN & H2<=5000 & H3 =0 L = B1=SCT & H1<=5000 & B2 =BKN & H2>5000 & H3 =0 M = B1=SCT & H1>5000 & H1<=10000 & B2 =BKN & H2>5000 & H3 =0 N = B1=SCT & H1>10000 & B2 =BKN & H2>10000 O = B =BKN & H1<=5000 & H2 =0 & H3 =0 P = B1=BKN & H1>5000 & H1<=10000 & H2 =0 & H3 =0 Q = B =BKN & H1>10000 & H2 =0 & H3 =0 R = B1=FEW & B2 =BKN & H2<=5000 & H3 =0 S = B1=FEW & B2 =BKN & H2>5000 & H2<=10000 & H3 =0 T = B1=FEW & B2 =BKN & H2>10000 & H3 =0 U= B1=5 V = B1 =SCT & B2 =SCT W = B1 =BKN & B2 =BKN X = B1 =FEW & B2 =BKN & B3 =BKN Y = B1 =SCT & B2 =BKN & B3 =BKN

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Kat1= E+K+O+R+W+X+Y Kat2=C+F+G+I+L+M+P+S+V Kat3=A+B+D+H+J+N+Q+T+U+Z The letters A to Y stand for the different cloud cover conditions. Kat3 includes all these combinations with little cloud cover in low levels and eventual high clouds. Kat1 includes overcast (or almost overcast) cloud cover conditions. G) Figures (see also Chapter 7) For all figures in this part of the appendix the following order is valid: Wind plots:

• RVR11: first figure; • RVR29: second figure; • RVR16: third figure; • RVR34: fourth figure; • Arsenal: fifth figure (if data available)

Plots including mean, standard deviation and central value

• RVR11: first figure; • RVR29: second figure; • RVR16: third figure; • RVR34: fourth figure;

Plots including the difference between the two means

• RVR11: left hand side, top; • RVR29: right hand side, top; • RVR16: left hand side, bottom; • RVR34: right hand side, bottom;

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tical

vis

ibili

ty/ft

Vertical visWind Arsenal

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

148

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

149

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:001800

1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

Time

RV

R/m

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00−100

−80

−60

−40

−20

0

20

40

60

80

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:001600

1650

1700

1750

1800

1850

1900

1950

2000

Time

RV

R/m

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00−250

−200

−150

−100

−50

0

50

100

150

200

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00−2000

−1500

−1000

−500

0

500

1000

1500

2000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00−2000

−1500

−1000

−500

0

500

1000

1500

2000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20000207

23:00 02:20 05:40 09:00 12:20 15:40−2

0

2

4

6

8

0

500

1000

1500

2000

RV

R/m

0

2

4

6

8

10TemperatureDew PointRVRWind

23:00 02:20 05:40 09:00 12:20 15:400

5

10

15

0

500

1000

1500

2000

RV

R/m

0

2

4

6

8

10TemperatureDew PointRVRWind

150

23:00 02:20 05:40 09:00 12:20 15:400

5

10

15

0

500

1000

1500

2000R

VR

/m

0

2

4

6

8

10TemperatureDew PointRVRWind

23:00 02:20 05:40 09:00 12:20 15:400

2

4

6

8

0

500

1000

1500

2000

RV

R/m

0

2

4

6

8

10TemperatureDew PointRVRWind

−5

0

5

10

win

d sp

eed/

knot

s

23:00 02:20 05:40 09:00 12:20 15:400

2000

4000

6000

8000

10000

Ver

tical

vis

ibili

ty/ft

Vertical visWind Arsenal

21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

151

21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

152

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1000

−800

−600

−400

−200

0

200

400

600

800

1000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1500

−1000

−500

0

500

1000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20010209

22:00 01:20 04:40 08:00 11:20−10

−5

0

5

10

15

0

500

1000

1500

2000

RV

R/m

−5

0

5

10

15TemperatureDew PointRVRWind

22:00 01:20 04:40 08:00 11:20−10

−5

0

5

10

0

500

1000

1500

2000

RV

R/m

−5

0

5

10

15TemperatureDew PointRVRWind

153

22:00 01:20 04:40 08:00 11:20−20

−10

0

10

20

0

500

1000

1500

2000R

VR

/m

−5

0

5

10

15TemperatureDew PointRVRWind

22:00 01:20 04:40 08:00 11:20−10

−5

0

5

10

500

1000

1500

2000

RV

R/m

−5

0

5

10

15TemperatureDew PointRVRWind

−5

0

5

10

15

20

win

d sp

eed/

knot

s

22:00 01:20 04:40 08:00 11:200

2000

4000

6000

8000

10000

Ver

tical

vis

ibili

ty/ft

Vertical visWind Arsenal

21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

154

21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

155

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00−2000

−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00−1500

−1000

−500

0

500

1000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00500

1000

1500

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00−1500

−1000

−500

0

500

1000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20011003

22:00 01:20 04:40 08:00−2

0

2

4

6

8

500

1000

1500

2000

RV

R/m

10

12

14

16

18TemperatureDew PointRVRWind

22:00 01:20 04:40 08:00−2

0

2

4

6

8

500

1000

1500

2000

RV

R/m

10

12

14

16

18TemperatureDew PointRVRWind

156

22:00 01:20 04:40 08:00−2

0

2

4

6

8

500

1000

1500

2000R

VR

/m

10

12

14

16

18TemperatureDew PointRVRWind

22:00 01:20 04:40 08:00−5

0

5

10

500

1000

1500

2000

RV

R/m

10

12

14

16

18TemperatureDew PointRVRWind

22:00 01:20 04:40 08:00−5

0

5

10

win

d sp

eed/

knot

s

Wind Arsenal

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

157

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

21:00 00:00 03:00 06:00 09:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

158

02:00 03:00 04:00 05:00 06:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

02:00 03:00 04:00 05:00 06:00600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00−1000

−800

−600

−400

−200

0

200

400

600

800

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

02:00 03:00 04:00 05:00 06:00500

1000

1500

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

02:00 03:00 04:00 05:00 06:00400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

02:00 03:00 04:00 05:00 06:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20011008

15:00 18:20 21:40 01:00 04:20−10

−5

0

5

10

0

500

1000

1500

2000

RV

R/m

10

15

20

25TemperatureDew PointRVRWind

15:00 18:20 21:40 01:00 04:20−10

−5

0

5

10

0

500

1000

1500

2000

RV

R/m

10

15

20

25TemperatureDew PointRVRWind

159

15:00 18:20 21:40 01:00 04:20−10

−5

0

5

10

0

500

1000

1500

2000R

VR

/m

10

15

20

25TemperatureDew PointRVRWind

15:00 18:20 21:40 01:00 04:20−10

−5

0

5

10

0

500

1000

1500

2000

RV

R/m

10

15

20

25TemperatureDew PointRVRWind

−10

−5

0

5

win

d sp

eed/

knot

s

15:00 18:20 21:40 01:00 04:200

2000

4000

6000

8000

10000

Ver

tical

vis

ibili

ty/ft

Vertical visWind Arsenal

15:00 18:00 21:00 00:00 03:00 06:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

160

15:00 18:00 21:00 00:00 03:00 06:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

15:00 18:00 21:00 00:00 03:00 06:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

15:00 18:00 21:00 00:00 03:00 06:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

161

18:00 21:00 00:00 03:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00−2000

−1500

−1000

−500

0

500

1000

1500

2000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

18:00 21:00 00:00 03:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

18:00 21:00 00:00 03:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00−1500

−1000

−500

0

500

1000

1500

2000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

18:00 21:00 00:00 03:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20020205

14:00 17:20 20:40 00:00 03:20 06:40 10:00 13:20−5

0

5

10

0

500

1000

1500

2000

RV

R/m

−5

0

5

10TemperatureDew PointRVRWind

14:00 17:20 20:40 00:00 03:20 06:40 10:00 13:20−5

0

5

10

15

0

500

1000

1500

2000

RV

R/m

−5

0

5

10TemperatureDew PointRVRWind

162

14:00 17:20 20:40 00:00 03:20 06:40 10:00 13:20−5

0

5

10

0

500

1000

1500

2000R

VR

/m

−5

0

5

10TemperatureDew PointRVRWind

14:00 17:20 20:40 00:00 03:20 06:40 10:00 13:20−5

0

5

10

0

500

1000

1500

2000

RV

R/m

−5

0

5

10TemperatureDew PointRVRWind

−5

0

5

10

win

d sp

eed/

knot

s

14:00 17:20 20:40 00:00 03:20 06:40 10:00 13:200

2000

4000

6000

8000

10000

Ver

tical

vis

ibili

ty/ft

Vertical visWind Arsenal

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

163

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

164

18:00 21:00 00:00 03:00 06:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00 06:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

18:00 21:00 00:00 03:00 06:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00 06:00−2000

−1500

−1000

−500

0

500

1000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

18:00 21:00 00:00 03:00 06:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00 06:00−2000

−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

18:00 21:00 00:00 03:00 06:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

18:00 21:00 00:00 03:00 06:00−2000

−1500

−1000

−500

0

500

1000

1500

2000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20020316

15:00 18:20 21:40 01:00 04:20 07:40 11:00−5

0

5

10

15

20

0

500

1000

1500

2000

RV

R/m

0

5

10

15TemperatureDew PointRVRWind

15:00 18:20 21:40 01:00 04:20 07:40 11:00−5

0

5

10

15

20

0

500

1000

1500

2000

RV

R/m

0

5

10

15TemperatureDew PointRVRWind

165

15:00 18:20 21:40 01:00 04:20 07:40 11:00−5

0

5

10

15

20

0

500

1000

1500

2000R

VR

/m

0

5

10

15TemperatureDew PointRVRWind

15:00 18:20 21:40 01:00 04:20 07:40 11:00−10

−5

0

5

10

15

0

500

1000

1500

2000

RV

R/m

0

5

10

15TemperatureDew PointRVRWind

−10

0

10

20

win

d sp

eed/

knot

s

15:00 18:20 21:40 01:00 04:20 07:40 11:000

2000

4000

6000

8000

10000

Ver

tical

vis

ibili

ty/ft

Vertical visWind Arsenal

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

166

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

167

21:00 00:00 03:00 06:00 09:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

21:00 00:00 03:00 06:00 09:00−2000

−1500

−1000

−500

0

500

1000

1500

2000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

21:00 00:00 03:00 06:00 09:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

21:00 00:00 03:00 06:00 09:00−2000

−1500

−1000

−500

0

500

1000

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

21:00 00:00 03:00 06:00 09:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

21:00 00:00 03:00 06:00 09:00−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

21:00 00:00 03:00 06:00 09:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

21:00 00:00 03:00 06:00 09:00−2000

−1500

−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

20021101

15:00 18:20 21:40 01:00 04:20 07:40 11:00 14:20−5

0

5

10

0

500

1000

1500

2000

RV

R/m

4

6

8

10

12

TemperatureDew PointRVRWind

15:00 18:20 21:40 01:00 04:20 07:40 11:00 14:20−2

0

2

4

6

8

0

500

1000

1500

2000

RV

R/m

4

6

8

10

12

TemperatureDew PointRVRWind

168

15:00 18:20 21:40 01:00 04:20 07:40 11:00 14:20−5

0

5

10

0

500

1000

1500

2000R

VR

/m

4

6

8

10

12

TemperatureDew PointRVRWind

15:00 18:20 21:40 01:00 04:20 07:40 11:00 14:20−2

0

2

4

6

8

0

500

1000

1500

2000

RV

R/m

4

6

8

10

12

TemperatureDew PointRVRWind

−10

−5

0

5

10

15

win

d sp

eed/

knot

s

15:00 18:20 21:40 01:00 04:20 07:40 11:00 14:200

2000

4000

6000

8000

10000

Ver

tical

vis

ibili

ty/ft

Vertical visWind Arsenal

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

169

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

12:00 18:00 00:00 06:00 12:00 18:000

500

1000

1500

2000

2500

Time

RV

R/m

central valuemeanmean +/− std.dev.

170

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1200

−1000

−800

−600

−400

−200

0

200

400

600

800

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−800

−600

−400

−200

0

200

400

600

800

1000

1200

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1000

−800

−600

−400

−200

0

200

400

600

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

03:00 06:00 09:00 12:00200

400

600

800

1000

1200

1400

1600

1800

2000

Time

RV

R/m

03:00 06:00 09:00 12:00−1000

−500

0

500

1000

1500

Diff

eren

ce/m

RVRMean1 minus Mean2Mean1 (t, t−1, t−2)Mean2 (t−10, t−11, t−12)

171

References Aamodt, A., and E. Plaza, 1994: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, IOS Press, 7, 39–59. Allan, S. S., Gaddy, S. G., and Evans, J. E. 2001: Delay causality and reduction at the New York City airports using terminal weather information systems, Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Mass. Allard, E., 1876: Memoire sur L’intensite et la Portee des Phares, Paris, Dunod. American Meteorological Society, 2005 : amsglossary.allenpress.com Austrocontrol, 2005: www.austrocontrol.at Burnley, S. (1), M. McKinney (2), T. A. Seliga (3), D. Burnham (4), and J. Goslin (5), 2004: Federal aviation administration requirements for runway visual range (RVR) visibility and ambient light sensors. 1. Federal Aviation Administration (FAA), Washington, DC 2. FAA William J., Hughes Technical Center, Atlantic City, NJ 3. Volpe National Transportation Systems Center, Cambridge, MA 4. Scientific & Engineering Solutions, Inc., Orleans, MA 5. Titan Corporation, Egg Harbor Township, NJ. 11th Conference on Aviation, Range, and Aerospace. Charles G. (Lin) Lindsey,1999: Aviation weather requirements in the air traffic management system. 8th Conference on Aviation, Range, and Aerospace Meteorology, January 10-15, 1999, Dallas, TX, American Meteorological Society. Cotton, W. R., and R. A. Anthes, 1989: Storm and Clouds Dynamics, Academic Press, Inc. 303-367. Dubois, D., F. Esteva, P. Garcia, L. Godo, L. de Mantaras, and H. Prade, 1997: Fuzzy modelling of case-based reasoning and decision, Case-Based Reasoning Research and Development. Proceedings of the 2nd International Conference on Case-Based Reasoning Evans, J. E., 1997: Safely Reducing Delays Due to Adverse Terminal Weather. International Workshop Modelling and Simulation in Air Traffic Management, Springer-Verlag, 185-202. Evans, J. E., S. Allan, and M. Robinson, 2004: Quantifying delay reduction benefits for aviation convective weather decision support systems. Proceedings of the 11th Conference on Aviation Range and Aerospace Meteorology, Hyannis, MA 2004. Free On-line Dictionary of Computing: http://foldoc.doc.ic.ac.uk/foldoc Friedrichs, P., and J. Lancaster, 2003: Study highlights the potential benefit of improved accuracy in aerodrome forecasts. ICAO Journal, Vol 58, 14-16, 29.

172

Gerapetritis, H., and J. M. Pelissier, 2004: The crtical success index and warning strategy. 17th Conference on Probability and Statistics in the Atmospheric Sciences, Tuesday 13 January, 2004. Hansen, B. K., 2000: Analog forecasting of ceiling and visibility using fuzzy sets. 2nd Conference on Artificial Intelligence, American Meteorological Society Hansen, B. K., 2000: Weather Prediction Using Case-Based Reasoning and Fuzzy Set Theory. Master of Computer Science thesis, Technical University of Nova Scotia, Halifax, Nova Scotia, Canada. Hansen, B. K., and Riordan, D., 1998: Fuzzy case-based prediction of ceiling and visibility, 1st Conference on Artificial Intelligence, American Meteorological Society, 118-123. Hansen, B. K., 2005: A fuzzy logic based analog forecasting system for ceiling and visibility. 38th Congress of the Canadian Meteorological and Oceanographic Society, 31 May - 3 June 2005, Edmonton, Alberta. Hansen, B. K., 2005: A fuzzy logic based analog forecasting system for ceiling and visibility. 39th Congress of the Canadian Meteorological and Oceanographic Society, 31 May - 3 June 2005, Vancouver, British Columbia. Fritzl, H., 2002: Factors that favour the formation and dissipation of fog/low stratus in Vienna Herzegh, P. H., Bankert, R. L., Hansen, B. K., Tryhane, M., and Wiener, G., 2004: Recent progress in the development of automated analysis and forecast products for ceiling and visibility conditions. 20th International Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, American Meteorological Society, 11-15 January 2004, Seattle, Washington. Herzegh, P., S. Benjamin, B. Brown, R. Rasmussen, T. Tsui, G. Wiener, and P. Zwack, 2003: Development of automated analysis and forecast products for adverse ceiling and visibility conditions. 19th International Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, American Meteorological Society. Herzegh, P.H., G. Wiener, R. Bateman, and B. Weekley, 2005: Automated forecasting of ceiling and visibility for aviation: exploration of a data mining-based forecast method. Fourth Conference on Artificial Intelligence Applications to Environmental Science. Hicks, T., T. Crawford, and M. Wilson, 2003: A fuzzy logic system for automated short term aviation weather forecasts. 3rd Conference on Artificial Intelligence Applications to Environmental Science, American Meteorological Society. ICAO, 2005: www.icao.int Kaltenböck, R., 2005: Pictures of Vienna International Airport

173

Keith R., and S. M. Leyton, 2004: An experiment to measure the value of statistical probability forecasts for aerodromes. 11th Conference on Aviation, Range, and Aerospace. Keith, R., 2003: Optimization of value of aerodrome forecasts. Weather and Forecasting, Vol. 18, 808-824. Koschmieder, H., 1924: Theorie der horizontalen Sichtweite. Beitr. Phys. Atmos., Vol. 12, 33-53 Kraus, H., 2001: Die Atmosphäre der Erde, Springer Verlag. 77-90, 217-227. Kulesa, G. J., D. J. Pace, W. L. Fellner, J. E. Sheets, V. S. Travers, and P. J. Kirchoffer, 2004: Improving Aviation Weather Forecasts: The Federal Aviation Administration's Efforts to Enhance Air Traffic Safety and Efficiency. 20th International Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, American Meteorological Society, 11-15 January 2004, Seattle, Washington. Lambergeon, D., G. Monceau, 2004: Response to the need for nowcasts and very short-term forecasts of visibility and height of cloud base at Roissy-Charles-de-Gaulle airport (CDG). Bulletin of the World Meteorological Organization, Vol,. 53, 313-315. Leyton, S. M., and J. M. Fritsch, 2002: Improved short-term probabilistic forecasts of ceiling and visibility. 10th Conference on Aviation, Range, and Aerospace Meteorology, American Meteorological Society. Leyton, S. M., and J. M. Fritsch, 2003: Short term probabilistic forecasts of ceiling and visibility utilizing high-density surface weather observations. Weather and Forecasting, Vol. 18, 891-902. Leyton, S. M., and J. M. Fritsch, 2004: The impact of high-frequency surface weather observations on short-term probabilistic forecasts of ceiling and visibility. Journal of Applied Meteorology, Vol. 43, 145-156. Liljequist, G. H., and K. Cehak, 1984: Allgemeine Meteorologie, 3. Auflage, Verlag Vieweg und Sohn Braunschweig/Wiesbaden. 137-142. Murphy, A. H., and M. Ehrendorfer, 1987: On the relationship between the accuracy and value of forecasts in the cost-loss ratio situation. Weather and Forecasting Vol. 2, 243-251. Meteorological Service of Canada, 2005: www.msc-smc.ec.gc.ca Murtha, J., 1995: Applications of fuzzy logic in operational meteorology. Scientific Services and Professional Development Newsletter, Canadian Forces Weather Service, 42-54. National Weather Association, 2005: www.nwas.org Niederl, A., 2005: Praktikumsbericht August 2005, Flughafen Innsbruck.

174

Office of the Federal Coordinator for Meteorology, 2005: www.ofcm.gov Peer, C., 2003: An evaluation of weather parameters causing aircraft departure and arrival delays at Vienna International Airport, Master of Science thesis, Leopold Franzens Universität, Innsbruck. Rella, S., 2002: Fog dissipation by artificial heating, Master of Science thesis, Leopold Franzens Universität Innsbruck Riesbeck, C. K., and R. C. Schank, 1989: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, Hillside, New Jersey, USA. Sasse, M., 2000: Pilotstudie zum Wettereinfluss auf den Flugverkehr. Diplomarbeit, Institute for Meteorology and Climatology, University Hannover. Stull, R. B., 2000: Meteorology for Scientists and Engineers, Second Edition, Brooks/Cole. 95-118, 149-154. Vaisala, 2005: homepage: www.vaisala.com Valdez, J. 2000. NWS Reinventing Goals for 2000, National Weather Service, National Oceanic and Atmospheric Administration. United States Department of Commerce. Vislocky, R. L., and J. M. Fritsch, 1997: An automated, observations-based system for short-term prediction of ceiling and visibility. Weather and Forecasting, Vol. 12, 31-43.