meteosat second generation (msg) cloud mask, cloud ... · cloud heights were greater than 3000m....

Meteosat Second Generation (MSG) Cloud

Mask, Cloud Property Determination and

Rainfall Comparison with In-situ Observations

Peter Silla Masika

March, 2007

Meteosat Second Generation (MSG) Cloud Mask,

Cloud Property Determination and Rainfall

Comparison with In-situ Observations

By

Peter Silla Masika

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in

partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science

and Earth Observation in Water Resources and Environmental Management Programme

Specialisation: Advanced use of Remote Sensing in Water Resources Management, Irrigation and

Drainage

Thesis Assessment Board

Prof. Dr. Ir. Z. Su Chairman (ITC, Enschede)

Dr. Ir. M. Booij External Examiner (Twente University, Enschede)

Dr. B. H. P. Maathuis Primary Supervisor (ITC, Enschede)

Dr. T. H. M. Rientjes Member (ITC, Enschede)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

Disclaimer

This document describes work undertaken as part of a programme of study at the International

Institute for Geo-information Science and Earth Observation. All views and opinions expressed

therein remain the sole responsibility of the author, and do not necessarily represent those of

the institute.

Dedicated to my wife, daughter and departed soul of my son &

To my parents

i

Abstract

To obtain accurate estimates of surface and cloud parameters from satellite data an algorithm has to

be developed which identifies cloud-free and cloud-contaminated pixels. Data from the Spinning

Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG)

satellites have been available since February 2004. The data is accessible to National Meteorological

and Hydrological Services (NMHSs). Unfortunately for NMHSs from developing countries the data

until recently has never been exhaustively exploited for rainfall estimation, one very important

variable in the atmosphere. Developed countries through research institutions have to some extent set

in place ways and means of exploiting the MSG data that has been made possible through data

distribution policy of EUMETSAT (free access to the data for research and education).

This study attempts to utilize available MSG data for developing simple cloud mask and height

algorithms and thereafter compare and determine the relationship between cloud height and observed

rainfall on a ground station. A multispectral threshold technique has been used: the test sequence

depends on solar illumination conditions and geographical location whereas most thresholds used here

were empirically determined and applied to each individual pixel to determine whether that pixel is

cloud-free or cloud-contaminated. The study starts from the premise of an acceptable trade-off

between calculation speed and accuracy in the output data. For this reason, only three infrared

channels of MSG satellite were used alongside climatological data provided by National

Oceanographic and Atmospheric Administration (NOAA) and also land surface climatological data

available from the WorldClim website.

The accurate measurement of spatial and temporal variation of tropical rainfall around the globe

remains one of the critical unresolved problems in the field of meteorology. This study attempted to

compare computed cloud height and observed rainfall on ground station (CGIS-Butare, Rwanda) and

derived cloud height-total rainfall relationship from storms over the same station.

Results from the simple cloud mask algorithm were validated using EUMETSAT cloud mask products

for a tropical region (≈ 11°N - 14°S and ≈ 6° - 51°E) over Africa. Overall accuracy of the simple

cloud mask developed here was found to be 87% for four scenes which were during day- and night-

time as well as twilight time as defined by sun elevation angles. Analysis of recorded rainfall at CGIS

and comparison of the same with computed cloud height showed that rainfall mainly occurred when

cloud heights were greater than 3000m. Further, deriving a relationship between the observed rainfall

and the cloud height was found to follow a Gaussian model in which clouds at approximate heights

between 4000m and 5000m produced higher amounts of rainfall. Below and above this height range,

rainfall amounts were found to be generally low. The derived cloud height-total rainfall relationship

was applied to other storms over this station. Initial results show low correlation between estimated

and observed rainfall. More synoptic observations have to be used to evaluate the derived

relationship. Next to this a better procedure to differentiate nimbostratus and cumulonimbus has to be

incorporated. Different relations between height and observed rainfall for the two types of clouds may

be derived which may improve the overall results.

KEY WORDS: MSG-SEVIRI, cloud mask, cloud height/type, rainfall comparison/estimation.

ii

Acknowledgements

First and foremost I am grateful to the government of the Netherlands for providing me the

scholarship under the Netherlands Fellowship Programme to pursue the M.Sc. course in Water

Resources and Environmental Management in this unique institution, ITC. I am equally grateful to my

organization, Kenya Meteorological Department for allowing me to fulfil my long-term dream of

attaining M.Sc. in Remote Sensing-related course.

My greatest gratitude goes to my supervisor, Dr. Ben H.P. Maathuis for his critical comments and

inputs. I wish to say your support was tremendous. Great thanks to all WREM staff especially Prof. Z.

Zu, Dr. A. Gieske, Dr. T. Rientjes, and Dr. R. Becht, for their valuable critical comments in this study.

This study could not have been accomplished without EUMETSAT’s favourable data distribution

policy for research and education. Special thanks go to this great organization and equally appreciate

their efforts in promoting satellite meteorology.

Yet again many thanks go to all ever dedicated staff members of WREM department at ITC for

imparting this valuable knowledge during that past 18 months. Equally thanks to the Programme

Director, Dr. Arno van Lieshout for his excellent assistance and cooperation.

I would like to appreciate MSG laboratory staff, specifically Mr. G. Reinink, for his tireless assistance

in retrieving satellite data.

I am grateful to all WREM 2006 course mates especially Essayas, Jose, Beyene, Edna, Anoja, Irena,

Marie, Musefa, and Mohammad for their continuous support and friendship bestowed on me during

that one and half years.

Special thanks go to all my friends with whom I shared my days in Enschede for eighteen months.

Fellow Kenyans cannot be left out for their encouragement during that period was great.

To my dear wife, Nancy and our lovely daughter, Faith thanks for your patience, prayers and daily

expectation of seeing me again. I sincerely owe you alot for all these. To my caring parents, my

brothers and sisters, and all friends in Kenya, your support and prayers cannot fail to be appreciated

too.

iii

List of Acronyms

A/MSU Advanced/Microwave Sound Unit

ANN Artificial Neural Networks

APOLLO AVHRR Processing Scheme Over cLouds, Land and Ocean

A/TOVS Advanced/Tiros-N Operational Vertical Sounder

AVHRR Advanced Very High Resolution Radiometer

BSC Bi-spectral Spatial Coherence

BTH Bi-spectral Threshold and Height

CCD Cold Cloud Duration

CCS Cloud Classification System

CGIS Geographic Information Systems and Remote Sensing Regional Outreach Centre in

Butare, Rwanda

CPC Climate Prediction Centre

CST Convective Stratiform Technique

DEM Digital Elevation Model

DMSP Defence Meteorological Satellite Program

EUMETcast European Organisation for the Exploitation of Meteorological Satellites’ Broadcast

System for Environmental Data

EUMETSAT European Organisation for the Exploitation of Meteorological Satellites

GAC Global Area Coverage

GHCC Global Hydrology and Climate Centre

GIS Geographic Information Systems

GOES Geostationary Operational Environmental Satellite

GRIB General Regularly-distributed Information in Binary form (GRIded Binary)

HIRLAM High Resolution Limited Area Model

HIRS High-resolution Interferometer Sounder

HRPT High Resolution Picture Transmission

HRV High Resolution Visible

IAPP International Advanced/Tiros-N (Television Infrared Observation Satellite-Next

generation) Operational Vertical Sounder (A/TVOS) Processing Package

IDL Interactive Data Language

ILWIS Integrated Land and Water Information System

ITCZ Inter-tropical Convergence Zone

ITPP International Tiros-N (Television Infrared Observation Satellite-Next generation)

Operational Vertical Sounder (TVOS) Processing Package

KLAROS Royal Netherlands Meteorological Institute (KNMI) Local APOLLO Retrievals in

an Operational System

KNMI Royal Netherlands Meteorological Institute

LAC Local Area Coverage

MPE Multi-sensor Precipitation Estimate

MSG Meteosat Second Generation

NOAA National Oceanographic and Atmospheric Administration

iv

NOAA-HL National Oceanographic and Atmospheric Administration (NOAA) High Latitude

NODC National Oceanographic Data Centre of the National Oceanographic and

Atmospheric Administration (NOAA)

NMHS National Meteorological and Hydrological Service

NWP Numerical Weather Prediction

NWS National Weather Service

PERSIAN Precipitation Estimation from Remotely Sensed Information using Artificial Neural

Networks

SAFNWC Satellite Application Facility for supporting Nowcasting and very short range

forecasting

SCM Simple Cloud Mask

SCH/T Simple Cloud Height/Type

SEVIRI Spinning Enhanced Visible and Infrared Imager

SSM/I Special Sensor Microwave Imager

SST Sea Surface Temperature

SYNOP World Meteorological Organization synoptic code for weather observations

TAMSAT Tropical Applications of Meteorology using SATellite

TIP-data TIROS-N (Television Infrared Observation Satellite-Next generation) Information

Processor data

TRMM Tropical Rainfall Measuring Mission

USGS United States Geological Survey

WMO World Meteorological Organization

v

Table of contents

Abstract ……………………………………………………………………………………………….. i

Acknowledgements …………………………………………………………………………………… ii

Table of contents ……………………………………………………………………………………... iii

List of figures ………………………………………………………………………………………… v

List of tables …………………………………………………………………………………………. vii

List of Acronyms ………………………………………………………………………………….... viii

1. Introduction ....................................................................................................................................1

1.1. Background.............................................................................................................................1

1.2. Significance of the Study........................................................................................................2

1.3. Research Objectives................................................................................................................3

1.4. Research questions..................................................................................................................3

1.4.1. General ...............................................................................................................................3

1.4.2. Specific ...............................................................................................................................4

1.5. Research Hypothesis...............................................................................................................4

1.6. Scope of Study........................................................................................................................4

1.7. Logical Sequence of Research Approach / Methodology ......................................................5

1.8. Outline of the Thesis...............................................................................................................6

2. Literature Review...........................................................................................................................7

2.1. Introduction.............................................................................................................................7

2.2. Meteosat Second Generation (MSG) Satellite........................................................................9

2.3. Cloud Masking......................................................................................................................10

2.3.1. Météo-France (SAFNWC) Cloud Mask...........................................................................11

2.3.2. Météo-France (Ocean and Sea Ice SAF) Cloud Mask .....................................................13

2.3.3. Meteosat VIS-IR and NOAA-A/TOVS Image fusion Cloud Mask .................................15

2.3.4. KNMI Cloud Mask Algorithm .........................................................................................16

2.3.5. KLAROS Cloud Mask Algorithm....................................................................................16

2.3.6. APOLLO Cloud Mask......................................................................................................17

2.3.7. GHCC Cloud Mask ..........................................................................................................17

2.3.8. AFWA Cloud Mask Algorithm........................................................................................18

2.4. Precipitation Processes .........................................................................................................20

2.5. Satellite Rainfall Estimation.................................................................................................20

2.5.1. Cloud-Indexing Methods..................................................................................................21

2.5.2. Bi-spectral Methods .........................................................................................................21

2.5.3. Life-history Methods ........................................................................................................22

2.5.4. Cloud Model-based Techniques.......................................................................................23

2.5.5. Blending Techniques........................................................................................................24

2.5.5.1. EUMETSAT Multi-sensor Precipitation Estimate (MPE) ......................................26

3. Materials and Methods ................................................................................................................27

3.1. Data Acquisition ...................................................................................................................27

vi

3.1.1. MSG Satellite Data.......................................................................................................... 27

3.1.2. Climatological Data......................................................................................................... 29

3.1.3. Dew Point Temperature................................................................................................... 30

3.1.4. Synoptic Data and Field work ......................................................................................... 33

3.2. Cloud Masking Method ....................................................................................................... 36

3.3. Rainfall Estimation Method................................................................................................. 37

4. Data Processing and Results....................................................................................................... 39

4.1. MSG Satellite Images .......................................................................................................... 39

4.1.1. Generation of MSG Satellite and Solar Angles............................................................... 39

4.1.2. Day-time Cloud Mask...................................................................................................... 41

4.1.3. Night-time Cloud Mask ................................................................................................... 43

4.1.4. Twilight Cloud Mask....................................................................................................... 44

4.2. Rainfall Estimation (A case of CGIS Weather station) ....................................................... 48

4.2.1. Direct Comparison of Cloud Height and Rainfall Intensity............................................ 49

4.2.2. Direct Comparison of Cloud Height and Total Rainfall ................................................. 52

5. Discussions of Results.................................................................................................................. 57

5.1. Cloud Mask Results ............................................................................................................. 57

5.2. Cloud Height/Type Results.................................................................................................. 62

5.3. Rainfall Estimation Results ................................................................................................. 63

6. Conclusions and Recommendations .......................................................................................... 67

6.1. Conclusions.......................................................................................................................... 67

6.2. Recommendations................................................................................................................ 68

References ............................................................................................................................................ 70

Appendices ........................................................................................................................................... 73

Appendix A: ILWIS Script for Simple Cloud Mask and Height Algorithms ................................... 73

Appendix B: Samples of Batch Files ................................................................................................ 84

Appendix C: Sample of CGIS Weather Station Data........................................................................ 85

Appendix D: Storms over CGIS Weather Station used for Developing Cloud height – Rainfall

Intensity Regression Function ........................................................................................................... 92

Appendix E: Storms over CGIS Weather Station used for Developing Cloud height – Total Rainfall

Regression Function.......................................................................................................................... 94

Appendix F: Sample of Rain gauge (tipping bucket) Rainfall Data (Nairobi- Dagoretti

Meteorological Station)..................................................................................................................... 96

vii

List of figures

Figure 1-1: MSG/SEVIRI image of 5th July 2006 at 12:00 UTC as a false Colour Composite...............4

Figure 1-2: Three major phases and important steps in the methodology ...............................................5

Figure 2-1: Cloud types grouped into different families according to height range and form (Source:

Strahler, 1965) ........................................................................................................................................8

Figure 2-2: MSG image false colour composite (BGR) of 25th December 2006 at 12:00 UTC............10

Figure 2-3: MSG cloud mask for 25th December 2006 at 12:00 UTC (EUMETSAT, 2006) .................11

Figure 2-4: GOES-East and MSG satellites SST products (in ˚C) for 25th Dec 2006 at 13:00 UTC ....14

Figure 2-5: An example of pixel array under consideration with Tmin at (i,j) ........................................23

Figure 2-6: Some factors influencing the differences between space- and time-collocated TMI and

SSMI.......................................................................................................................................................24

Figure 2-7: The PERSIAN CCS model structure (source:(Hong et al., 2004a)) ..................................25

Figure 3-1: Flow chart for simple cloud mask (SCM) and cloud height/type (SCH/T) retrieval ..........27

Figure 3-2: MSG Data Retriever window (Courtesy of ITC) ................................................................28

Figure 3-3: False colour composite (bands 1, 2, and 3 –in BGR) (left) and Band 9 (10.8µm) (right) on

19/12/2006 at 12:00 UTC.......................................................................................................................29

Figure 3-4: Climatological Temperature (in °K) images; (a) day-time (b) night-time (c) mean , of

Africa and part of Atlantic Ocean for the month of May.......................................................................30

Figure 3-5: Schematic view of temperature lapse rates in an idealized convective cloud.....................32

Figure 3-6: Locations of the four stations shown on MSG satellite false colour composite image ......33

Figure 3-7: (a) Image acquisition by SEVIRI radiometer, and (b) Schematic diagram on MSG satellite

and Rain gauge observation time ...........................................................................................................35

Figure 4-1: Flow chart for generating MSG satellite and Sun angles....................................................39

Figure 4-2: Sun (for 26th December 2006 at 15:00 UTC) and MSG Satellite (0˚N and 0˚E) ................40

Figure 4-3: Solar illumination conditions on 26th December 2006 at 15:00 UTC.................................40

Figure 4-4: Description of the test sequences for Land surface (left) and Sea surface (right) ..............41

Figure 4-5: Description of test sequence for land surface (left) and sea surface (right)........................43

Figure 4-6: Description of test sequence for land surface (left) and sea surface (right)........................44

Figure 4-7: Cloud masks for (a) day-time, (b) twilight time, and (c) night-time; for MSG-1 image of

7th March 2006 at 15:30 UTC. ...............................................................................................................45

Figure 4-8: Solar illumination conditions on 7th March 2006 at 15:30 UTC.........................................45

Figure 4-9: Cloud mask (a) and false colour composite (b) for MSG image of 07/03/2006 at 15:30

UTC........................................................................................................................................................46

Figure 4-10: Cloud height (in Meters) image (MSG image of 07/03/2006 at 15:30 UTC)...................47

Figure 4-11: Classified cloud height image of 07/03/2006 at 15:30 UTC.............................................47

Figure 4-12: Segments (yellow lines) of cloud mask of 23/11/2005 at 13:30 UTC overlaid on False

colour composite (VIS006, VIS008, and NIR016) in (BGR) ................................................................48

Figure 4-13: Diurnal cloud height and Rainfall intensity changes on (a) 5th May 2006, and (b) 10th

May 2006................................................................................................................................................50

Figure 4-14: Rainfall intensities within cloud height classes ................................................................50

Figure 4-15: Gaussian model fit, X = Average cloud height (m), Y = Average rainfall intensity

(mm/hr)...................................................................................................................................................51

viii

Figure 4-16: Observed and estimated rainfall intensity for different storms ........................................ 51

Figure 4-17: Diurnal cloud height and Total rainfall changes on (a) 5th May 2006, and (b) 10th May

2006....................................................................................................................................................... 52

Figure 4-18: Gaussian model fit, X= Average storm height (m), Y= Total rainfall (mm) ................... 53

Figure 4-19: Observed and Estimated total rainfall plotted with the error bars ................................... 54

Figure 4-20: Relationship between the observed and the estimated total rainfall ................................ 55

Figure 5-1: Flow chart on segmentation and visualization of EUMETSAT CLM and SCM............... 58

Figure 5-2: EUMETSAT cloud mask assigned feature classes for 26th December 2006 at 15:00 UTC ..

..................................................................................................................................... 59

Figure 5-3: Cloud mask segments of EUMETSAT CLM (yellow lines) and SCM (red lines) for 26th

December 2006 at 15:00 UTC, on a false colour composite ................................................................ 59

Figure 5-4: EUMETSAT CTH (a), SCH/T (b), and Difference (between CTH and SCH/T) (c) images

for 25th December 2006 at 11:45 UTC (height is in meters)................................................................. 63

Figure 5-5: Diurnal height and Total rainfall changes on 1st March 2006 (left) and 28th October 2006

(right) over Naivasha station ................................................................................................................. 65

ix

List of tables

Table 2-1: Spectral channel characteristics of SEVIRI in terms of central, minimum and maximum

wavelength of the channels and the main application areas of each channel (Source: (EUMETSAT,

2006)) ............................................................................................................................................9

Table 2-2: Definition of illumination conditions (SAFNWC); solar elevation is in degrees ................12

Table 2-3: Test sequence over land (SAFNWC) ...................................................................................12

Table 2-4: Test sequence over sea (SAFNWC).....................................................................................12

Table 3-1: Locations of the four stations within Eastern Africa............................................................33

Table 4-1: Observed storms and their total amount of rainfall ..............................................................53

Table 4-2: Storm heights and estimated total rainfall ............................................................................54

Table 5-1: Contingency table for MSG image of 25th December 2006 at 12:00 UTC ..........................60

Table 5-2: Contingency table for MSG image of 26th December 2006 at 15:00 UTC ..........................60

Table 5-3: Contingency table for MSG image of 4th January 2007 at 22:00 UTC ................................61

Table 5-4: Contingency table for MSG image of 10th January 2007 at 17:00 UTC ..............................61

METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-

SITU OBSERVATIONS

1

1. Introduction

1.1. Background

For more than 40 years, meteorological satellites have been the best way to observe the changing

weather on a large scale (EUMETSAT, 2006) . Typically, operational meteorology utilizes two types

of satellites, namely; polar orbiting and geostationary satellites, to provide the required information.

Polar orbiting satellites fly at relatively low altitudes of approximately 800km above the earth surface

and can provide information based on a high spatial resolution. Geostationary satellites, on the other

hand, are in the equatorial plane and at high altitudes of about 36000km above the earth surface. Their

revolution time is the same as that of the earth itself and therefore the satellites are always viewing the

same area on the earth. They have low spatial resolution due to their altitudes. However, they can

perform frequent imaging, in animated mode, which can depict the ever-changing atmospheric

processes.

The first generation of European meteorological satellites dates back to 1977, with the launch of

Meteosat-1. Since then this series have advanced to Meteosat-7 which is currently located around

57°E and manoeuvring to replace Meteosat-5 located at 63°E.

These series are followed by Meteosat Second Generation (MSG) satellites of which the first one

(MSG-1 now Meteosat-8) was launched on 28th August 2002 and became operational in early 2004.

The second of this series (MSG-2 now Meteosat-9) was launched on 20th December 2005. These two

satellites are located at 0°N and 0°E.

MSG satellites are spin-stabilized and capable of greatly enhanced earth observations (EUMETSAT,

2006) . The Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor on board MSG has a

high temporal resolution of 15 minutes and spatial resolution of 3 km (sub-satellite) for all channels

except 1km for high resolution visible (HRV) channel. The major improvement for this series of

satellites is the enhanced spectral resolution of 12 channels. The presence of the 3.9µm channel in the

current sensor has allowed analyses of cloud cover especially at night-time.

The primary mission of MSG satellites is the continuous observation of earth’s full disk with a multi-

spectral imager. The repeat cycle of 15 minutes for full-disk imaging provides multi-spectral

observations of rapidly changing phenomena such as deep convection. They also provide better

retrieval of wind fields which are obtained from the tracking of clouds, water vapour and ozone

features. In this study, main attention is given to cloud properties, such as cloud height, that may be

associated with rainfall amounts observed a ground station.

Presence and characteristics of clouds gives information about the state of the atmosphere. For many

cloudy situations, the reflected visible radiation and the emitted thermal radiation are not simple to

interpret because the cloud is not the only reflecting/radiating source (Dlhopolsky and Feijt, 2001). Of


SITU OBSERVATIONS

2

importance is to determine cloud properties by first distinguishing cloud-free pixels from cloud-

contaminated pixels. Quantitative data sets obtained from the cloud-contaminated pixels have several

potential applications one of which is for water resources and environmental management.

In general, effective integrated water resources management requires timely, accurate and

comprehensive meteorological, hydrological and other related information. Use of satellites in

observing variables such as rainfall, evaporation and soil moisture has enhanced provision of these

data in a timely and effective manner for the water resources management sector. These

meteorological variables needs to be monitored effectively and since they are associated with

atmospheric moisture hence clouds, there is need to identify the clouds first through masking all

cloud-contaminated areas in satellite images.

Cloud masking allows identifying cloud-free areas where other products such as land or sea surface

temperatures may be computed. It also allows identifying cloudy areas where other products (e.g.

cloud types and cloud top temperature/height) may be derived. Cloud type on the other hand provides

a detailed cloud analysis. It may be used as input to an objective meso-scale analysis which in turn

may be used in a simple nowcasting scheme (Météo-France, 2005b). Cloud type product is essential

for generation of cloud top temperature and height products and for identification of precipitating

clouds which in turn may be used to estimate rainfall intensity/amount.

1.2. Significance of the Study

For a considerable long time, series of precipitation amounts are recorded worldwide. Such amounts,

mainly expressed in millimetres (mm) and collected during a day or an hour are not only useful for

general meteorological and climatological practices, but are of special interest for hydrology and

agricultural meteorology. Surface-based observations of precipitation is accomplished primarily by

gauges and, where economically viable, by radars. Over the world’s oceans these measurements are

often done on buoyancies which are few worldwide. On the other hand, over the land areas the

coverage from surface observations is not uniform. Worse still, ground measurements from the

conventional rain gauges have deteriorated over the last couple of years and thus an alternative is

being sought to continue providing precipitation measurements not only on spatial basis but also on

higher temporal scale. The field of remote sensing has advanced and through various meteorological

satellites precipitation estimation has been made possible to reasonable scales, for instance, 3km

(spatial resolution) and 15 minutes (temporal resolution) for MSG satellites.

However, satellites measure cloud properties (e.g. brightness temperature) an important product that

provides crucial information that can be used to infer rainfall intensity and/or total rainfall.

Understanding these properties, and the crucial information that can directly or indirectly be used in

water resources management, is important. Thus there is need for determining cloud type/height using

readily available MSG satellite data in order to estimate rainfall. (Maathuis et al., 2006) showed that

MSG retriever software developed at ITC can be used to retrieve MSG data and to estimate rainfall

over the entire MSG field of view which covers the whole of Africa and part of Europe. The results in

the study showed bias for low and high intensity rainfall amounts due to different types of clouds.


SITU OBSERVATIONS

3

Further, potential applications of developing cloud masking and cloud type algorithm are many. Some

of the most important ones nowadays are, in operational weather forecasting and in energy and water

balance studies. Clouds represent the most significant source of error in the extraction of earth surface

energy and water balance parameters out of meteorological satellite data (Valk et al., 1998) . Energy

and water balance models are used to estimate fluxes in cloudy conditions. In order to develop an

accurate energy balance mapping algorithms, cloud masking is essential.

Cloud mask and type software modules have been developed by the Centre de Météologie Spatiale of

Météo-France and are embedded in the Satellite Application Facility for supporting NoWCasting and

very short range forecasting (SAFNWC)/MSG software package that is distributed by EUMETSAT

(Derrien and Le Gléau, 2005). These cloud mask and type algorithms uses transfer functions derived

from atmospheric models which are not published. Most of National Hydrological Services, especially

in Africa, have no access to these transfer functions and even then may not be in a position to derive,

on their own, the transfer functions. Besides, due to financial limitations for most of these National

Hydrological Services, shareware or freeware (such as ILWIS) can be used for masking clouds and

determining the cloud type through semi-automated processing.

Thus there is a need for masking out clouds and determining their basic properties in order to improve

forecasted rainfall estimates from MSG. It is envisaged that improving rainfall estimation will assist

most of National Hydrological Services to provide information on the status of water resources within

their area of jurisdiction. It is also expected that it would further improve timely decision making for,

areas prone to disasters related to weather such as floods, landslides or areas frequently affected by

droughts. This therefore calls for a need to develop simple cloud mask (SCM) and cloud type/height

(SCH/T) algorithms which may be embedded in readily available shareware such as ILWIS.

1.3. Research Objectives

This study addressed the following two main objectives;

� Determination of cloud mask and cloud height/type on all daily MSG images; and

� Relating derived cloud height with rainfall at a ground rainfall station.

Specifically the study focused on:

� Developing simple cloud mask and height algorithms;

� Analyzing relationship between MSG cloud height images and observed rainfall intensity

and/or total rainfall at a ground station; and

� Using derived relation to estimate total rainfall from storms at various heights.

1.4. Research questions

1.4.1. General

Can simple cloud mask (SCM) and cloud height/type (SCH/T) algorithms be developed, using

ancillary input data from general climatology databases, and applied to MSG images covering the

whole of Africa?


SITU OBSERVATIONS

4

1.4.2. Specific

a) Can the cloud height from the masked cloud at every moment be used to determine

the cloud type?

b) Is there any relationship between the rainfall intensity and/ or total rainfall and the

cloud height/type?

c) Can the cloud mask and height algorithms developed be able to process

automatically MSG satellite data as they are received from EUMETSAT through

EUMETCast every 15 minutes?

1.5. Research Hypothesis

The study set the following hypotheses:

� The smaller the number of cloud forms appear in the atmosphere, the easier it becomes to

identify them in satellite imagery;

� The more complex the cloud mask algorithm structure becomes, the better it performs; and

� Satellite retrieved cloud properties can be related to rainfall observed on the earth surface.

1.6. Scope of Study

The study was conducted on the MSG field of view which covers the whole of Africa (≈ 39˚N - 38˚S

and ≈ 34˚W - 53˚E). MSG images as in figure 1-1 were subjected to the developed cloud mask and

cloud height algorithms.

However, for validation of the results a small portion (≈ 11°N - 14°S and ≈ 6°E - 51°E) of the view

was considered. Rainfall data from Geographic Information Systems and Remote Sensing Regional

Outreach Centre (CGIS), Rwanda was used. Also for the same purpose in-situ rainfall data was

collected from Nairobi-Dagoretti, Kisumu, and Naivasha in Kenya.

Figure 1-1: MSG/SEVIRI image of 5th July 2006 at 12:00 UTC as a false Colour Composite

(BGR) of bands 1, 2, and 3 (EUMETSAT, 2006)


SITU OBSERVATIONS

5

1.7. Logical Sequence of Research Approach / Methodology

The methodology in this study consists of three phases namely; pre-field work, field work campaign,

and post field work as shown in figure 1-2.

Figure 1-2: Three major phases and important steps in the methodology

Literature

review

Retrieving

MSG data

Retrieving

land surface

temperature

Retrieving

sea surface

temperature

Developing cloud mask and

height algorithms

Rainfall data

collection

Rainfall data

analysis

MSG image

processing with

developed algorithm

Comparing cloud height and total

rainfall and finding statistically

significant relation between total

storm rainfall and cloud height

Validation of the derived relation

Rainfall Estimation

Pre-field work Phase

Field work Phase

Post field work Phase


SITU OBSERVATIONS

6

1.8. Outline of the Thesis

The thesis consists of six chapters.

Chapter 1 is the current chapter within which this section is contained. As has been noted, this chapter

briefly introduces the study, outlining its justification, the objectives, and the scope of the study.

General approach to carry out the study has also been shown.

Chapter 2 contains literature review on various methodologies adopted for cloud mask algorithm. A

few of these algorithms are presented in this chapter. Also presented here is an overview of satellite

rainfall estimation methods.

Chapter 3 provides general approach to this current study with details of data requirement and

acquisition. Sources of various data are pointed out in this chapter. Steps undertaken to process some

of the data are explained.

Chapter 4 elaborates on the data processing and presents the results on various stages in the study.

Chapter 5 provides detailed analysis of various results and discussions attached to these results.

Chapter 6 finally presents conclusions and recommendations drawn from the study.


SITU OBSERVATIONS

7

2. Literature Review

2.1. Introduction

Condensation or deposition of water above the earth’s surface creates clouds which develop in an air

mass that becomes saturated. The air mass may have passed over warm bodies of water, or over wet

surfaces and carried upward by turbulence or convection. Saturation occurs by way of atmospheric

mechanisms that the temperature of the air mass is cooled to its dew point. The lifting required for

cooling and condensing this water vapour results from several processes, and study of these processes

provides a key for understanding the distribution of rainfall in various parts of the world.

The major mechanisms or processes that occur causing cloud development include:

A) Orographic uplift: - Occurs where air is forced to rise because of the physical presence of

elevated land. As the air parcel rises, it cools as a result of adiabatic expansion at a rate

approximately 1°C per 100m until saturation. Beyond saturation level the parcel rises moist

adiabatically at a rate of 0.6°C per 100m (Strahler, 1965).

B) Convectional lifting: - Associated with surface heating of the air at the ground surface. Once

enough heating occurs, the air mass becomes warmer and lighter than the surrounding

environment and then rise expanding and cooling. When sufficient cooling takes place,

saturation occurs forming clouds. This is the common phenomena within tropics forming

cumulus cloud and or cumulonimbus clouds (thunderstorms).

C) Convergence or Frontal lifting: - Takes place when two air masses come together of which

in most cases they have different temperature and moisture characteristics. In frontal lifting,

one of the air masses is usually warm and moist while the other is cold and dry. The leading

edge of the cold dry air acts as an inclined wall or front causing the moist warm air to be lifted

thereby cooling and saturation is finally reached. This is common phenomena in mid-latitudes

whereas near the equator winds from both northern and southern hemisphere meet at the Inter-

tropical Convergence Zone (ITCZ) and lifting, cooling, saturation of the air mass occur

forming clouds.

D) Radiative cooling:- Occurs when the sun no longer supplies the earth surface or water body

surface and overlying air with energy derived from solar insolation (e.g. at night). Here the

surface of the earth now begins to lose energy in the form of longwave radiation which cools

the ground and air above it. Clouds that result from this type of cooling take the form of

surface fog.

Basically clouds consist of extremely tiny droplets of water (≈ 0.02 to 0.06mm) in diameter, or minute

crystals of ice. Clouds appear white when thin or when sun shines on the outer surface. When dense

and thick, clouds appear grey or dark underneath. The presence and movement of clouds is often the

only clue that indicates a significant meteorological process occurring in the atmosphere. They

indicate the presence of moisture and some type of cooling mechanism.


SITU OBSERVATIONS

8

Cloud types may be classified on the basis of two characteristics: general form and altitude (Strahler,

1965). There are two major forms of clouds namely; stratiform or layered types and cumiliform.

Stratiform are further subdivided according to the level of elevation at which they lie. Clouds lie

within high (cirrus and its related forms), middle (alto-stratus and alto-cumulus) and low (stratus,

nimbostratus, and stratocumulus) levels. Vertically developed clouds mainly brought about by

thermal convection or frontal lifting consists of fair weather clouds and cumulonimbus type of clouds.

Figure 2-1 below shows different cloud types grouped into different families according to their

altitudes.

Figure 2-1: Cloud types grouped into different families according to height range and form (Source: Strahler,

1965)

Based on cloud altitudes, three major families are classified namely; family A (high level clouds),

family B (middle level clouds), and family C (low level clouds). From figure 2-1, the approximate

altitudes of these families are shown. In addition and of high importance in precipitation occurrence is

a fourth family type D, which includes clouds with vertical developments mainly due to convection.

Within these families and particularly family C and D, there exist the multi-layered clouds which are

those with higher depths which give precipitation hydrometers a better environment to develop and

grow. Some of the clouds which exist as multi-layered are nimbostratus and cumulonimbus.

Nimbostrati are considered multi-layered clouds because their vertical extent often goes well into the

middle cloud region. These clouds are dark, usually overcast, and are associated with large areas of

continuous precipitation. Cumulonimbuses on the hand are clouds that can produce lightning, thunder,

heavy rains, hail, and strong winds. They are the tallest of all clouds that can span all cloud layers.

They usually have large anvil-shaped tops which form due to strong winds at high levels of the

atmosphere.

Thus in the atmosphere the most relevant type of clouds in relation to contribution to precipitation are

the multi-layered clouds.


SITU OBSERVATIONS

9

2.2. Meteosat Second Generation (MSG) Satellite

Meteosat Second Generation (MSG) satellites provide vital data for meteorology and climatology at

frequent intervals and over wide areas. These series of satellites provides information for the entire

African continent at much higher spatial and temporal resolutions as compared to the earlier

meteorological satellite series. Coupled with these characteristics is the higher spectral resolution (12

bands/channels) of the SEVIRI instrument as provided in table 2-1.

Table 2-1: Spectral channel characteristics of SEVIRI in terms of central, minimum and maximum

wavelength of the channels and the main application areas of each channel (Source:

(EUMETSAT, 2006))

Band Spectral Characteristics of Spatial Main observational

No. Band spectral band Resolution application

(µm) (µm) (km, Sub-

satellite)

λcen λmin λmax

1 VIS0.6 0.635 0.56 0.71 3 Surface, clouds, wind fields

2 VIS0.8 0.81 0.74 0.88 3 Surface, clouds, wind fields

3 NIR1.6 1.64 1.50 1.78 3 Surface, Cloud phase

4 IR3.9 3.90 3.48 4.36 3 Surface, clouds, wind fields

5 WV6.2 6.25 5.35 7.15 3 Water vapor, high level

clouds, atmospheric instability

6 WV7.3 7.35 6.85 7.85 3 Water vapor, atmospheric

instability

7 IR8.7 8.70 8.30 9.10 3 Surface, clouds, atmospheric

instability

8 IR9.7 9.66 9.38 9.94 3 Ozone

9 IR10.8 10.80 9.80 11.80 3 Surface, clouds, wind fields,

atmospheric instability

10 IR12.0 12.00 11.00 13.00 3 Surface, clouds, atmospheric

instability

11 IR13.4 13.40 12.40 14.40 3 Cirrus cloud height,

atmospheric instability

12 HRV Broadband (≈0.4-1.1) 1 Surface, clouds

One of the key objectives of setting the MSG programme by EUMETSAT was the extraction of

meteorological and geophysical fields from satellite image data in support of general meteorological,

climatological and environmental activities. Of importance among the products of MSG satellite is

cloud information which as stated earlier gives information about the state of the atmosphere. In order

to study the behaviour and properties of clouds, they must first be identified from the satellite image.


SITU OBSERVATIONS

10

The following section briefly discusses a number of cloud extraction methods already developed by

individuals and institutions in an attempt to identify all cloudy pixels in various satellite(s) images.

2.3. Cloud Masking

Cloud detection from remote sensing data is required for many applications. Some of these are such as

determination of cloud cover, identification of cloudy pixels for the retrieval of cloud-related

parameters, or exclusion of pixels with even minor cloud contamination if further processing would

be affected by the presence of clouds (Schröder et al., 2002). Several methods can be used to perform

cloud detection. Some of these methods are such as multispectral thresholding techniques that can be

applied to individual pixels (Saunders and Kriebel, 1988) , (Derrien et al., 1993), (Stowe et al., 1999).

Dynamic cloud cluster analysis relying on histogram analysis was suggested by (Desbois et al., 1982)

whereas (Bankert, 1994) indicated use of artificial neural networks which needs manual training.

Another approach was suggested by (Ebert, 1987) which involve pattern recognition techniques based

on large scale texture analysis.

Figure 2-2 is a false colour composite (NIR01.6, VIS0.8, and VIS0.6 as Blue, Green, and Red

respectively) image of 25th December 2006 at 12:00 UTC for a small portion of Eastern Africa

continent, which indicates presence of various types of clouds and how they appear in MSG satellite

image.

Figure 2-2: MSG image false colour composite (BGR) of 25th December 2006 at 12:00 UTC

Kenya

Tanzania

Rwanda

Uganda

Burundi

Convection

Thin cirrus

Low level clouds


SITU OBSERVATIONS

11

Usually low level clouds such as stratus are difficult to identify on an infrared image but on a visible

bands colour enhancement (and false colour composite) they appear white to grayish. They appear

layered and as semi-transparent. Deep convective clouds on a false colour composite appear cyan in

colour and with sharp edges i.e. with distinct boundaries with the rest of nearby clouds. On the other

hand high level clouds such as thin cirrus appear cyan in colour and feather-like in pattern. These

features can be seen on figure 2-2 as indicated for each type of these clouds.

Therefore clouds in such an image could be masked for various studies, one of which is for rainfall

estimation. Methods as enumerated in the following sections were an attempt to detect and mask all

clouds in various satellite images including MSG.

Some of these methods proved to have some disadvantages. For instance, (Dlhopolsky and Feijt,

2001), indicated that the histogram analysis method is time consuming and in many cases cannot

make accurate threshold without human interaction. Various authors and institutions have developed

cloud mask algorithms for detection of clouds. The most relevant algorithms are explained in the

proceeding sections.

2.3.1. Météo-France (SAFNWC) Cloud Mask

Satellite Application Facility for supporting NoWCasting (SAFNWC), within Météo-France and run

by a consortium of institutions namely; Spanish Meteorological Institute (INM), Météo-France, the

Swedish Meteorological Institute, and the Austrian Meteorological Institute, is tasked to develop and

maintain a software package allowing the extraction from MSG/SEVIRI imagery a set of 12 products

useful for nowcasting purposes on any user defined area in the MSG (Derrien and Le Gléau, 2005). In

the software, cloud mask and cloud type software modules are implemented. The cloud mask

algorithm is based on multispectral threshold technique applied to each pixel of the image. Figure 2-3

below shows an example of cloud mask as developed by SAFNWC and accessed from EUMETSAT

through EUMETCast.

Figure 2-3: MSG cloud mask for 25th December 2006 at 12:00 UTC (EUMETSAT, 2006)


SITU OBSERVATIONS

12

The cloud mask algorithm developed here follow series of tests, the first being to identify pixels

contaminated by clouds or snow/ice and applied to land or sea which depend on the solar illumination

and on the viewing angles as defined in the table 2-2 with the tests presented in tables 2-3 and 2-4.

Most of the thresholds are determined from satellite-dependent look-up tables and NWP forecast

fields data and also from ancillary data (elevation and climatological data). Some thresholds are

computed at a spatial resolution defined by the user and others are empirical constants.

The second process is applied to all pixels, even already classified cloud-free or contaminated pixels,

to identify dust clouds and volcanic ash clouds. Spatial filtering is applied to reclassify isolated pixels

having a class type different from their neighbours.

Table 2-2: Definition of illumination conditions (SAFNWC); solar elevation is in degrees

Night-time Twilight Day-time Sunglint

Solar elevation<-3 -3<Solar elevation<10 10<Solar elevation Solar elevation>15

Table 2-3: Test sequence over land (SAFNWC)

Day-time Twilight Night-time

Snow detection Snow detection T10.8

T10.8 T10.8 T10.8-T12.0

R0.6 R0.6 T8.7-T10.8

T10.8-T12.0 T10.8-T12.0 T10.8-T8.7

T8.7-T10.8 T8.7-T10.8 T10.8-T3.9

T10.8-T3.9 T10.8-T8.7 T3.9-T10.8

T3.9-T10.8 T10.8-T3.9 Local spatial texture

Local spatial texture T3.9-T10.8 T8.7-T3.9

Local spatial texture

T8.7-T3.9

Table 2-4: Test sequence over sea (SAFNWC)

Day-time Sunglint Twilight Night-time

Ice detection Ice detection Ice detection SST

SST SST SST T10.8-T12.0

R0.8(R0.6) T10.8-T12.0 R(0.8)R0.6 T8.7-T10.8

R1.6 T8.7-T10.8 T10.8-T12.0 T10.8-T3.9

T10.8-T12.0 Local spatial texture T8.7-T10.8 T12.0-T3.9

T8.7-T10.8 R0.8(R0.6) T10.8-T8.7 T3.9-T10.8

T10.8-T3.9 T10.8-T3.9 T10.8-T3.9 Local spatial texture

T3.9-T10.8 Low clouds in sunglint T3.9-T10.8

Local spatial texture Local spatial texture

T8.7-T3.9


SITU OBSERVATIONS

13

Some of the notable tests are such as those using IR10.8 and IR12.0 (here in the table indicated as

T10.8 and T12.0 respectively) in which over the sea a pixel is flagged as cloudy when its estimated

sea surface temperature (SST) value is lower than a monthly climatological SST value by 4K. The sea

surface temperatures are estimated from T10.8 and T12.0 brightness temperatures using a non-linear

split window algorithm (Le Borgne et al., 2003). If this test is not applied, IR10.8 is used in which

case the threshold is determined from surface temperatures forecast by using Numerical Weather

Prediction (NWP) model. This test is applied over both the land and the sea surfaces. More

importantly is the fact that the threshold is derived from a global Pathfinder night-time bulk SST

climatology covering a period of 10 years and available at a 1/9th degree (approximately 12 km)

horizontal resolution.

Over land, IR8.7 together with IR10.8 is used in which the difference (IR10.8-IR8.7) should be

greater than 3.5+0.3/cos (θsat), during night-time or in case of low sun elevation e.g. at twilight, for

any pixel to be flagged cloud-contaminated. θsat is the satellite zenith angle. Usually, low clouds are

characterized at night-time by high IR10.8-IR3.9 brightness temperature differences, which allows

their identification over land (Derrien and Le Gléau, 2005). This detection may be less efficient at

large viewing angles hence the need to use a different channel (here IR8.7). An empirical test

(3.5+0.3/cos (θsat)) has been developed based on the observation that decrease of IR8.7- IR10.8 with

the satellite zenith angle is much stronger for low clouds than for vegetated areas. However, during

day-time the empirical test threshold is greater (-4.5-1.5*(1/cos (θsat)-1); where 1/cos (θsat) is the

secant of the satellite zenith angle, for any pixel to be flagged cloudy.

Most of the other test thresholds provided in tables 2-3 and 2-4 are computed from simulation of the

surface (ocean, land or snow) top of atmosphere reflectance (for the visible and near infrared bands)

by adding an offset and a correction factor. Top of atmosphere reflectance is simulated as:

( ))210 *1/* surfsurftoa RaRaaR −+= (2.1)

Where: a0, a1, and a2 are coefficients computed from satellite and solar angles, water vapour

and ozone content using look-up tables.

Rsurf is the land, ocean or snow surface reflectance.

Offsets of various percentages and correction factors are added to the above expression. However,

they are not described further in the literature. Hence this makes this method difficult to apply.

The dynamic thresholds applied to thermal bands differences are obtained by interpolation into look-

up tables using the satellite zenith angles and the NWP forecast using radiative transfer models. It is

no doubt that the tests are many and therefore require special software to handle. In addition, many

African National Meteorological and Hydrological Services (NMHSs) have no capability to handle

numerical weather prediction (NWP) model forecasts using radiative transfer models.

2.3.2. Météo-France (Ocean and Sea Ice SAF) Cloud Mask

The developments of the Ocean and Sea Ice Satellite Application Facility (O&SI-SAF) Sea surface

temperature algorithms of Météo-France for the determination of Atlantic sea surface temperature

require cloud masking. The SST products have three components namely; GOES-East, MSG and


SITU OBSERVATIONS

14

NOAA-HL derived SSTs (Météo-France, 2005a). In all these components cloud mask algorithm

applied is the same and is based on multispectral thresholding technique. Specific adaptation to

marine conditions was introduced in the development of the algorithm. These conditions mainly

consider temporal stability of SST and climatology. An example of combination of cloud-free GOES-

East and MSG satellites SST product in MSG georeference for 25th December 2006 at 13:00 UTC is

provided in figure 2-4 below.

Figure 2-4: GOES-East and MSG satellites SST products (in ˚C) for 25th Dec 2006 at 13:00 UTC

Temporal stability of SST was suggested by (Wu et al., 1999) and is applied in Ocean and Sea Ice

SAF under the following form: for a clear sky pixel, channel 11µm (IR10.8) temperatures at time H

(T11H) are compared to the maximum value of the corresponding temperatures at time H-30 minutes

(T11H1) and time H+30 minutes (T11H2). If T11H-Max (T11H1, T11H2) <Threshold, the pixel is

considered as cloudy (Météo-France, 2005a). Threshold set here as suggested by (Wu et al., 1999) is -

0.5K. The process tries to address change of temperature over a pixel. A negative change implies

setting in of cloud in that pixel which is in most cases colder that the pixel temperature within a time

range (1 hour). This process implies that all pixels’ real time state will not be determined since it

requires the future (next 30 minutes) state. Furthermore, the comparison process may take some time.

In considering climatology, the climatologic minimum temperature at any time of the year in question

is compared with calculated SST value. Too low SST is indicative of cloud contamination and too low

threshold depends on the distance of the considered pixel to the pre-calculated cloud mask and the

location of the pixel with respect to the coast. Here it is assumed that near a cloud, too cold

temperatures are more suspect and the control of the calculated SST against climatology should be

more severe. The scheme works as follows:

If Tsmin-Ts > ∆t, the pixel is considered as cloudy

where: Tsmin is the climatologic minimum temperature

Ts is the calculated SST


SITU OBSERVATIONS

15

∆t is the threshold

SST is calculated from a non-linear algorithm in the form of expression given below:

CorrSCCTTTBSBBTSAAT guessS +++−++++= 1021010 )0.128.10)((8.10)( (2.2)

where: A0, A1, B0, B1, B2, C0, and C1 are constants for which in case of MSG are: 0.98826, 0, 0,

1.18116, 0.07293, 1.10718, and 0 respectively

S = sec(θ)-1, with θ: satellite zenith angle

Tguess is the climatological SST

Corr is correction factor and is 0.2 for MSG

Climatologic minimum temperature, Tsmin is derived from the Pathfinder archive (AVHRR data from

1985 to 1995). The climatology has been made on a decadal (10 day) basis and includes minimum and

mean values re-mapped over the MSG disk at same resolution as the thermal infrared bands in MSG.

Various thresholds used depend on the position of the pixel with respect to the coast hence it applies

only over the sea. The method appears less complicated as compared to the previous one and therefore

easy for implementation.

2.3.3. Meteosat VIS-IR and NOAA-A/TOVS Image fusion Cloud Mask

(Casanova et al., 2005) developed an automatic method of cloud classification for direct application

in civil aviation. Here visible and infrared channels of Meteosat satellite were used alongside data

provided by the A/TOVS (Advanced/Tiros-N Operational Vertical Sounder) onboard NOAA polar

satellites. Different spectral techniques were used for different purposes. In their study, an automatic

method of cloud classification which provided, in real time, the cloud cover over civil airports on the

Iberian Peninsula was developed emphasizing on rain clouds.

The method consisted of a series of algorithms based on the physical properties of cloud surfaces and

thermodynamic state of the atmosphere. TIP-data included in the telemetry of the high resolution

picture transmission (HRPT) satellites were taken and processed through International TOVS

Processing Package (ITPP) or International ATOVS Processing Package (IAPP) software, depending

on whether the datum type was TOVS or A/TOVS, in order to transform the high-resolution

interferometer sounder (HIRS) and advanced/microwave sound unit (A/MSU) sensors’ radiances into

atmospheric data.

In this method, albedo classification was performed which revealed that most clouds were good

reflectors since it depends first on their thickness and to some extent on the nature of the cloud

particles. To avoid sunglint contamination, illumination geometric conditions reflectance thresholds

were set. From here reflectivity image was obtained which was further reclassified according to

various categories based on surface type. Threshold tests were performed by comparing historic data

of mean temperatures at ground level to the calculated brightness temperature values. This allowed

detection of non-cloudy pixels which were to be removed before further processing in order to obtain

the linear relationship between height and cloud top temperature. This was done by applying the

temperature value to the equation obtained through the geopotential and temperature images provided

by the A/TOVS data calculated at different pressure levels.


SITU OBSERVATIONS

16

This method can be seen to use thermodynamic sounding of the atmosphere which in many cases is

not available at many meteorological weather stations. Many African National Hydrological Services

are also not able to handle the atmospheric sounding due to the cost involved.

2.3.4. KNMI Cloud Mask Algorithm

The Royal Netherlands Meteorological Institute (KNMI) has developed an algorithm for cloud

detection and characterization called MetClock (METeosat CLOud Characterisation KNMI).

According to (Valk et al., 1998), MetClock algorithm comprises two threshold tests to perform cloud

detection on the basis of Meteosat IR data, the relative and absolute infrared tests. A cloudy pixel is

determined by comparing Meteosat apparent brightness temperatures with the earth surface

temperatures. The relative infrared test uses images of different observation times to compare changes

in temperature at the earth surface with changes in temperature of a pixel. A pixel is classified as

cloudy when the change in pixel temperature exceeds the change of the earth surface temperature with

a certain threshold.

On the other hand absolute infrared test uses a single image to directly compare pixel temperatures

with the earth surface temperatures. Here again a pixel is classified as cloudy when it exceeds a

certain threshold. From these two tests it is clear that the results depend strongly on the accuracy of

the surface temperature maps. The surface temperatures are provided by Numerical Weather

Prediction (NWP) model, the High Resolution Limited Area Model (HIRLAM).

The relative test group consists of five tests, thresholding on the IR imagery and VIS imagery between

various time difference including previous imageries and also future imageries either in hours or days.

This is the shortcoming of this particular cloud classification method since it takes into account

forecast environment which may not be always correct. Besides, surface temperature over complex

terrain and high mountains may not be accurate and therefore the method may not be very much

applicable in such areas. The algorithm was developed for the first series of Meteosat satellites

(Meteosat 1-7) and can be as well applied to MSG satellites.

2.3.5. KLAROS Cloud Mask Algorithm

In their study (Dlhopolsky and Feijt, 2001) developed KNMI Local implementation of APOLLO

retrievals in an Operational System (KLAROS) algorithm for processing MSG data. AVHRR data

was used as prototype data set with which to produce cloud products expected to be derived with

MSG.

KLAROS is more or less the same as MetClock. However this method was aimed at improving cloud

detection by using a radiative transfer model to help link radiances from the different wavelengths and

produce physically meaningful variables. The method was designed to work with use of thresholds

defined in databases. The temperature database is derived from the HIRLAM NWP model while

reflectivity database was created from two years of NOAA AVHRR data clear skies and verified with

synoptic observations.


SITU OBSERVATIONS

17

It is further stated that KLAROS consists of a set of programs for cloud detection and cloud property

retrieval. The two modes of operation are one in C tool which does the data processing and the other

is user interface written with the Interactive Data Language (IDL).

2.3.6. APOLLO Cloud Mask

AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO) was designed to make use of

all five spectral channels during day-time and to discretize all AVHRR data into four different groups

called cloud-free, fully cloudy, partially cloudy, and snow/ice, before deriving physical properties

(Saunders and Kriebel, 1988). Within APOLLO, clouds are discretized into three layers according to

their top temperature (Kriebel et al., 2003). The layer boundaries are set to 700hPa and 400hPa and

the associated temperatures are derived from standard atmospheres.

Each cloudy pixel is checked to see whether it is thick or thin cloud, depending on its channel 4 and 5

temperatures and, during day-time, its channel 1 and 2 reflectances. Thin clouds are taken as ice

clouds, i.e. cirrus, whereas thick clouds are treated as water clouds. APOLLO is designed to process

AVHRR HRPT (High Resolution Picture Transmission) data as well as Local Area Coverage (LAC)

and Global Area Coverage (GAC) data. Those pixels in which the solar elevation is more than 5°

above the horizon are processed by means of the day-time algorithm whereas all others are processed

by the night-time algorithm.

APOLLO uses five threshold tests applied to each pixel and this allows establishing the group of

cloud-free and contaminated pixels. These tests are based on AVHRR channels 1, 2, 4 and 5 and rely

on simple physical principles. Every pixel which is brighter than a threshold in the solar channels or

colder than another threshold in the thermal channels is called cloudy. The use of physical parameters

and self-adjusting thresholds minimizes the influence of differences between the instruments aboard

different satellites. However, since AVHRR is polar-orbiting it is not to establish meaningful time

series of cloud products. Thus the thresholds needs to be carefully selected in order to establish

whether a pixel is cloudy or is partially cloudy or is cloud-free.

2.3.7. GHCC Cloud Mask

The Global Hydrology and Climate Centre (GHCC) in Huntsville, Albama receives GOES-East and

West satellite data in real-time from their ground stations and produces a number of products from the

Imager and Sounder in support of research and operational activities. Cloud detection method used

here is bi-spectral spatial coherence (BSC) which uses two spatial tests and one spectral threshold to

identify clouds in the GOES Imager or Sounder imagery (Jedlovec and Laws, 2003). The performance

of the BSC method is adequate during the day; however it performs poorly near sunrise/sunset and at

night.

Bi-spectral Threshold and Height (BTH) which is built on BSC uses spatially and temporally varying

thresholds. This method also provides cloud top pressure information with the cloud mask. The

underlying principle in this cloud detection method with GOES imagery is that the emissivity

difference of clouds at 10.7µm and 3.9µm varies from that of the surface (land or ocean) and can be

detected from channel brightness temperature differences. (Jedlovec and Laws, 2003) noted that while


SITU OBSERVATIONS

18

emissivity of clouds at 3.9µm is considerably less than at 10.7µm, reflected solar radiation at 3.9µm

makes effective brightness temperatures (sum of emission and reflective components) quite large.

The key to cloud detection in the BTH technique is the use of multispectral channel differences to

contrast clear and cloudy regions. The 10.7µm and 3.9µm channels on the Imager and similar

channels on the Sounder are used to produce an hourly difference image (longwave minus shortwave)

for this purpose (Jedlovec and Laws, 2003). Two composite images are created for each hour which

represent the smallest negative and smallest positive difference image values (values closest to zero)

from the preceding 20 day period (for each time). These composite images serve to provide spatially

and temporally varying thresholds for the BTH method. An additional 20 day composite image is

generated for each hour using the warmest longwave (10.7µm) brightness temperature for each

location from the 20 day period. This composite image is assumed to represent a warm cloud-free

thermal image for each time period.

BTH method uses the images generated as enumerated above in a four step cloud detection procedure.

These include testing adjacent pixels, one-dimensional spatial variability (which fills-in between the

cloud edges), minimum difference (which compares the current difference image value to the

composite images), and Infrared threshold (which uses an hourly 20-day composite of the warmest

10.7µm channel values at each pixel location.

It is no doubt that this method requires high memory space for storing the images generated. It also

implies that a lot of iterations have to be done every time a comparison and composite images are to

be generated.

2.3.8. AFWA Cloud Mask Algorithm

Kidder et al., (2005) developed various Meteosat Second Generation (MSG-1) cloud mask algorithms

for implementation at the United States Air Force Weather Agency (AFWA). These algorithms are

named; cloud mask, nocturnal cloud mask, daytime cirrus, nocturnal thin cirrus, precipitating clouds,

and multi-channel skin temperature.

Cloud mask algorithm uses difference-from-background technique by constructing 10-day infrared

background for each hour of the day. The process assumes that in 10 days each pixel is observed to be

cloud-free at least once. This method exploits the tendency of clouds being colder than the underlying

surface. Pixels whose radiance is less than the background radiance by more than a threshold value

are flagged as cloudy. Here 8.7µm channel is used in constructing the 10 days background image.

Nocturnal cloud mask test uses the brightness temperature at 10.8µm and the albedo at 3.9µm to

detect ice clouds, liquid water clouds, and clear scenes. 8.7µm channel data are used to screen desert

pixels. Various empirical thresholds are used in the test. Albedo and brightness temperature

background database contains the 3.9µm albedo and 10.8µm brightness temperature data observed

over MSG-1 pixel each day for the previous 10-day period.

Day-time cirrus test for MSG-1 data utilizes three reflective channels, 0.6µm, 0.8µm, and 1.6µm. The

measured radiances are converted to albedos (0 to 1) by dividing the radiances by the solar irradiance


SITU OBSERVATIONS

19

and by the cosine of the solar zenith angle, then multiplying by phi. This test utilizes the fact that

liquid water clouds are highly reflective at all three wavelengths and thus will appear white. Ice

clouds (and snow on the ground) are highly reflective at 0.6µm and 0.8µm, but poorly reflective at

1.6µm and therefore they appear cyan in colour. When the albedos are represented in the RGB colour

cube, cirrus is having a cyan colour due to ice particles.

Nocturnal thin cirrus test uses albedo at 3.9µm, which is calculated from measured radiances at 3.9µm

and 10.8µm (Kidder et al., 2005). Radiation from below the cloud leaks through thin cirrus, which

results in a negative albedo. They indicated that nothing else results in a negative 3.9µm albedo, hence

this is a very sensitive test for thin cirrus at night.

Precipitating clouds test uses the brightness temperature difference between the 6.2µm water vapour

channel and the 10.8µm window infrared channel to detect high, thick clouds, which are likely to be

precipitating. As clouds are usually formed within troposphere, the maximum water vapour content is

also expected within this level. Casonova et al., (2005) indicated that in an adiabatic atmosphere, the

relation between the height and temperature within troposphere is linear. This linear relation can be

obtained by use of A/TOVS data for pressure levels between 1000 hpa (approximately sea level) and

300 hpa (approximately tropopause). It is for this reason that WV06.2 is appropriate to use with

IR10.8 in order to extract the most probable raining clouds.

Kidder et al., (2005) indicated that at 6.2µm the atmosphere is opaque due to water vapour absorption

and that low clouds are not sensed at 6.2µm. Only deep clouds penetrate the water vapour to be

sensed at both 6.2µm and 10.8µm, and when this happens then the brightness temperature difference

at these two wavelengths becomes small (Kidder et al., 2005). Empirically determined threshold

temperature difference of 11K is used to flag out cloudy pixels, (i.e. if WV06.2-IR10.8 < 11K, then

the pixel is rain cloud).

Multi-channel skin temperature procedure employs two channels (10.8µm and 12.0µm). Both 10.8µm

and 12.0µm radiances are affected by water vapour in the column between the surface and the

satellite, but 10.8µm is less affected than 12.0µm. The difference between the brightness temperatures

at 10.8µm and 12.0µm can be used to correct the 10.8µm brightness temperature for water vapour

absorption to yield an estimate of the skin temperature, that is, the brightness temperature which

would be observed if there was no water vapour in the atmosphere (Jedlovec and Laws, 2003). The

temperature retrieved here is that of the surface only and not the air temperature. However, the

method applies to clear-sky pixels only.

Most of the above cloud mask methods have not revealed the actual threshold values used. However,

under the strength of the arguments in the theories used in these methods to develop the mask

algorithm, a simple cloud mask (SCM) could be attempted which could further be used in other

applications such as rainfall estimation, weather forecasting, water and energy balance studies, among

others.


SITU OBSERVATIONS

20

2.4. Precipitation Processes

Precipitation is water in some form, falling out of the air, and settling on the surface of the earth.

Precipitation occurs due to condensation in the atmosphere and not condensation that occurs at the

surface such as dew.

The major two models of precipitation formation are: collision-coalescence and ice crystal models.

An important distinction between the two processes is the temperature of the cloud. Warm clouds are

the ones whose mass lies above freezing level while cold clouds primarily exist where the temperature

is below freezing level. The collision-coalescence model applies to warm clouds that form in the

tropics whereas ice crystal model applies to the process of precipitation in the mid and high latitudes.

For precipitation to form under collision-coalescence model there needs to be a variety of different

size condensation nuclei. Large condensation nuclei will create large water droplets while smaller

condensation nuclei create small ones. In the ice crystal model, cloud water exists in liquid form even

though the temperatures are cold enough to freeze water. Water has a temperature below freezing but

still in liquid state i.e. super-cooled water.

The following section discusses various methods of estimating rainfall (one of the major form of

precipitation) from space by use of satellites. Some of these methods attempt to address rain

formation processes as explained in this section.

2.5. Satellite Rainfall Estimation

The measurement of the surface precipitation is very important to studies of the hydrological cycle,

water management planning, flash flood identification, input to hydrological and agricultural models,

verification of weather modification experiments and the study of convective systems (Kamarianakis

et al., 2006). Rainfall affects lives and economies of a majority of the earth’s population. Heavy rain

systems are crucial to sustaining the livelihood of many countries. Excess rainfall can cause floods,

landslides and loss of property.

Rainfall is among the atmospheric parameters, one of the most difficult to measure because of its high

temporal and spatial variability and discontinuity. Moreover the coverage of precipitation

measurements by ground conventional means (rain gauge networks or weather radars) is much less

than adequate especially in the African continent.

With the advent of meteorological satellites, improved identification and quantification of

precipitation at time scales consistent with the nature and development of cloud has been realised

(Levizzani et al., 2002). Meteorological satellites expand the coverage and time span of conventional

ground-based rainfall data for a number of applications, above all hydrology and weather forecasting.

The primary scope of satellite rainfall monitoring is to provide information on rainfall occurrence,

amount and distribution over the globe for meteorology at all scales, climatology, hydrology, and

environmental sciences (Levizzani et al., 2002). The uneven distribution of rain gauges and weather

radars over most part in the world has called for this new technology of satellite rainfall measurement.


SITU OBSERVATIONS

21

It is also a well know fact that precipitation is one of the most variable quantity in both space and time

especially within the tropics.

Geostationary weather satellite visible (VIS) and infrared (IR) imagers provide the rapid temporal

update cycle needed to capture the growth and decay of precipitating clouds. The swath widths of

satellites in tropical orbit such as the Tropical Rainfall Measuring Mission (TRMM) and of sensors in

polar orbits like the Special Sensor Microwave Imager (SSM/I) series leave substantial gaps all over

the globe (Levizzani et al., 2002). The quantitative rainfall determination from a variety of

precipitating systems differ both dynamically and microphysically and this prompts for non-unique

solutions based on physics of precipitation formation processes.

Based on a number of earlier studies, (Levizzani et al., 2002) reviewed several satellite-based rainfall

estimation methods. In this section a few of these satellite-based rainfall estimation methods that use

thermal infrared are briefly explained. They revealed the four main categories of cloud classification

as: cloud-indexing, bi-spectral, life-history, and cloud model-based. Each of these categories stresses a

particular aspect of sensing cloud physics properties using satellite imagery and in final rainfall

estimation.

2.5.1. Cloud-Indexing Methods

According to (Kidder and Haar, 1995), cloud-indexing is the oldest precipitation estimation technique

which assumes that it is fairly easy to identify cloud types in satellite imagery. This method assigns a

rain rate to each cloud type. The rain at a particular location or in a particular area can be written as:

i

i

i frR ∑= , (2.2)

Where ri is the rain rate assigned to cloud type i, and fi is the fraction of time that the point is covered

with (or fraction of the area covered by) cloud type i.

This method lacks the validity of assigning a constant rain rate to a particular cloud type. Depending

on the cloud formation processes, rain rate may vary significantly even for a particular type of cloud.

2.5.2. Bi-spectral Methods

Bi-spectral methods are based on the very simple, although not always true, relationship between cold

and bright clouds and high probability of precipitation, which is characteristic of cumulonimbus

(Levizzani et al., 2002). Lower probabilities are associated to cold but dull clouds (thin cirrus) or

bright but warm (stratus). Generally cirrus clouds are cold but do not produce as much precipitation as

some warmer clouds.

Techniques in this category are based on cloud classification and using either radar derived rainfall or

good network of ground stations as training data. From here rainfall estimation over a given array of

pixels can be derived.

The underlying assumption here is that all rain bearing clouds are successfully classified, which is not

always the case.


SITU OBSERVATIONS

22

2.5.3. Life-history Methods

These are the methods mainly in the family of techniques that specifically require geostationary

satellites images. They rely upon a detailed analysis of the clouds life cycle, which is particularly

relevant for convective clouds (Levizzani et al., 2002).

One of the most famous techniques which use this method is Tropical Applications of Meteorology

using SATellite (TAMSAT) of the Reading University, United Kingdom. The assumption inherent in

the TAMSAT procedure is that the relationship between the quantity of rainfall and the cold cloud

duration (CCD) is linear, provided there is adequate averaging of data either in space and time. The

averaging procedure reduces the false alarms that CCD may cause especially over a short time period

or over a very small area which is associated with convective clouds.

(Dugdale and Milford, 1985) developed the concept of cold cloud duration (CCD), using the thermal

channel of Meteosat, to generate time series of cloud temperature for tropical altitudes, where most

rainfall comes from convective activities. They suggested that the duration above a certain threshold

temperature value is representative of the amount of rain that is generated.

However, according to (Grimes et al., 1999) the basic assumptions in this method are:

1) Rainfall is predominantly convective in origin and that the raining clouds can be

identified as those with cloud top temperatures below a threshold temperature (T),

2) The number of hours for which a given pixel is colder than T (the CCD) is linearly

related to the rainfall over the same time period, that is:

eDaaRs ++= 10 (2.3)

where: Rs is the rainfall over the pixel,

D is the CCD over the pixel,

e is the error with zero mean, E[e] =0, and homogeneous variance

Var[e]=ε2

3) The threshold temperature, T and the parameters a0, a1, can be estimated for a given

region and a given time of year by the analysis of historic data for that region and time of

year, that is:

DaaRs 10′+′=′ (2.4)

where: Rs, a0, and a1 are estimated values.

Rs, a0, and a1 are calculated for each month for a number of empirically determined calibration zones.

This method has proved to be successful in tropical regions especially for convective clouds occurring

in the region of Inter-tropical Convergence Zone (ITCZ) in which case the first two assumptions are

reasonable. However, due to inter-annual variability in rainfall-CCD relationship, there could be over-

or underestimation of rainfall for a particular month locality if fixed calibration is applied.


SITU OBSERVATIONS

23

2.5.4. Cloud Model-based Techniques

Cloud model-based techniques aim at introducing the cloud physics into the retrieval process for

quantitative improvement deriving from the overall better physical description of the rain formation

processes (Levizzani et al., 2002). (Adler and Mack, 1984) derived a one-dimensional cloud model by

first identifying locations of convective cells and then assigning rain parameters. Associated anvil

stratiform rainfall area is identified by a threshold brightness temperature, the value of which is

calculated from the satellite data. The model calculates maximum rain rate and maximum volume rain

rate from a sequence of model runs as a function of maximum cloud height or minimum cloud model

temperature. The use of the one-dimensional cloud model to account for ambient temperature,

moisture and shear conditions provides a stronger physical (less empirical) basis for the cloud height -

rain relationships.

(Adler and Negri, 1988) utilized data from Geostationary Operational Environmental Satellite

(GOES) infrared (10.5-12.6µm) channel, in 30 minutes, for an area in the southern Florida for a one-

dimensional cloud model relating cloud top temperature to rain rate and rain area in the Convective

Stratiform Technique (CST). The method here followed a few steps, first of which is to identify the

candidate thunderstorm or regions of enhanced convection by searching for the minimum in the

GOES temperature Tb array.

The second step involves eliminating local minima temperature that represent thin, non-precipitating

cirrus by calculating slope parameter for each minimum temperature Tmin as:

min61 TTS −= −

−

(2.2)

where, 61−

−

T is the average temperature of the six closest pixels. If the Tmin is located at (i,j),

6/)( 1,1,,2,1,1,261 −+++−−−

−

+++++= jijijijijiji TTTTTTT (2.3)

Note that due to pixel offset along the scan line which is approximately half as large as that across the

scan, 6 pixels (highlighted in figure 2-5) are taken as the closest pixels to the one under consideration.

i , j

Figure 2-5: An example of pixel array under consideration with Tmin at (i,j)

After this step empirical discrimination of thin cirrus, from active convection, in the

temperature/slope plane using radar and visible imagery data is derived (Adler and Negri, 1988). Once

this is done, correction for the field of view between the GOES field of 8km and that of the cloud

model (approximately 1km in horizontal dimension) as used by (Adler and Mack, 1984). Rain

parameters are then assigned to the feature based on one-dimensional cloud model and thereafter a

threshold temperature is used to identify the anvil stratiform region. This threshold is expected to


SITU OBSERVATIONS

24

coincide with the relatively thick portion of mature anvils hence modal Tb in the frequency

distribution of anvil Tb is used as anvil background temperature.

The above four methods/techniques have been widely applied in satellite rainfall estimation with

different scientists endeavouring to optimally estimate rainfall over different space and time ranges.

The following section briefly explains the blending techniques used to estimate rainfall over a given

area.

2.5.5. Blending Techniques

With the advent of passive microwave measurements, several VIS/IR techniques have been re-

examined and integration sought that could help adjusting some of the well known problems of the

top–down approach of these methods, which generally derive precipitation only from cloud top

information (Levizzani et al., 2002). These methods have suggested combining the observations

delivered by satellite instruments of different type to improve averaged rainfall estimation by using

multi-source data. Combining IR data from geosynchronous satellites attempt to take advantage of

both IR and MW techniques. They benefit from the fact that there is excellent time and space

coverage of IR images and from the direct connection of the MW observations with precipitation.

However, these techniques require some precautions as (Turk et al., 2003) indicated that raining and

idealized non-raining conditions as observed by low orbiting earth may cause discrepancies as a result

of viewing geometries (see figure 2-6). This depends upon the three-dimensional structure of the

cloud and the azimuthal direction that the sensor views it. Moreover, the timing and foot print offsets

usually cause significant difference between geostationary satellites and polar orbiting satellites.

Figure 2-6: Some factors influencing the differences between space- and time-collocated TMI and SSMI

observations under idealized precipitating cloud conditions (Source: (Turk et al., 2003))

These methods often use statistical integration of the satellite IR and MW data. The choice of which

variables to match in order to provide the final product may rest on one part the extent of accuracy

required and on another the processing time. (Marzano et al., 2005) indicated a possibility of direct

combination of microwave brightness temperatures and thermal infrared radiances, in which the


SITU OBSERVATIONS

25

advantage is the exploitation of the observable information without any post-processing and the

disadvantage of space-time collocation matching.

Maathuis et al., (2006) applied blending technique using TRMM derived rainfall intensity to calibrate

MSG image in which a relation between thermal infrared observations and the passive microwave

observations was derived. In order to obtain good correlation between these two variables, as well as

to solve collocation problems, they used equal temperature classes to average rainfall intensities. The

regression function obtained here was applied to MSG images for regionalization of rainfall intensity

over eastern part of Africa.

Statistical methods in these techniques are significant and are applied to empirically-trained retrieval

algorithms in order to estimate rainfall to a reasonable accuracy. However, the best approach would

be based on physically-based retrieval algorithms which, on the other hand, would need a

climatological and microphysical tuning. This again would resort to approaches whose aim would be

cumulative estimates but not instantaneous estimates.

Besides, most of the above methods consider rainfall events and not a specific type of cloud. Different

clouds and different stages of development (e.g. for convective clouds) may have different cloud

height- rainfall intensity/amount relationship. This calls for an approach that may address the problem

of comparing rainfall and cloud height of different clouds that are at different stages of their

development.

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks

(PERSIAN) Cloud Classification System (CCS) as described by (Hong et al., 2004a) addressed the

problem of comparing different type of clouds by extracting cloud features from infrared

geostationary satellite imagery. The PERSIAN algorithm fits the pixel brightness temperature and its

neighbour temperature textures, in terms of means and standard deviations, to the calculated pixel rain

rates based on an Artificial Neural Networks (ANN). The general approach in the algorithm is as

provided in figure 2-7 below.

Figure 2-7: The PERSIAN CCS model structure (source:(Hong et al., 2004a))


SITU OBSERVATIONS

26

Firstly images are pre-processed through cloud segmentation procedure as in (a). Several inputs of

feature extraction of the cloud patches are applied as in (b). Once cloud patches are identified cloud

classification follows clustering them accordingly as in (c). The final step is to develop non-linear

temperature and rainfall fitting for all classified cloud clusters as in (c). The parameters of the

temperature – rainfall curves are calibrated based on rain estimates from sources such as Radar

networks over a region under study.

This method offers probably the best idea in comparing cloud properties and rainfall intensity or

rainfall amount. However, the enormous data requirement in this algorithm, which is unavailable in

many regions, renders it inapplicable in many regions. Consequently a simple satellite rainfall

estimation method is vital for regions with low passive microwave datasets. Despite low reliability

results that may be obtained in using few satellite cloud data, rainfall estimation can be derived as first

approximation for users such as hydrologists or general water management resource organisations.

2.5.5.1. EUMETSAT Multi-sensor Precipitation Estimate (MPE)

The EUMETSAT multi-sensor precipitation estimate (MPE) has been developed in order to derive

instantaneous rainfall intensities from MSG. The method is based on the blending of brightness

temperatures of the MSG infrared channels with rainfall intensities from Special Sensor

Microwave/Imager (SSM/I) on the United States Defence Meteorological Satellite Program (DMSP)

satellites (Heinemann, 2003). The basic assumption of the Multi-sensor Precipitation Estimate (MPE)

method is that colder clouds are more likely to produce precipitation than warmer clouds. Here

Heinemann, (2003) pointed out that the relationship between the cloud top temperature and the

surface rainfall intensity is non-linear and that it depends strongly on the current weather situation.

Temporally and spatially co-registered SSM/I and MSG measurements are used to derive look-up

tables which describe rainfall intensities as a function of the MSG infrared brightness temperature.

The look-up tables are applied to MSG images in order to derive rainfall intensities in full spatial and

temporal resolution. This method is indicated to efficiently estimate the spatial distribution and

strength of convective precipitation over not only large scale tropical convection but also small scale

convective processes and cold fronts. It is however not suitable for estimating precipitation from

warm fronts and also orographically induced precipitation which is usually detected but miss-located

to great distances, sometimes upto 100km (Heinemann, 2003).

These products are available in http://oiswww.eumetsat.org/SDDI/html/product_description.html

(EUMETSAT, 2007) and can be downloaded in GRIB2 format. MPE products can be imported into

any geographic information systems (GIS) packages such as ILWIS by using windows based GRIB2

import package which can be obtained from

ftp://ftp.cpc.ncep.noaa.gov/wd51we/wgrib2/Windows_XP/ (NOAA-NWS-CPC, 2005). The respective

rainfall intensities can then be viewed in ILWIS. GRIB viewer from SatSignal software and available

in http://www.david-taylor.pwp.blueyonder.co.uk/software/grib-viewer.htm (David, 2006) can as well

be used to view the amounts at a desired location.

The following chapter outlines materials and methods used in this study with detailed information on

data acquisition required for cloud masking and also for rainfall comparison as well as rainfall

estimation.


SITU OBSERVATIONS

27

3. Materials and Methods

3.1. Data Acquisition

The study attempted to use various data whose source was either straightforward to obtain or needed

pre-processing. The general approach in developing the cloud mask is as given in figure 3-1. The

proceeding sections explain how each of the retrieval and computation processes was done with

ILWIS scripts in Appendix A.

Figure 3-1: Flow chart for simple cloud mask (SCM) and cloud height/type (SCH/T) retrieval

3.1.1. MSG Satellite Data

The Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard MSG satellite provides

data to EUMETSAT at Darmstadt (Germany) which is processed and then uplinked to HOTBIRD-6 in

wavelet compressed format (Gieske et al., 2004). The images are received and archived at ITC in

compressed form on external drives which are linked to the ITC network and hence accessed through

ordinary personal computers. The image geocoding and radiometric calibration coefficients are

supplied in so called EPI and PRO files. The data is not atmospherically corrected. Therefore direct

ground observation(s) can only be related to the satellite observation(s) (at the required resolution)

after atmospheric correction of the images. In this study this step is not necessary since the focus is on

Not ok Ok

Dew point temp

calculation and set

temp threshold(s)

Merge SST and LST

Time series (K)

Perform Cloud

Mask

Retrieve data

from MSG

online archive

Retrieve land

Surface

Temperature

(LST) (°C)

Retrieve Sea

Surface

Temperature

(SST) (°C)

Classify into different

height classes

TOA temperature

(IR_039, IR_108,

IR_120)

VIS Range

(VIS006, VIS008,

VIS016) – Day-

time only

Cloud mask

algorithm

Visualize the

cloud mask on

Colour

composite


SITU OBSERVATIONS

28

clouds which are the main atmospheric parameters aimed at removing from satellite images for direct

ground observation(s) relations.

The retrieval of MSG data is straightforward using import utilities developed. External batch files

were created using the MSG data Retriever software available at ITC. For more details about the

software refer to (Maathuis et al., 2005). Figure 3-2 shows MSG data Retriever window with the

command line indicating all parameters that are to be retrieved from MSG image.

Figure 3-2: MSG Data Retriever window (Courtesy of ITC)

The executing commands are saved as a text file and therefore any time a different image is required

the changes are only done using a text editor and saved as a batch file in ILWIS software. These batch

files are provided as Appendix B. The software has been used intensively in this study as the MSG

geometric model is implemented. In this study, only bands 4, 9, and 10 (3.90µm, 10.8µm, and 12.0µm

respectively) were used in the cloud masking process. Visible bands 1, 2, and 3 (0.06µm, 0.08µm, and

1.60µm respectively) were only for visualization in order to ensure optimal (visual) cloud mask

validation especially during the day.

Examples of raw images of band 9 (10.8µm) and visible bands 1, 2, and 3 (0.06µm, 0.08µm, and

1.60µm respectively), with the visible bands viewed as false colour composite, are provided in figure

3-3.


SITU OBSERVATIONS

29

Figure 3-3: False colour composite (bands 1, 2, and 3 –in BGR) (left) and Band 9 (10.8µm) (right) on

19/12/2006 at 12:00 UTC

On the left figure cyan areas are cloud patches and dark areas are water bodies. Right figure with

pseudo colour representation shows colder areas in deep blue to green while warmer areas appear

orange to red in colour.

3.1.2. Climatological Data

Climatological data required in this study were minimum, maximum, and mean land surface

temperature as well as sea surface temperature. Minimum, maximum, and mean land surface

temperature were obtained from ‘WorldClim’ database available for download from

http://www.worldclim.org (Hijmans et al., 2005b). This dataset contains global climate grids with a

spatial resolution of a square kilometre and can be used for mapping and spatial modelling in GIS

(Hijmans et al., 2005a). Sea surface temperatures (SST) were derived from climatological data using

the NOAA National Oceanographic Data Centre (NODC) and the University of Miami Rosenstiel

School of Marine and Atmospheric Science (RSMAS) AVHRR Version 5.0 Pathfinder SST dataset

available at ftp://data.nodc.noaa.gov/pub/data.nodc/pathfinder/Version5.0_Climatologies/ (NOAA-

NODC, 2006) for the period 1985 to 2001. This averaged data was already resolved to 4km and in 5-

day, 7-day, 8-day, monthly, seasonal, and annual periods and each period provided daytime-only,

night-time-only, and day-night combined.

Here day-night combined monthly mean sea surface temperature HDF file dataset was imported to

ERDAS and then into ILWIS. The dataset provided needs to be rescaled and transformed to represent

SST in degree Kelvin. The scale and offset provided are 0.075 and -3°K respectively, so that the

expression for calculation of SST appears as given in equation 3.1.

15.273)3075.0*( +−= origK SSTSST (3.1)

where: SSTK and SSTorig are corrected SST (in °K) and original SST (in °C), respectively.


SITU OBSERVATIONS

30

At this stage the land surface minimum, maximum, and mean temperatures given in monthly were

merged with the generated mean monthly SST. The final images of climatological monthly day-time,

night-time, and mean temperature of the entire globe were generated. These were used in computing

the dew point temperature as well as performing cloud mask. Figures 3-5(a - c) shows these

climatological images for the month of May for the African continent and part of Atlantic Ocean. The

temperature is in ˚K.

Figure 3-4: Climatological Temperature (in °K) images; (a) day-time (b) night-time (c) mean , of Africa and part

of Atlantic Ocean for the month of May

3.1.3. Dew Point Temperature

Dew point temperature is an important geophysical parameter that indicates the state of moisture

content in the air under given conditions (Hubbard et al., 2003). This is the critical temperature at

(a)

(b)

(c)


SITU OBSERVATIONS

31

which air is fully saturated and below which condensation normally occurs. Figure 3-5 shows

schematic diagram of idealized atmosphere in which a convective cloud is developing vertically.

In their study, Hubbard et al., (2003) presented a temperature-based (daily maximum, minimum, and

mean) daily dew point temperature estimation method for historical studies in the Northern Great

Plains in USA. They developed four regression-based methods incorporating daily maximum,

minimum, and mean temperatures and also daily precipitation for different locations in the plains.

After statistical analysis of the results obtained from the four methods, they concluded that the model

that performed satisfactorily was in the form:

λγβα +−++= )()()( nxnmd TTTTT (3.2)

where: Td is the daily dew point temperature in °C

Tm is the daily mean air temperature in °C

Tn is the daily minimum air temperature in °C

Tx is the daily maximum air temperature in °C

α, β, γ, and λ are coefficients of the regression equation.

The method was further supported by the fact that the associated data set required are easily available

in most typical meteorological weather stations. The model as Hubbard et al., (2003) pointed out can

estimate dew point temperature with sufficient accuracy under varied climatic conditions. Moreover,

they also indicated that the climatic conditions observed within the Northern Great Plains are

representative of many other regions in the world.

Based on the above statements, dew point temperature was therefore computed by use of the model as

given in equation 3.2 and consequently equation 3.3 with all the associated coefficients was adopted.

However, here use of the climatological monthly mean, maximum, and minimum temperatures was

made instead of the daily temperatures. Thus monthly climatological dew point temperature was

obtained as follows:

0119.1)(0072.0)(9679.0)(0360.0 +−++−= nxnmd TTTTT (3.3)

where: Td is the calculated monthly climatological dew point temperature in °C

Tm is the mean monthly temperature in °C

Tn is the minimum monthly temperature in °C

Tx is the maximum monthly temperature in °C

Minimum, maximum and mean monthly temperatures used here were those obtained from the centres

mentioned in section 3.1.2 above. However, it is expected that some slight differences may occur in

the final dew point temperature values obtained since there was no recalibration of the model with

local (African region) data which would otherwise provide more suitable coefficients and

subsequently more accurate estimation of dew point temperature. In addition to this, differences due

to use of monthly instead of daily temperatures are expected since the regression is based on daily

temperatures.


SITU OBSERVATIONS

32

Figure 3-5: Schematic view of temperature lapse rates in an idealized convective cloud.

From dew-point concept as visualised in figure 3-5, cloud height can be extracted. The reference point

is the earth surface. Dry adiabatic lapse rate of 1°C per 100m and saturated/moist adiabatic lapse rate

of 0.6°C per 100m were used here as suggested by Strahler, (1965) and widely accepted in many

studies. An example of cloud height (considering only one pixel value) calculation is given here

below:

Supposing that: surface maximum monthly (e.g. for May) climatological temperature is 300.4K; dew

point temperature (at the base of the cloud) is 299.3K (as obtained from equation 3.3); and that

brightness temperature observed by the satellite at the top of the cloud is 242.7K. Here brightness

temperature was taken as the mean of IR10.8 and IR12.0. In addition supposing an ideal situation of

unstable atmosphere where change of temperature with height (lapse rate) is approximately 1°C per

100m (for dry adiabatic) and approximately 0.6°C per 100m (for moist adiabatic),

THEN: The cloud top height would be calculated as given in the following expression.

)100*6.0*)(()100*1*)(( bddx TTTTH −+−= (3.4)

3506)100*6.0*)7.2423.299(()100*1*)3.2994.300(( =−+−=

where: H is the cloud height in meters

Tx is the maximum monthly climatological temperature in °K

Td is the dew point temperature (in °K) as calculated from equation 3.3

Tb is the brightness temperature (in °K) at the top of the cloud

Thus in this example the cloud top height would be 3506m

Lapse rate =

1°C/100m

Dew point

temperature

Clo

ud

hei

gh

t Condensation level

Earth surface

Top of the cloud

Lapse rate =

0.6°C/100m


SITU OBSERVATIONS

33

3.1.4. Synoptic Data and Field work

Synoptic data was collected (from Eastern part of Africa) during a fieldwork campaign and also

through a request made to CGIS, Rwanda. Fieldwork activity focused on collecting rainfall data

which was measured by setting two rain gauges (tipping bucket type with data loggers); one at

Dagoretti corner (Nairobi, Kenya) and one at Kisumu Airport (Kenya). Rainfall data was also

collected from a rain gauge at the Ministry of Water and Irrigation, Naivasha (Kenya). Locations of

the four stations are as shown in table 3-1 (with the time of rain gauge recording interval) as well as in

figure 3-7.

Table 3-1: Locations of the four stations within Eastern Africa

Station Coordinates Time of Recording Interval

Latitude Longitude

Nairobi (Dagoretti) 1° 18´S 36° 46É not regular (on tipping)

Naivasha 0° 24´S 36° 18É not regular (on tipping)

Kisumu 0° 06´S 34° 36É not regular (on tipping)

CGIS, Butare 2° 36´S 30° 06É 30 minutes

Figure 3-6: Locations of the four stations shown on MSG satellite false colour composite image


SITU OBSERVATIONS

34

The choice of CGIS station was based on availability of long term series of data that is recorded after

every 30 minutes from an hour (for instance; 12:00, 12:30, 13:00, 13:30 UTC etc) (see also appendix

C). With this temporal scale it would be easy for comparison with the MSG satellite images which are

received in 15 minutes. Besides, CGIS is far enough from the other stations in Kenya and sparsely

distributed rainfall stations in this current study are important especially in the rainfall estimation for

the obvious reason that at different climatological regimes rainfall estimation methods may have

different regression functions.

The stations from Kenya (Dagoretti –Nairobi, and Kisumu) were selected again due to their distance

from one another which is about 400km. In addition Kisumu is situated next to Lake Victoria and thus

in cloud mask it would be interesting to investigate the influence of water body (e.g. lake or sea) to

cloud mask. Setting own rain gauge (tipping bucket) was therefore necessary to accurately observe the

rainfall amount over these two stations. The tipping bucket type of rain gauge used here has the

capability of showing the date and time of rainfall observation (see Appendix F). It is therefore easy

to compare observations with MSG satellite images which are acquired in after every 15 minutes.

However, the limitation here was that the time the tipping occurs could be between MSG image

acquisition times. This is well explained by figure 3-7 (b) with the shaded area showing time between

two tips and in which satellite image is acquired between the two tips.


SITU OBSERVATIONS

35

(a)

(b)

Figure 3-7: (a) Image acquisition by SEVIRI radiometer, and (b) Schematic diagram on MSG satellite and Rain

gauge observation time

In addition figure 3-7 (a) shows the time stamping of MSG satellite image data, which indicates that

within the equatorial region the data is acquired between the two times, say for12:00 UTC image,

between 12:00 UTC and 12:12 UTC from south to north of the whole disk. The remaining 3 minutes

MSG image

acquisition time

Time

Rain gauge observation

times

Image acquisition by the SEVIRI

radiometer

(Source: Meteosat Second

Generation System Overview,

EUM TD 07– Issue 1.1, 25 May

2001) (EUMETSAT, 2001)

Archive repeat cycle-time (e.g. 12:12UTC)

Dissemination repeat cycle-time (e.g.12:00UTC)

Appr. 12:06UTC at

equator


SITU OBSERVATIONS

36

are used in calibrating and turning of the radiometric mirror ready for the next image acquisition.

Thus from the two diagrams, it is clear that MSG image acquisition time and gauge observations may

not necessarily be at exactly the same time.

Thus, in order to solve the problem of different time of observations between the rain gauge and the

satellite, there was a need to average over a period of time and space. Here observations within one

hour, four MSG top of atmosphere brightness temperatures of the three infrared channels (IR03.9,

IR10.8, and IR12.0) as well as total amount of rainfall observed at the station, were averaged.

In addition to the two stations in Kenya, rainfall data was collected from a rain gauge at the Ministry

of Water and Irrigation, Naivasha. The rain gauge (tipping bucket) was installed in the year 2004 and

therefore long period of data was available. Sample of data set collected from the field using this type

of rain gauge is given in Appendix F for Nairobi (Dagoretti) which is similar to that of Kisumu and

Naivasha.

3.2. Cloud Masking Method

Cloud mask method chosen in this study was based on multispectral thresholding technique. This

included creating ILWIS scripts in order to generate the necessary images required for processing the

cloud mask image.

In this method, a number of tests that allowed identification of pixels contaminated by clouds were

performed. The main characteristic of these tests, applied to sea or land pixels, depended on the solar

illumination conditions and on the satellite viewing angle. The definitions of day-time, night-time, and

twilight are as given in table 2-2. The quality of the cloud detection process was assessed by

visualizing with the visible bands (for day-time) of the same day and time. Night cloud mask were

checked by using one thermal band.

Here use of non-linear algorithm, as developed by (Météo-France, 2005a), was made in order to

compute sea surface temperature using climatological SST. Split window approach was used with

IR10.8 and IR12.0 (brightness temperature of bands 9 and 10) averaged and applied in the algorithm

which is in the form given below.

( )( ) ( ) 2.010718.1*07293.01sec18116.198826.0 0.128.108.10 ++−+−+= TTSSTTTs θ (3.5)

where: sT is the calculated sea surface temperature (SST) in °C

8.10T and 0.12T are brightness temperature (in °C) of bands 9 and 10 respectively

SST is the climatological sea surface temperature (°C)

Sec θ is the inverse of cosine of the satellite zenith angle.

Here climatological night-time temperature was used as SST. A number of tests were performed in an

attempt to extract cloud-contaminated pixels. These tests are explained in the next chapter as well as

in the ILWIS scripts which are given in Appendix A. The final cloud mask obtained was then ready

for further cloud classification.


SITU OBSERVATIONS

37

Cloud mask results were also validated using available cloud mask products from EUMETSAT,

thanks to the ITC Geodata software development section that recently developed the GRIB2 decoder.

During initial stages of this study, validation was not possible since there were no available cloud

mask products in the same format as MSG products readily available within ITC. An example of

cloud mask from EUMETSAT is as given in figure 2-3 showing cloud as white, clear land as green,

and clear sea as blue.

The following section outlines how the mask was classified according to heights and used to compare

and estimate rainfall from clouds (here going to be referred to as storms).

3.3. Rainfall Estimation Method

As earlier pointed out, rainfall is one of the most difficult atmospheric parameter to measure due to its

variability in space and time. However, (Jobard, 2001) stated that rainfall can be inferred from

infrared satellite observations in which case brightness temperature for thermal bands for e.g from

10.8µm and 12.0µm measured over cloudy area is related to the cloud height. Clouds with very cold

top temperature indicate deep convection which is associated with observed surface precipitation

especially in the tropics. The relationship between infrared temperature and rainfall intensity or

amount is entirely indirect (Jobard, 2001). Generally it is difficult to discriminate the convective part

of the system producing heavy rainfall from the stratiform part of the system or cirrus clouds which

are also cold at the top and hardly produce rainfall. In this case therefore, it is worthy attempting to

relate cloud height with observed rainfall. The height has to be obtained from methods as explained in

the previous section 3.2 in which an attempt has been made to extract clouds at various heights.

One-dimensional cloud model-based technique was used in this study to find the basic relationship

between cloud height and observed rainfall at a ground station. This was based on relating cloud top

height to rainfall amount. Regression-based model as given in equation (3.3) was used to obtain cloud

height images. Cloud top height was processed from the cloud top temperature as recorded by MSG

satellite. Here brightness temperatures for bands 9 and 10 were averaged. Figure 3-5 indicates

schematic temperature lapse rate in relation to cloud height. The general assumption here was that

temperature lapse rate, as depicted in the schematic diagram holds in an unstable atmosphere in which

case deep convective clouds are formed.

In this study, rainfall estimation was based on comparison between point observation and satellite

estimation using the one-dimensional cloud-model based technique as mentioned above. In this case

therefore, and as Maathuis et al., (2006) pointed out; there is a need to incorporate an averaging

procedure in order to account for the collocation problems such as spatial and timing offsets. Here

spatial average was carried over 5x5 pixels and temporal average was carried for an hour (four MSG

images in an hour). Retrieved temperatures of infrared bands 4, 9, and 10 were averaged and used in

the simple cloud mask algorithm developed as an ILWIS script given in Appendix A. See also the

next chapter for explanations of different thresholds used. Cloud height obtained in this algorithm was

used to compare and estimate rainfall from storms.


SITU OBSERVATIONS

38

The method used in this study follows the idea that clouds produce different amount of rainfall or

have different rainfall intensity at different stages of development. This is best represented by (Hong

et al., 2004b) in their study on cloud patch-based rainfall estimation using satellite image

classification approach. It should be noted here that due to different climatological regimes, empirical

relations (either temperature versus rainfall intensity or cloud height versus rainfall intensity) derived

may vary significantly. (Adler and Mack, 1984) studied the impact of the regime-to-regime various on

empirical rain estimation schemes based on satellite-observed cloud height or cloud temperature

information in which curves representing coastal and inland regimes were strikingly different. They

pointed out that these differences had obvious implications for the application of an empirical satellite

rain estimation derived in one location and applied in other climatological regimes even with a simple

local adjustment. Varying synoptic situations may also cause these types of differences.

Rainfall data from CGIS station in Rwanda was investigated to identify storms which produced

rainfall over a given period (recorded after every 30 minutes). The observations were recorded at e.g.

1000hrs, 1030hrs, 1100hrs, 1130hrs, etc. The data was in local time and was converted to Universal

Time Convention (UTC). For the case of Rwanda, 2 hours are subtracted from local time to change to

UTC and for Kenya 3 hours are subtracted from local time.

Appendix C shows sample of meteorological records including rainfall obtained from CGIS weather

station and used to investigate cloud height variation over a given period. This would give an idea on

how the two relate over the selected region which may provide water resource management

authorities first approximations of rainfall amount expected from a storm at a particular height.

Twelve storms of different days and time from CGIS station were used to develop a regression

function between the height and observed rainfall. The function was consequently used to estimate

rainfall amount from other storms over the same station to validate the performance of the method

developed and thresholds selected during the simple cloud mask algorithm development.


SITU OBSERVATIONS

39

4. Data Processing and Results

This chapter attempts to analyse the processed MSG images in detail and the final cloud mask

obtained as well as the cloud height classification images. Analysis of field data and rainfall estimates

drawn from developed regression function(s) are also presented.

4.1. MSG Satellite Images

The first step was retrieval of relevant bands as well as calculation of solar illumination angles. Solar

illumination angles were based on conditions as given in table 2-2. MSG satellite and solar zenith

angles were also generated since this change due to earth rotation about its axis.

4.1.1. Generation of MSG Satellite and Solar Angles

Generating MSG satellite and sun angles was done by creating a batch file which could be adapted for

any date and time in case new angles were required. This particular applet, which can be executed into

an active directory, works in a java environment which must be installed in the system. Generated

angles were imported into ILWIS for further processing. Figure 4-1 shows the flow chart for

generating the satellite and solar angles. The processing was done for mainly MSG field of view

covering Africa (≈ 39˚N - 38˚S and ≈ 34˚W - 53˚E).

Figure 4-1: Flow chart for generating MSG satellite and Sun angles

Create MSG and Sun zenith angles

(In Java environment)

Import satellite and sun zenith angles

into ILWIS

Add MSG georeference

Resample the satellite and sun

angles into required georeference

Calculate secant angle of the satellite

and the sun

MSG secant

angle image

Sun secant angle

image

Apply sun elevation angle

& generate threshold maps


SITU OBSERVATIONS

40

Figure 4-2 shows examples of the sun and MSG satellite zenith angles. Sun position as from the left

image can be seen to be overhead in the south west of the image (brightest part) for 26 December

2006 at 15:00 UTC. MSG satellite is situated at 0˚N and 0˚E and can be seen to appear at the same

position (brightest part) in the right image.

Figure 4-2: Sun (for 26th December 2006 at 15:00 UTC) and MSG Satellite (0˚N and 0˚E)

Zenith angles (left and right hand image respectively)

After calculating sun elevation angle, solar illumination conditions were generated by use of threshold

mentioned in table 2-2 in which the condition is day-time when the sun elevation angle is greater than

10° and night-time when the sun elevation angle is less than -3°. The condition is twilight, that is,

either before night-time or before day-time when the sun elevation angle is between -3° and 10°. An

example of such an image of 7th March 2006 at 15:30 UTC is provided in figure 4-3.

Figure 4-3: Solar illumination conditions on 26th December 2006 at 15:00 UTC

As earlier pointed out, the algorithm is based on a multispectral threshold technique applied to each

pixel of the image. A number of tests for each solar illumination condition in which an example of

0˚

23˚

23˚

46˚

46˚

0° 0°

0˚N 0˚E


SITU OBSERVATIONS

41

cloud mask is given in figure 4-7 were applied. The tests applied in the algorithm attempted to address

both the land and sea surfaces based on their characteristics. Flow charts for these tests are given in

the following respective sections.

4.1.2. Day-time Cloud Mask

Figure 4-4: Description of the test sequences for Land surface (left) and Sea surface (right)

Figure 4-4 shows the description of test sequence used in cloud mask during day-time. Over the land

surface, during the day cloud contaminated pixels were identified by using standard deviation from

the climatological surface temperatures (mean, minimum, and maximum) which should be greater

than 1K. Minimum surface temperature was taken as the monthly climatological night-time

temperature as processed from the ‘WorldClim’ database whereas maximum surface temperature was

taken as day-time temperature and the mean surface temperature was average of the day-time and

night-time surface temperatures.

To remove false cloud assignment to pixels over desert areas, brightness temperature of band 9

(IR10.8) less than 293.15K was applied otherwise the pixels with a higher temperature were

considered cloud free. Further, all pixels already defined as cloudy were subjected to tests in order to

avoid cool areas or higher elevated areas. These involved using monthly climatological temperature

standard deviation (amplitude). Cloudy pixels with brightness temperature (IR10.8) less than

maximum (Tmax) day-time monthly climatological temperature less half the monthly standard

deviation are assigned cloudy else not cloudy. This does not affect the ocean areas since the

climatological standard deviation is very small. This test allows us to reduce misclassifications to the

minimum except in high elevated areas and desert areas.

Over the sea surface, cloud-contaminated pixels were identified by using standard deviation from the

climatological surface temperatures (mean, minimum, and maximum) which should be less than 1K.

Further, small difference of -1K (and above) between the local sea surface temperature (as calculated

Stddev<1

Clear

IR10.8>293.15K

IR10.8> (Tmax -

stddev/2)

Cloud

Yes

Yes

Yes

No

No

No

Stddev>1

and

Tsmin-SSTcal<-1K

Clear Cloud

Yes

No


SITU OBSERVATIONS

42

using equation 3.5), here referred to as SSTcal, and minimum monthly climatological sea surface

temperature (here referred to Tsmin) was also used to mask cloudy pixels over the sea surfaces.

As earlier explained, Météo-France have developed cloud mask in which this study adopted some of

the basic ideas to develop some of the thresholds used. Estimating SST by using IR10.8 and IR12.0

brightness temperature together with minimum monthly climatological SST was used by Météo-

France. Here the same two bands are used, as top of atmosphere (in °K) together with minimum

monthly SST (here taken as the night time temperature). Météo-France took a small difference of 4K

between estimated SST (by using IR10.8 and IR12.0 brightness temperatures) and the monthly

climatological minimum SST. Monthly climatological minimum SSTs are derived from a global

Pathfinder night-time bulk SST climatology. The bulk night-time SST, as (Derrien and Le Gléau,

2005) pointed out, does not account for the thermal heating at midday observed in infrared satellite

measurements. In this study this difference was set at -1K over the sea surface as stated above.

Brightness temperature of band 9 (IR10.8) was applied by Météo-France as well as by (Kidder et al.,

2005) in which the idea was to estimate the temperature that would be observed if there was no water

vapour in the atmosphere. Météo-France computed threshold from surface temperatures forecast by

NWP model. In this study threshold of 293.15K was set as the maximum temperature for any pixel to

be flagged cloud contaminated. Météo-France again used IR10.8 and IR12.0 difference to detect thin

cirrus clouds and cloud edges characterized by higher IR10.8-IR12.0 values than cloud-free surfaces.

Here use of IR10.8 less than the maximum climatological surface temperature (with half amplitude of

the climatological monthly minimum, maximum, and mean temperatures) was to extract thin cirrus

clouds as well as to avoid confusion of moist, warm, cloud free areas with clouds. With these few

tests day-time cloud mask was obtained of which an example is as given in figure 4-7 (a).

Notable features of this cloud mask are such as sharp boundary between the land and the sea that

appears along some coastal areas, especially in this particular case to the North West of the continent.

This depicts cloudy conditions over the ocean and non-cloudy conditions over the land, which may

not be always the case. The sharp boundary is due to the land-sea temperature effects and increases as

we move from equatorial regions to higher latitudes where temperatures are generally low over the sea

such as the case in the north-western part of the continent (over the Atlantic ocean). This is more

pronounced especially when desert (usually with high temperatures) areas lie next to water body.

Cloud mask image shows presence of clouds over the northern part of Africa whereas from the false

colour composite of the visible and near infrared bands does not show the same situation. Over central

Africa and Atlantic Ocean (the specific region of interest in this study) most of the cloudy pixels (as

can be seen from the false colour composite image) have been masked out.

Also as can be seen from the false colour composite image in figure 4-9 (b), there appears no thick

clouds in the northern part of the continent. However, the algorithm has classified the region to be

under low level clouds which are semi-transparent in the visible and near infrared bands.


SITU OBSERVATIONS

43

4.1.3. Night-time Cloud Mask

Figure 4-5 shows the description of the test sequences used in cloud masking at night time. Details of

these tests are explained below.

Figure 4-5: Description of test sequence for land surface (left) and sea surface (right)

During night-time, the standard deviation of the monthly climatological temperature was set at a

minimum of 1K and the mean brightness temperature (Tmean) of IR10.8 and IR12.0 was taken less than

283.15K over the land surface for any pixel to be flagged as cloudy. The difference of the monthly

minimum climatological temperature and the brightness temperature of IR10.8 is used is set to be

greater than 9K for any pixel to be assigned cloudy. This ensured avoiding cooler areas at night which

would otherwise be assigned cloud contaminated. Use of mean brightness temperature for IR10.8 and

IR12.0 followed Météo-France developed cloud mask idea in which the difference between the two is

used to detect thin cirrus clouds and cloud edges characterised by higher difference (IR10.8-IR12.0)

values than cloud-free surfaces. However, in this study the mean of the two was expected to simply

avoid the confusion of very moist, warm, cloud free areas with clouds.

Over the sea, the standard deviation of the monthly climatological temperature is less than 1K. The

difference between the local calculated sea surface temperatures (SSTcal) and the monthly mean

climatological temperature was taken to be greater than 10K. Low clouds over the sea were screened

by use of IR03.9 to scale down aggregated temperatures of IR10.8 and IR12.0 (i.e. IR10.8*IR12.0).

The difference between their mean temperatures and the scaled temperature is set at a minimum

threshold value of 2K for the cloudy pixels. This test is based on the fact that the water cloud

emissivity is lower at IR03.9 than in IR10.8 or IR12.0. The test allows detecting low clouds at night

time. The approach is the same as that of Météo-France using the difference between IR03.9 and

IR10.8. An example of night-time cloud mask is given in figure 4-7 (c).

Stddev>1K

and

Tmean-SSTcal<10K

[(IR10.8*IR12.0)/IR03.9]-

Tmean <2K

Cloud Clear

Yes

No

No

Yes

Stddev<1K

and

Tmin-IR10.8<9K

and

Tmean>283.15K

Clear Cloud

Yes

No


SITU OBSERVATIONS

44

4.1.4. Twilight Cloud Mask

Figure 4-6 shows the test sequences used in extracting clouds during twilight time. Details of these

tests and dynamic thresholds are explained below the figure.

Figure 4-6: Description of test sequence for land surface (left) and sea surface (right)

At twilight time the difference between climatological minimum temperature and the brightness

temperature of band 9 (IR10.8) was set at a threshold of 9K such that any pixel with greater difference

than this value and with mean brightness temperature (IR10.8 and IR12.0) less than 283.15K were

cloud contaminated. This ensured screening cloudy pixels over the land surfaces where also standard

deviation of the monthly climatological temperatures was set at a minimum of 1K.

Over the sea, the difference of mean monthly climatological SST and the calculated SST was taken to

be greater than 5K for the cloudy pixels. Here Météo-France used IR10.8 and IR12.0 brightness

temperatures to estimate SST by using a nonlinear split window algorithm. A pixel is flagged cloud

contaminated if its estimated SST value is lower than a minimum monthly climatological SST value

by 4K. However, Météo-France does not apply this test where climatological SST is lower than

270.15K. In this study low clouds were extracted by use of IR3.90 to scale down brightness

temperature of bands 9 and 10 (IR10.8 and IR12.0, respectively). Here maximum threshold value of

2K as the difference between the scaled temperature and the mean brightness temperature of IR10.8

and IR 12.0 was used. Threshold for the difference between estimated SST and the climatological

SST from Météo-France gives a threshold of 4K which is comparable to the set value in this study.

Météo-France uses IR03.9 and IR10.8 difference to extract low clouds for both day-time and twilight

time basing the fact that solar reflection at IR03.9 (approximated by the IR03.9-IR10.8 brightness

temperature difference) may be rather high for clouds (especially low clouds), which is not the case

for cloud free areas . An example of twilight cloud mask from this study is as given in figure 4-7 (b).

Stddev<1K

and

Tmin-IR10.8<9K

and

Tmean>283.15K

Cloud Clear

Yes

No

Stddev>1K

and

Tmean-SSTcal<5K

[(IR10.8*IR12.0)/IR03.9]-

Tmean>2K

Cloud Clear

Yes

No

No

Yes


SITU OBSERVATIONS

45

Figure 4-7: Cloud masks for (a) day-time, (b) twilight time, and (c) night-time; for MSG-1 image of 7th March

2006 at 15:30 UTC.

Cloudy pixels are represented as green whereas grey represents non-cloudy pixels. Merging the three

images resulted in final cloud mask as given in figure 4-9 (a). Colour composite image of the same

day and time is as in figure 4-9 (b). Here solar illumination conditions are as given in figure 4-8

below.

Figure 4-8: Solar illumination conditions on 7th March 2006 at 15:30 UTC

(a)

(b)

(c)


SITU OBSERVATIONS

46

Figure 4-9: Cloud mask (a) and false colour composite (b) for MSG image of 07/03/2006 at 15:30 UTC.

In the cloud mask, green are clouds and white are no cloudy pixels. False colour composite of the

visible and near infrared bands (VIS006, VIS008, and NIR016) is more visible from central Africa to

the Atlantic Ocean. This is the day-time region as can be seen from figure 4-8. To the eastern part, it

is not easy to visualise since this area already falls under twilight and night conditions.

Sharp boundary between the land and the sea can be seen to appear along some coastal areas,

especially in this particular case to the North West. This depicts cloudy conditions over the ocean and

non-cloudy conditions over the land, which may not be always the case. The sharp boundary is due to

the land-sea temperature effects. This is more pronounced especially over desert (usually with high

temperatures) areas next to water body. Cloud mask image shows presence of clouds over the

northern part of Africa whereas from a visual check using the false colour composite of the visible

and near infrared bands does not show the same scenario. Over central Africa and Atlantic Ocean

most of the cloudy pixels (as can be seen from the false colour composite image) have been masked

out.

The next step was to process heights for the extracted clouds based on the formula for estimating dew

point temperature as given in equation 3.4 in which an example is given in figure 4-10.

a b

Legend

Cloud

Cloud free


SITU OBSERVATIONS

47

Figure 4-10: Cloud height (in Meters) image (MSG image of 07/03/2006 at 15:30 UTC)

The cloud height images were classified into three different classes namely; low clouds (50m-1500m),

middle clouds 1500m-3000m, and high clouds (> 3000m). These classes were chosen based on

occurrence of different types of clouds at different levels as is shown in figure 2-1. Based on this

classification and as explained in section 2.1, it is possible to show areas where precipitation is likely.

However, this is further investigated in the proceeding section of rainfall estimation. An example of

classified cloud height image is given in figure 4-11 below.

Figure 4-11: Classified cloud height image of 07/03/2006 at 15:30 UTC


SITU OBSERVATIONS

48

In order to check whether all cloudy pixels have been extracted properly for the area of study,

segmentation of the classified cloud image was done and overlaid on to a false colour composite

image of visible and near-infrared bands of the same day and time. Figure 4-12 shows an example of a

small window (eastern part of Africa) of such an overlay for MSG satellite image of 23rd November

2005 at 13:30 UTC. This area was under twilight condition on 7th March 2006 at 15:30 UTC and thus

such an overlay is not provided here. Segmentation was performed on the classified cloud height

image of 23rd November 2005 at 13:30 UTC and the segments overlaid on the false colour composite

image of the same date and time.

Figure 4-12: Segments (yellow lines) of cloud mask of 23/11/2005 at 13:30 UTC overlaid on False

colour composite (VIS006, VIS008, and NIR016) in (BGR)

Clouds appear as cyan in colour in the false colour composite image. As can be observed visually

from figure 4-12, most of the cloudy pixels have been identified. This is more visible over areas where

deep cyan colour (mostly deep convective clouds) appears. Some semi-transparent clouds have not

been masked out. However, this is not of serious concern in this current study since most of these

semi-transparent clouds do not contribute to precipitation, and if they do, very little rainfall is

expected from them. Further discussions to the accuracy of the simple cloud mask algorithm

developed are provided in the next chapter.

4.2. Rainfall Estimation (A case of CGIS Weather station)

As earlier pointed out, CGIS weather station has a rain gauge which is set to measure rainfall among

other meteorological parameters in every 30 minutes. The advantage to such a type of record of data is

that it is possible to compare with MSG satellite observation(s) of parameters such as top of

atmosphere brightness temperatures of infrared bands, cloud height, and cloud type; and develop a

relationship that can be used to infer rainfall intensity or amount from observed (non atmospherically


SITU OBSERVATIONS

49

corrected) satellite parameters. Various methods as explained in section 2.5 can be used in developing

the relations between these parameters. In this study rainfall versus processed cloud height were

investigated to get an idea on how they relate and also to find a function that can be used to forecast

rainfall intensity or amount expected on a ground station (e.g. CGIS).

An attempt was made to relate rainfall intensities from clouds of different dates and times with

processed cloud heights of the same dates and times. The relationships were generally too low. This

was mainly due to the fact that at different dates and times the cloud/storm over the station is not

necessarily at the same development stage. It is likely that in such an approach, relationships are being

drawn for storms at different stages of their development (and probably of different types) over the

station.

As explained in section 2.5.5, satellite-based rainfall estimation algorithm, Precipitation Estimation

from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud

Classification System (CCS) by Hong et al., (2004a), extracts cloud features from IR10.8

geostationary satellite imagery in estimating fine scale rainfall distribution. The algorithm processes

satellite cloud images into pixel rain rates by; separating cloud images into distinctive cloud patches,

extracting cloud features, clustering cloud patches into well-organized subgroups, and calibrating

cloud-top temperature and rainfall relationships for the classified cloud groups using gauge-corrected

radar hourly rainfall data.

Based on this method, therefore, relating rainfall intensities and cloud heights of different dates and

times was not expected to yield good relationship. Thus storms over the station were treated

separately in order to relate their rainfall intensities or total rainfall amounts with their heights.

The relationships between cloud height and the rainfall intensity as well as between cloud height and

the total rainfall from a storm were then developed. The following sections detail the results of these

approaches.

4.2.1. Direct Comparison of Cloud Height and Rainfall Intensity

Rainfall estimation method used here was based on average storm height during its existence over

ground station. Firstly, diurnal trend of observed rainfall intensity and processed cloud heights, over

the weather station, were investigated. Two days were selected for this purpose and figure 4-13 shows

how cloud height and rainfall intensity varied during the selected days. MSG images of 30 minutes

interval were processed to obtain cloud heights whereas rainfall intensities observed at the station at

the same time of MSG image acquisition were used.

In both cases it can be seen that clouds at a height of above 3000m contribute to a large fraction of the

rainfall recorded at the station. The highlighted part clearly shows the time rainfall was observed at

the station which agrees with the time of high cloud heights as processed by the simple cloud height

algorithm developed using the dew point temperature and lapse rate concepts as applied in equation

3.4.


SITU OBSERVATIONS

50

0

1000

2000

3000

4000

5000

6000

0:30

3:30

6:30

9:30

12:30

15:30

18:30

21:30

Local time (Hrs)

Clo

ud

He

igh

t (m

)

0

10

20

30

40

50

60

Ra

infa

ll I

nte

ns

ity

(m

m/h

r)

Cloud height

R/Intensity

(a)

0

1000

2000

3000

4000

5000

6000

0:30

3:30

6:30

9:30

12:30

15:30

18:30

21:30

Local time (Hrs)

Clo

ud

He

igh

t (m

)

0

5

10

15

20

25

30

35

40

45

50

Ra

infa

ll I

nte

ns

ity

(m

m/h

r)

Cloud height

R/Intensity

(b)

Figure 4-13: Diurnal cloud height and Rainfall intensity changes on (a) 5th May 2006, and (b) 10th May 2006

Secondly, identification of storms on different days and plotting their rainfall intensities against

processed cloud height, in this case in class intervals of 500m, followed. This was meant to check

whether the above two day’s cases were a mere coincidence or is the true scenario expected from this

particular station. Here cloud heights were grouped from 2500m in intervals of 500m. Fifteen storms

with their 30 minutes interval processed heights and their respective observed rainfall intensities were

plotted as shown in figure 4-14. For details of these storms refer to Appendix D.

0

10

20

30

40

50

60

2500 3000 3500 4000 4500 5000 5500 6000

Height (m)

Ra

infa

ll In

ten

sity (

mm

/hr) Rain intensity

Figure 4-14: Rainfall intensities within cloud height classes

From this plot it is clear that high rainfall intensities are observed within cloud heights of 4000m to

6000m. This indicates the same situation as in the case of 5th May 2006 and 10th May 2006 as shown

in figure 4-13. However, in each cloud height class there are low rainfall intensity observations. This

could be associated with early stages of cloud formation or late stages (dissipating stage) of the cloud.

Rainfall intensity within each class was averaged and plotted against average cloud height in each

class. The best model fit was found to be Gaussian, whose regression function is: (y=a*exp ((-(x-

b)^2)/(2*c^2)), where: a = 9.8, b = 4745.6, and c =1200.3; with correlation coefficient of 0.95 and

standard error of 1.03). This model agrees with the fact that very low clouds (e.g. stratocumulus,


SITU OBSERVATIONS

51

cumulus, and stratus) and very high clouds (e.g. cirrus, cirrocumulus, and cirrostratus) produces very

low rainfall.

Figure 4-15: Gaussian model fit, X = Average cloud height (m), Y = Average rainfall intensity (mm/hr)

An attempt was made to use this function to estimate rainfall intensity for various storms. Figure 4-16

represents plots of all observed and estimated values within each cloud height class. It is clear that the

function overestimated the rainfall intensities from these storms except in very few levels (height)

where it underestimated. This appears so when cloud height is between 2500m and 3000m. Thus there

was a need to adopt a different approach by either using rainfall intensity or total rainfall amount from

different storms.

0.00

2.00

4.00

6.00

8.00

10.00

12.00

2500 3000 3500 4000 4500 5000 5500 6000

Cloud height (m)

Rain

fall

Inte

nsity

(m

m/h

r)

Observed

Estimated

Figure 4-16: Observed and estimated rainfall intensity for different storms

Based on the above results, it can be observed that the developed regression function did not perform

well. There was general overestimation of rainfall intensity. Further investigation of the relationship

between cloud height and total amount of rainfall from a storm was carried out as explained in the

following section.

Av

era

ge

rain

fall

in

ten

sity

(m

m/h

r)

Average cloud height (m)


SITU OBSERVATIONS

52

4.2.2. Direct Comparison of Cloud Height and Total Rainfall

Diurnal trend of observed total rainfall and processed cloud heights, over the weather station, were

investigated. Two days were selected for this purpose and figure 4-17 shows how cloud height and

total rainfall varied during the selected days. MSG images of 30 minutes interval were processed to

obtain cloud heights whereas total rainfall observed at the station at the same time of MSG image

acquisition was used.

0

1000

2000

3000

4000

5000

6000

0:30

3:30

6:30

9:30

12:30

15:3

0

18:30

21:3

0

Local time (Hrs)

Clo

ud

He

igh

t (m

)

0

5

10

15

20

25

30

35

To

tal

Ra

infa

ll (

mm

)

Cloud height

Total Rainfall

(a)

0

1000

2000

3000

4000

5000

6000

0:30

3:30

6:30

9:30

12:3

0

15:3

0

18:3

0

21:3

0

Local time (Hrs)

Clo

ud

He

igh

t (m

)

0

5

10

15

20

25

30

To

tal

Ra

infa

ll (

mm

)

Cloud height

Total Rainfall

(b)

Figure 4-17: Diurnal cloud height and Total rainfall changes on (a) 5th May 2006, and (b) 10th May 2006

It can be observed that rainfall was recorded at the station when the processed cloud height was at

high levels (above 3000m). It is clear then that high clouds over this station are the main rain

producing rainfall clouds. This indicated that there is a relationship between cloud height and total

rainfall produced by the cloud at certain height. The general idea followed in this comparison is that

the more the cloud is sustained at a certain height while producing rainfall, the more the rainfall is

observed at a ground station. This idea is borrowed from the case of CCD as explained in section

2.5.3 in which the relationship drawn from the life-history cycle of the storm was found to be linear

provided spatial and temporal average are considered.

However, as (Grimes et al., 1999) pointed out, the most important assumption is that rainfall is

predominantly convective in origin and that the raining clouds can be identified as those with cloud

top temperatures below a certain temperature threshold. Here, cloud height is used to compare the

total amount of rainfall observed at CGIS. A regression function can be derived using as many storms

from the station as possible.

Twelve storms were used to derive a regression function that was later used to estimate total amount

of rainfall from other storms for validation purpose. Comparison with the observed station amount

over the same period with the estimated rainfall amount showed slight overestimation for some storms

and underestimation for others. Table 4-1 shows the date and time of the storms used to develop the

relationship between cloud height and the observed total storm event rainfall. Appendix E shows

processed details of the twelve storms.


SITU OBSERVATIONS

53

Table 4-1: Observed storms and their total amount of rainfall

Storm Date Time Duration Average Total

(UTC) (hrs) Cloud Height Rainfall

(m) * (mm)

1 07/03/2006 1530-1930 3.5 5317.5 18.0

2 07/03/2006 2230-0200 3.5 5317.3 8.6

3 18/03/2006 1230-1530 3 3074.3 2.8

4 27/03/2006 1700-2130 4.5 3361.6 12.2

5 01/04/2006 2100-0500 8 3917.6 42.0

6 14/04/2006 1530-1800 2.5 2614.3 5.2

7 05/05/2006 2030-2230 2.5 4571.7 63.0

8 10/05/2006 0630-0930 3 4819.8 38.6

9 12/05/2006 1400-1900 5 5132.8 38.2

10 20/07/2006 2200-0130 3.5 2448.3 2.2

11 22/07/2006 0430-0630 2 2455.4 1.4

12 05/08/2006 1400-1630 2.5 5716.5 11.4

* Above the terrain

Determination of the model fit showed Gaussian fit as the best for CGIS by using the 12 storms that

appeared over the station selected for this analysis. The above storms were used to determine a

regression function between the two variables. The best fit obtained was again a Gaussian model

(y=a*exp ((-(x-b)^2)/(2*c^2)); where: a = 60.6, b = 4405.3, and c = 583.0 with correlation coefficient

of 0.96 and standard error of 6.56mm. This is presented graphically in figure 4-18. The model agrees

with the fact that very low clouds (e.g. stratocumulus, cumulus, and stratus) and very high clouds (e.g.

cirrus, cirrocumulus, and cirrostratus) produces very low rainfall.

Figure 4-18: Gaussian model fit, X= Average storm height (m), Y= Total rainfall (mm)

To

tal

rain

fall

(m

m)

Average cloud height (m)


SITU OBSERVATIONS

54

The model whose equation is (4.1) was used to estimate the total amount of rainfall from other storms

over the same station.

−−=

2

2

0.583*2

)3.4405(exp*6.60

xy (4.1)

where: y is the estimated total storm rainfall

x ix the average cloud height

Five storms were taken for estimating the total amount of rainfall expected from them and the results

are presented in table 4-2 and graphically shown in figure 4-19, together with the standard error.

Table 4-2: Storm heights and estimated total rainfall

Avg. Obs. Est.

Storm Total Total

Height Rainfall Rainfall Difference

Storm Date Time (UTC) (m) (mm) (mm) (%)

1 08/03/2006 1600-1700 5588.2 2.6 7.74 -198

2 21/04/2006 0900-1000 3127.7 5.0 5.49 -10

3 25/04/2006 1900-2000 2615.5 5.8 0.54 91

4 14/05/2006 1900-2130 5487.2 13.0 10.83 17

5 16/05/2006 1400-1500 2730.2 2.8 0.98 65

From these results it can be seen that two out of five storms have been estimated to a reasonable

accuracy. These were the storms of 21st April 2006, and 14th May 2006. The 8th March 2006 storm

was overestimated by 198% which is quite high whereas that of 21st April 2006 was overestimated by

very low percentage of 10%. The rest of the storms were underestimated with lowest at 17% (14th

May 2006). Figure 4-19 shows a plot with error bars whose value is 6.56 mm (standard error of the

derived function).

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5

Storms

Tota

l Ra

infa

ll (m

m)

Observed

Estimated

Figure 4-19: Observed and Estimated total rainfall plotted with the error bars


SITU OBSERVATIONS

55

Furthermore relating the observed and the estimated for these five storms, a relation in the form of:

Observed Rainfall = 0.5777*Estimated Rainfall +2.8847, was obtained.

y = 0.5777x + 2.8847

R2 = 0.3615

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12

Estimated rainfall (mm)

Ob

se

rve

d r

ain

fall (

mm

)

Figure 4-20: Relationship between the observed and the estimated total rainfall

Goodness of fit (R2) of approximately 0.36 was obtained in this relationship. This shows that there is

low correlation between observed total rainfall and that estimated using the derived regression.

However, considering the approach as enumerated above, all the estimates can be said to be nearly the

same as the observed total rainfall.


SITU OBSERVATIONS

56


SITU OBSERVATIONS

57

5. Discussions of Results

This chapter discusses in detail results of the simple cloud mask and rainfall estimation as obtained in

the previous chapter. Various results of previous studies are compared to some results of the current

study.

5.1. Cloud Mask Results

The simple cloud mask (SCM) algorithm has been checked and validated using the EUMETSAT

cloud mask products. The choice of this algorithm for validation was based on the fact that

EUMETSAT validation procedure of their cloud mask was more realistic since they use database that

is automatically built and is collocated with the MSG satellite data and that of surface observations.

The surface data used are hourly weather observations, coded by observers into the World

Meteorological Organization (WMO) synoptic code (SYNOP). In addition meteorological

information extracted from the French NWP model Action de Recherche Petite Echelle Grande

Echelle (ARPEGE) forecast fields is used. Based on these facts, it can be concluded that EUMETSAT

cloud mask algorithm as developed by Météo-France is more robust as compared to other cloud

algorithms for MSG satellite images. The current study was not able to collect such enormous data for

validation thus made use of EUMETSAT products.

These products are available from EUMETSAT in GRIdded Binary (GRIB2) format. GRIB is a World

Meteorological Organization’s (WMO’s) standard binary format for exchanging gridded data. The

raw data from EUMETSAT was imported into ILWIS after gluing the six segments (provided in the

original EUMETCast data stream) of MSG satellite field of view that are provided. In ILWIS a

procedure to cross check the accuracy of the cloud mask developed in this study is given in the flow

chart (figure 5-1) below.


SITU OBSERVATIONS

58

Figure 5-1: Flow chart on segmentation and visualization of EUMETSAT CLM and SCM

EUMETSAT CLM raw data received at ITC was processed to check on the accuracy of the developed

simple cloud mask (SCM) in this study. The EUMETSAT CLM of MSG of 26th December 2006 at

15:00 UTC is given in figure 5-2. GRIB2 import routine developed at ITC was used to convert the

data and appropriate classes were assigned manually. These were named as; cloud, clear land, water,

and background. In addition GRIB2 import routine assigns the proper geometric model and therefore

the cloud mask can be directly integrated with other processed results.

EUMETSAT CLM

(GRIB2)

Simple Cloud Mask

(SCM)

Reclassification

Mask clouds only

Sub-map study area Sub-map study area

Segmentation of

cloud mask

Segmentation of

cloud mask

Overlay onto a VIS/NIR false

colour composite image

VISUALIZATION


SITU OBSERVATIONS

59

Figure 5-2: EUMETSAT cloud mask assigned feature classes for 26th December 2006 at 15:00 UTC

The clouds were selected and a small window (≈ 11°N - 14°S and ≈ 6° - 51°E), covering a part of the

tropics over the African continent considered in this study in developing the SCM, was extracted.

Segmentation was done for both SCM and EUMETSAT CLM. Both were overlaid onto a false colour

composite of the same day and time (e.g. 26th Dec 2006 at 15:00 UTC) and figure 5-3 shows the

results of these overlays.

Figure 5-3: Cloud mask segments of EUMETSAT CLM (yellow lines) and SCM (red lines) for 26th December

2006 at 15:00 UTC, on a false colour composite


SITU OBSERVATIONS

60

From figure 5-3 it can be seen that the segments of the EUMETSAT CLM and those of SCM match in

some areas and mismatch for other areas. Detection of clouds from the developed algorithm will be

assumed to be accurate when the two lines exactly overlaid on each other in this visualization

procedure. It can be seen that over the Indian Ocean, the SCM did not extract majority of the cloudy

pixels. This could indicate that the climatological SST has to be reviewed either to use daily data

instead of the monthly used in this study.

However, the other technique used to evaluate the accuracy of the SCM was based on creating

confusion (contingency) matrix. This method compares all pixels, within a selected window, to find

out whether the pixels are assigned as cloudy or non-cloudy in both masks.

Here the two cloud masks images were crossed to built a contingency table that indicates the number

of pixels in each category. This will show the ability of the SCM to detect cloudy and non-cloudy

events based on EUMETSAT CLM. In order to get better results of accuracy of the SCM, there is a

need to use a number of images. Since the SCM algorithm was developed based on different solar

illumination conditions, it was appropriate to choose MSG images based on these three conditions.

Here four days images were used for validation and their contingency matrices are as given in the

following tables 5-1 to 5-4 for the specified day and time.

Table 5-1: Contingency table for MSG image of 25th December 2006 at 12:00 UTC

Table 5-2: Contingency table for MSG image of 26th December 2006 at 15:00 UTC

EUMETSAT CLM (Number of pixels)

Cloudy Not cloudy Total Error of

commission (%)

Cloudy 648922 56295 705217 8.0

Not cloudy 73981 415852 489833 15.1

Total 722903 472147 1195050

Sim

ple

cl

ou

d

ma

sk

(SC

M)

(Nu

mb

er

of

pix

els)

Error of

Omission

(%)

10.2 11.9 Overall Accuracy: 89.1 %



commission (%)

Cloudy 587923 97384 685307 14.2

Not cloudy 35360 472169 507529 7.0

Total 623283 569553 1192836

Sim

ple

cl

ou

d

ma

sk

(SC

M)

(Nu

mb

er

of

pix

els)

Error of

Omission

(%)

5.7

17.1

Overall Accuracy: 88.9 %


SITU OBSERVATIONS

61

Table 5-3: Contingency table for MSG image of 4th January 2007 at 22:00 UTC

Table 5-4: Contingency table for MSG image of 10th January 2007 at 17:00 UTC

From these tables it can be seen that cloud mask of 25th December 2006 at 12:00 UTC, 26th December

2006 at 15:00 UTC and that of 4th January 2007 at 22:00 UTC had the highest accuracies of 89.1%,

88.9% and 88.0% respectively. In the same cloud masks only 10.2%, 5.7% and 7.8% (respectively) of

total number of pixels detected by EUMETSAT, were not detected by the SCM. This can be termed

as the cloud failure score or underestimation of cloudy events. On 10th January 2006 at 17:00 UTC,

there was relatively higher cloud failure of 15.7%. This could be associated with non-detection of low

clouds or thin, semi-transparent broken clouds at night over both the land and the sea. On this day

SCM also depicted a slightly lower overall accuracy of 83.3%.

On 25th December 2006 at 12:00 UTC (day-time), 8.0% of total number of cloudy pixels from the

SCM was not under cloudy conditions as per the EUMETSAT CLM. This is very low as compared to

other days where the total number of pixels assigned cloudy were almost double; 14.2% (26th

December 2006 at 15:00 UTC), 16.7% (4th January 2007 at 22:00 UTC), and 22.5 % (10th January

2006 at 17:00 UTC). This implies that the day-time algorithm was able to differentiate the cloudy and

non-cloudy pixels to a greater accuracy as compared to the night-time and twilight time algorithms.

Besides, only 7.0% and 7.1% of the EUMETSAT cloudy pixels were assigned non-cloudy by the

SCM for 26th December 2006 at 15:00 UTC and 4th January 2007 at 22:00 UTC, respectively. This

was low as compared to the SCM of 25th December 2006 at 12:00 UTC and 10th January 2007 at

17:00 UTC, which assigned EUMETSAT CLM cloudy pixels as non-cloudy at 15.1% and 12.0%,

respectively. This could be associated to the use of NWP models by EUMETSAT which is likely to

model the more variable day atmospheric profile to a greater accuracy. This is not possible with the

simple thresholds used in this study and thus the high difference in assigning cloudy pixels to non-

cloudy despite good results in overall accuracy of 89.1% for the day SCM.



commission (%)

Cloudy 500360 100566 600926 16.7

Not cloudy 42209 549701 591910 7.1

Total 542569 650267 1192836

Sim

ple

cl

ou

d

ma

sk

(SC

M)

(Nu

mb

er

of

pix

els)

Error of

Omission

(%)

7.8

15.5




commission (%)

Cloudy 419299 121656 540955 22.5

Not cloudy 78193 575902 654095 12.0

Total 497492 697558 1195050

Sim

ple

cl

ou

d

ma

sk

(SC

M)

(Nu

mb

er

of

pix

els)

Error of

Omission

(%)

15.7

17.4



SITU OBSERVATIONS

62

Generally underestimation could be occurring over the sea areas since climatological SST used was

monthly mean SST. Use can be made of 5-, 7-, or daily- mean SST which most likely would improve

the accuracies.

During day-time (25th December 2006 at 12:00UTC), SCM overestimated cloudy events by only

11.9%, which is slightly lower than the other times. It is likely that this low failure is due to thresholds

used. Use of NWP model by EUMETSAT to compute some thresholds for twilight time and night-

time seems to improve the accuracies during these times and hence the higher differences from the

SCM. Better results of the day-time cloud mask could be associated with the fact that convective

activities could be present and thus easy to screen the clouds based on the thresholds used in this

study.

Moreover, the period selected here is when the Inter-tropical Convergence Zone (ITCZ) is generally

within the region under consideration and therefore convective activities, with low presence of thin

and semi-transparent cirrus clouds, are common. On 26th December 2006 at 15:00UTC (twilight time),

4th January 2007 at 22:00UTC (night-time), and 10th January 2007 at 17:00UTC (twilight time),

overestimation score was 17.1%, 15.5%, and 17.4%, respectively.

In general terms and considering the four situations, the overall accuracy of the study is 87.3%. This

indicates that SCM performed well and thus the simple thresholds used were able to extract cloudy

pixels as intended. It is worthy noting that the time mentioned here refers to the defined solar

illumination conditions that are occurring over most part of the selected region.

The overall accuracies obtained in this study may depict good performance of the SCM algorithm

developed. However, on superimposing the EUMETSAT CLM on a false colour composite showed

that not all cloudy pixels were correctly screened. Thus a more robust validation method or cloud

mask algorithm may be sought.

5.2. Cloud Height/Type Results

An attempt was made to validate the simple cloud height/type (SCH/T) algorithm using the

EUMETSAT cloud top height (CTH) products also available from EUMETSAT and accessed through

EUMETCast. EUMETSAT CTH products are based on sea surface whereas SCH/T products are

based on the earth surface. Thus digital elevation model (DEM) products, sourced from

ftp://edcftp.cr.usgs.gov/pub/data/gtopo30/global (USGS, 2007) were used to compute the

EUMETSAT CTH products based on the earth surface. Examples of the EUMETSAT cloud top

height and SCH/T images for 25th December 2006 at 11:45 UTC, for a part of African tropical region

and Atlantic Ocean, are given in figure 5-4.


SITU OBSERVATIONS

63

Figure 5-4: EUMETSAT CTH (a), SCH/T (b), and Difference (between CTH and SCH/T) (c) images for 25th

December 2006 at 11:45 UTC (height is in meters)

The results show significant differences in cloud heights for those pixels assigned cloudy by both

EUMETSAT cloud mask products and by the SCM algorithm. The difference image (figure 5-4 (c)) is

also provided. Various other days EUMETSAT CTH products were investigated and the same high

differences were obtained.

However, based on figure 2-1, it can be seen that the EUMETSAT CTH products might be too high.

Cloud height computed using the SCH/T algorithm appears to provide estimates which may be

realistic based on the same figure 2-1. Thus validation with the EUMETSAT CTH products may not

provide reliable results. Further validation of simple cloud height/type algorithm was not carried.

5.3. Rainfall Estimation Results

Understanding that rainfall estimation from one-dimensional cloud-based model technique lacks a

strong physical basis, it was essential to estimate total amount of rainfall from individual storms. This

method provided an idea of how much rainfall a cloud at a certain height can produce. Although this

approach has limitations given the assumptions used (e.g. wind shear over the station does not

(a) (b)

(c)


SITU OBSERVATIONS

64

strongly affect the storm), it is possible to establish cloud height- rainfall relations that may be used as

first approximations. Thus the results would be more general than existing methods, so that the

technique would not be tied to one storm or one climatological regime or one synoptic situation.

As given in figure 4-14, rainfall intensities were plotted against cloud height classes and the clusters

indicates that cloud height between 4000m and 5500m produced rainfall of significant intensities.

Total rainfall from storms can also be seen from figure 4-18 to depict the same trend where high

amounts are within cloud heights of above 4000m and below 5500m.

Diurnal trends (see figure 4-13 and 4-17) of cloud heights and rainfall intensity and/or total rainfall

are rather interesting. They showed that rainfall occurred at the station when the cloud height was

higher than 3000m. This is by no means a coincidence of results of the two days selected for

investigating trends which again confirm the results as shown in figures 4-14 and 4-18.

However, despite clear relationship depicted by the direct cloud height – rainfall intensity plots,

estimated rainfall intensities for other storms using the derived function, was way above the observed

intensities at the station as can be seen from figure 4-16.

From statistical analysis, between observed and estimated total rainfall from other five storms,

correlation coefficient of 0.96, root mean square error of 3.72mm, and a skill score index of 0.23 were

obtained. (Laurent et al., 1998) pointed out that the non-dimensional skill score index, as here applied,

is the relative distance between the estimated values and the observed values and it depends on the

standard deviation (error) of the observed data. Skill score is equal to one when the estimates are

perfect and equal to zero when there is best constant estimates. The skill score obtained here indicates

the estimates were not perfect and that is confirmed from the values as can be seen from table 4-2.

Results presented here are for only a few storms and do not depict general results of all storm

situations that may occur over the station. However, they may give a first approximation of rainfall

amount expected from storms that occur at a certain height. As Heinemann, (2003) pointed out, one of

the major difficulties in relating precipitation observed at a ground station and measured satellite

signals is that the amount of precipitation reaching the ground depends very much on the structure of

the atmospheric layer under the precipitating cloud. This can be said to aggravate the error in the

estimates since the atmospheric layers below the precipitating clouds are not modelled in this study to

incorporate them.

It should also be noted that twelve storms were used to derive the regression function. This may not

be enough to derive regression that may be representative of all types of storms that may occur over

this station. There is a need to use more storms in order to derive a representative regression function.

However, given the data available (from February to August 2006) this was not possible. Besides,

there were several days with no precipitation occurrence given that during this period there was only

one rainfall season over this region.

In order to check whether the results of comparing diurnal change of total rainfall and storm height

were mere coincidence, rainfall data from and independent station were investigated. Data from

Ministry of Water and Irrigation, Naivasha were used. Two days, one with long rainfall records and


SITU OBSERVATIONS

65

0

1000

2000

3000

4000

5000

6000

0:30

3:30

6:30

9:30

12:30

15:30

18:30

21:30

Local time (Hrs)

Clo

ud

heig

ht

(m)

0

0.5

1

1.5

2

2.5

3

3.5

To

tal

rain

fall

(m

m)

Cloud height

Total rainfall

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0:30

3:30

6:30

9:30

12:30

15:30

18:30

21:30

Local time (Hrs)

Clo

ud

heig

ht

(m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

To

tal

rain

fall (

mm

)

Total rainfall

Cloud height

the other without rainfall were chosen. Here the day with rainfall records was 1st March 2006 and the

day without rainfall was 28th October 2006. Simple cloud height (SCH) algorithm was applied to

compute the cloud heights for the two days. Results to this are presented graphically in figure 5-5.

Figure 5-5: Diurnal height and Total rainfall changes on 1st March 2006 (left) and 28th October 2006 (right) over

Naivasha station

As can be observed, rainfall occurred generally when cloud height was slightly higher than 3000m

especially in the afternoon (the shaded part in the left graph). During this time the likely clouds over

the station are convective type of clouds which predominantly occur in the afternoon over this region.

Early in the morning, no rainfall was recorded even though the cloud height was slightly more than

3000m. These are likely to be cirrus clouds which mainly occur after dissipation of convective clouds.

Thus the convective clouds that produced rainfall in the afternoon and in the night must have

dissipated and cirrus clouds appeared in early morning.

The right graph of 28th October 2006 shows that cloud heights above 3000m occurred in the night and

there was no rainfall recorded on this day. This may be attributed to the fact that the rainfall was

measured at a point and that it may have rained away from the rain gauge.

The situation over CGIS station is slightly different as can be observed that on 5th May 2006 rainfall

occurred in the afternoon whereas on 10th May 2006 it occurred in the morning. This means that

convective clouds (the likely clouds producing this rainfall) over this region may be sustained at

various times during the 24 hours period. Nevertheless, the results of Naivasha station and those of

CGIS station are similar in that rainfall is observed when cloud height was more than 3000m.


SITU OBSERVATIONS

66


SITU OBSERVATIONS

67

6. Conclusions and Recommendations

6.1. Conclusions

The main objective is to develop simple cloud mask and height algorithms that can be used for further

studies. As enumerated some of the most important studies include; operational weather forecasting

and energy and water balance studies. Clouds represent the most significant source of error in the

extraction of earth surface energy and water balance parameters out of meteorological satellite data

(Valk et al., 1998). Energy and water balance models are used to estimate fluxes in cloudy conditions.

Thus the focus is to develop a simple cloud mask algorithm in order to be able to accurately develop

other algorithms.

Further to developing simple cloud mask and cloud height algorithms, rainfall estimation is a focus in

this study. Availability of satellite images based on thermal infrared bands is essential and is the first

focal point in this study. In addition to this is the importance of geospatial data on meteorological

parameters that are associated with formation of clouds. Various sources were explored in order to

obtain the long term climatological meteorological data, specifically temperature (minimum,

maximum, and mean).

Firstly, climatological data from the identified sources were used to process input data for the simple

cloud mask algorithm. Secondly, field work campaign was carried for collection of ground rainfall

data that was used for comparison with the processed cloud heights on various satellite images.

During simple cloud mask algorithm development, various thresholds (multi-spectral threshold

technique) were explored in order to optimise on extracting all clouds present on any particular day

and time.

Based on the developed simple cloud mask algorithm, the following comments were drawn:

� Setting thresholds for screening all cloudy pixels in satellite images is the most difficult part

in threshold techniques. The main problem is that the thresholds are functions of many

variables such as; surface type (land, ocean, ice), surface conditions (vegetation, soil

moisture), recent weather (which changes surface temperature and reflectance significantly),

atmospheric conditions (temperature inversions, haze, foggy), season, time of day and even

satellite-earth-sun geometry (hence bidirectional reflectance and sun glint) (Kidder and Haar,

1995).

� An automatic simple cloud mask algorithm has been presented ready for use in other

applications among them those interested in identification of cloudy pixels for the retrieval of

cloud-related parameters (e.g. cloud heights) especially those for clouds which contribute to

rainfall (e.g. cumulonimbus and nimbostratus). Additionally, exclusion of cloudy pixels for

further processing (if required) would be affected by the presence of pixels e.g. for land

surface, ocean colour and aerosol observations. Thus given its aim, a compromise between


SITU OBSERVATIONS

68

calculation speed and accuracy of the results was necessary and use of only three channels of

MSG satellite aimed at developing the simple algorithms as per the study objective.

� The simplicity in the algorithm and significant accuracies based on EUMETSAT data thirsty

cloud mask algorithm, and the possibility of automation into shareware or freeware such as

ILWIS, may greatly improve cloud detection for specifically weather forecasting in most of

the African National Hydrometeorological Services (NHMS). Thus it was envisioned at the

developmental phase that this algorithm would be simple and physically sound and that the

MSG satellite imageries and the necessary processing tools (software e.g. ILWIS) would be

available in these NHMSs in Africa.

Based on the simple cloud height/type (SCH/T) algorithm developed and consequently comparison

with the observed rain gauge data, the following conclusions were drawn:

� That dew point temperature concept can be used to estimate cloud height which can thus be

used to infer rainfall observed on the earth surface. Despite empirical formulation in obtaining

geospatial dew point temperature and replication from a different region (USA Northern

Great Plains), high correlations when comparing rain gauge observations and processed cloud

heights have been obtained.

� That satellite convective rainfall estimation schemes using thermal infrared data depend on

empirically-derived relations between satellite-observed clouds and rainfall. Worse still,

derived relationship from one specific location or climatological regime is not replicable to

another and thus general low correlations between satellite data and rain gauge observations.

� That deriving a concrete regression function for rainfall estimation may be rather difficult

from simple data inputs such as cloud height or even cloud top temperature. This may require

complex model of high computational strength in order to be able to estimate rainfall to a

reasonable accuracy. Besides, earlier studies have shown that unless for strong convection,

there is low correlation between VIS/IR features and precipitation. This is the same reason as

to why rainfall estimates from the derived regression function in this study were of low

accuracies since not all cases were conclusively discerned as convective activities.

� That there is always need for spatial and temporal averaging of satellite data in order to get

better results while comparing point observations on the earth surface

Nevertheless, the author is aware that the small area considered for the validation of the cloud mask

algorithm may not entirely reflect the overall accuracy of the algorithm. However, this gives an

indication of the expected results for specifically equatorial Africa.

6.2. Recommendations

Regarding the research methods and ability to improve in the simple cloud mask (SCM) and cloud

height/type (SCH/T) algorithms, further research can be considered as follows:


SITU OBSERVATIONS

69

� Improving on thresholds tests based on different cloud microphysical processes on formation

of cloud particles.

� Improving on threshold tests based on variables such as surface type, surface conditions,

recent and prevailing weather conditions, and atmospheric conditions.

� Recalibration or deriving a relationship between dew point temperature and readily available

meteorological data e.g. minimum, maximum, and mean temperatures, as suggested by

Hubbard et al., (2003) for any region under consideration.

On the part of rainfall estimation/comparison method, further research can be considered as follows:

� Improving on rainfall estimation scheme by determining the environment of the convection

(in cases where rainfall is assumed to emanate from convective activities) in terms of

temperature, moisture and wind shear.

� Developing a model that is characterised by the significant transience, heterogeneity, and

variability to associate rainfall with the extremely complex and yet imperfectly understood

precipitating processes in order to produce higher quality estimates as suggested by Hong et

al., (2004).

The SCM and SCH/T algorithms seem to work well, but they will benefit a lot from a more thorough

validation method. SYNOP data could be used for this purpose. However, as stated by Casanova et

al., (2004) subjectivity of the meteorological observer, which in most cases depends on expertise as

well as experience, is an issue while using SYNOP as a validation method.

Last but not least, final recommendations for development of cloud mask and cloud height algorithms

would be that; having observed that earth surface features dictates setting of threshold tests, there is a

need to develop cloud mask algorithm that will consider all these features. In addition, it is

recommended that rainfall estimation/comparison method be based on accurately known cloud

formation processes in order to integrate with one-dimensional physical cloud models.


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70

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Appendices

Appendix A: ILWIS Script for Simple Cloud Mask and Height Algorithms

// STEP 1: DATA IMPORT //

// Edit batch file to select date and time for thermal bands (Band IR_039, Band IR_108 and Band

//IR_120, imported in Kelvin)

! import_t.bat

! import_tt.bat

! import_ttt.bat

! import_tttt.bat

// Edit batch file to select date and time for visual bands (band VIS006, Band VIS008 and Band

//VIS016, imported as 8 bit)

//Only used for visual inspection of the segments that result from processing cloud heights

! import_v.bat

// Filling the undefined pixels in thermal band 4 (IR_039) by setting minimum brightness

//temperature at 204K

T1_bd39:=iff((T1_band_1<204),204,T1_band_1)

T1_band_1:=ifundef(T1_bd39,204)


SITU OBSERVATIONS

74







//Assigning domain values of original TIR images for each band to avoid undefines

T1_band1{dom=value;vr=0:1000:0.0001}:=T1_band_1.mpr












//Map list with assigned domains is created

crmaplist TR1 T1_band1 T1_band2 T1_band3




// Taking temporal average //

AvgT.mpl = maplistcalculate("(@1+@2 +@3 +@4)/4",0,2,TR1.mpl,TR2.mpl,TR3.mpl,TR4.mpl )

open AvgT.mpl

// GENERATION OF SATELLITE AND SUN ANGLES //

//Calculate satellite, sun zenith angle and sun elevation, for MSG projection

! generateangles.bat


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75

copy satzen*.* msgzen

copy sunzen*.* solzen

msgzen:=map('msgzen',genras,Convert,378,0,Real,4,SwapBytes)

solzen:=map('solzen',genras,Convert,378,0,Real,4,SwapBytes)

setgrf msgzen.mpr angle

setgrf solzen.mpr angle

msg_zenres.mpr{dom=value.dom;vr=-1000:1000:0.001}:= MapResample(msgzen.mpr,%8,bicubic)

sec_msgzen.mpr{dom=value.dom;vr=0:10:0.001}:=(1/(cos(degrad(msg_zenres))))-1

sol_zenres.mpr{dom=value.dom;vr=-1000:1000:0.001}:= MapResample(solzen.mpr,%8,bicubic)

sec_solzen.mpr{dom=value.dom;vr=0:10:0.001}:=(1/(cos(degrad(sol_zenres))))-1

sun_elev.mpr{dom=value.dom;vr=-1000:1000:0.001}:=90-sol_zenres

// Calculating solar illumination conditions

illum_cond%7.mpr{dom=sol_cond}:=iff(sun_elev<-3,"night",iff(sun_elev<10,"twilight","day"))

// Delete unused files generated in the process of generating angles

!del_file.bat

// Boolean images created to be used in multiplying resulting images in every step

Day:=iff((sun_elev>10),1,0)

Night:=iff((sun_elev<-3),1,0)

Twilight:=iff((sun_elev<10) and (sun_elev>-3),1,0)

//Aggregating (spatial averaging) the retrieved TIR images//

Avg.mpl:=MapListApplic(AvgT, MapAggregateAggFnc(##, 5, nogroup))

open Avg.mpl

closeall

//STEP 2: CLOUD MASKING //

// (1) DAY TIME CLOUD MASK //

// Dew point calculation (temperature layers input in Kelvin)

// Formula according to Hubbard, Mahmood and Carlson (2003): Estimating daily dew point

// temperature for the northern Great Plains using maximum and minimum temperature, Agron J.

//95:323-328 (2003)

// Td=-0.0360 (T-mean) +0.96789(T-min) +0.0072(T-max-T-min) +1.0119 (in degree Celsius)


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76

Td_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-

273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Night


273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Twilight


273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Day

// Mean T_b of band 10.8 and band 12.0 (input in Kelvin)

b_mean_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Night

b_mean_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Twilight

b_mean_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Day

// Standard deviation of the climatological maps (t_nightall2f, t_dayall2f, and t_meanall2f)

t_std.mpr{dom=VALUE.dom;vr=-10000000.0:10000000.0:0.1}= MapMaplistStatistics(t_clim.mpl,

Std, 0, 2)

// Derive cloud mask according to the MSG algorithm in the Météo-France, Atlantic Sea Surface

//temperature product manual, Version 1.5, Nov 2005 (SAF/OSI/M-F/TEC/MA/121)

Ts_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_D-273.15)+(1.18116*

(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Night


(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Twilight


(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Day

// Cloud mask using fine scale climatology as in SAF/OSI/M-F/TEC/MA/121

deltat_D.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_D)*Night

deltat_D.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-

Ts_D)*Twilight

deltat_D.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_D)*Day

// Pixels with small difference (i.e. deltat_D) are masked

Sc_Clim.mpr{dom=BOOL.dom;vr=0:1} = iff((deltat_D>-1)or(deltat_D>0.5)or(deltat_D>2),1,0)

Sc_Clim1.mpr{dom=BOOL.dom;vr=0:1} =Sc_Clim*Night

Sc_Clim1.mpr{dom=BOOL.dom;vr=0:1} =Sc_Clim*Twilight

Sc_Clim1.mpr{dom=BOOL.dom;vr=0:1} =Sc_Clim*Day

CL_1.mpr{dom=BOOL.dom;vr=0:1} = iff((%4_2<293.15) and (Sc_Clim1>0),1,0)


SITU OBSERVATIONS

77

CL_1_1.mpr{dom=BOOL.dom;vr=0:1} =CL_1*Night

CL_1_1.mpr{dom=BOOL.dom;vr=0:1} =CL_1*Twilight

CL_1_1.mpr{dom=BOOL.dom;vr=0:1} =CL_1*Day

CL_2.mpr{dom=BOOL.dom;vr=0:1} = iff((CL_1>0)and(%4_2<(%2-(t_std/2))),1,0)

CL_2_2.mpr{dom=BOOL.dom;vr=0:1} = CL_2*Night

CL_2_2.mpr{dom=BOOL.dom;vr=0:1} = CL_2*Twilight

CL_2_2.mpr{dom=BOOL.dom;vr=0:1} = CL_2*Day

cloud_mask_D.mpr{dom=BOOL.dom;vr=0:1} = MapFilter(CL_2_2.mpr,MAJORITY.fil)

// (2) NIGHT TIME CLOUD MASK //



// temperature for the northern Great Plains using maximum and minimum temperature, Agron J.

//95:323-328 (2003)

// Td=-0.0360 (T-mean)+0.96789(T-min)+0.0072(T-max-T-min)+1.0119 (in degree Celsius)

Td_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-

273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Day


273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Twilight


273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Night


b_mean_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Day

b_mean_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Twilight

b_mean_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Night


t_std.mpr{dom=VALUE.dom;vr=-10000000.0:10000000.0:0.1}= MapMaplistStatistics(t_clim.mpl,

Std, 0, 2)



Ts_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_N-273.15)+(1.18116*

(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Day


SITU OBSERVATIONS

78




(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Night


deltat_N.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_meanall2f-Ts_N)*Day

deltat_N.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01}=(t_meanall2f-

Ts_N)*Twilight

deltat_N.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_meanall2f-Ts_N)*Night

// Step A: Land Surface cloud mask only, <t_std greater than 1>

// Cloud mask adaptation step A: iff IR_108<283.15 Kelvin (10 degrees Celsius) in combination with

//positive values from the previous step results

// larger threshold for <deltat_N> is used to exclude cooler areas

CL_N1.mpr{dom=BOOL.dom;vr=0:1}:=iff(((%6>1)and((b_mean_N<283.15)and

(deltat_N>10))),1,0)

CL_NN1.mpr{dom=BOOL.dom;vr=0:1}:=CL_N1*Day

CL_NN1.mpr{dom=BOOL.dom;vr=0:1}:=CL_N1*Twilight

CL_NN1.mpr{dom=BOOL.dom;vr=0:1}:=CL_N1*Night

// Step B: temperature threshold sea surface only <t_std less than 1> and difference between

//T-min and actual IR_108 temperature greater than 9 Kelvin

CL_N2.mpr{dom=BOOL.dom;vr=0:1} = iff((%6<1)and((%1-%4_2)>9),1,0)

CL_NN2.mpr{dom=BOOL.dom;vr=0:1} =CL_N2*Day

CL_NN2.mpr{dom=BOOL.dom;vr=0:1} =CL_N2*Twilight

CL_NN2.mpr{dom=BOOL.dom;vr=0:1} =CL_N2*Night

// Step C: Low cloud mask over the sea (Normalizing IR_108 and IR_120 with IR_039 by multiplying

//temperature of the two bands) results in scaled values whose difference with the mean of IR_108 and

//IR_120 indicates presence of low clouds and (dif>2K).

ratall_N.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Day

ratall_N.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Twilight

ratall_N.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Night

difnew_N.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_N-b_mean_N)*Day


SITU OBSERVATIONS

79

difnew_N.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_N-b_mean_N)*Twilight

difnew_N.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_N-b_mean_N)*Night

CL_N3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=iff((%6<1)and((difnew_N)>2),1,0)

CL_NN3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_N3*Day

CL_NN3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_N3*Twilight

CL_NN3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_N3*Night

// Add all corrected cloud masks from previous steps

CL_N4.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_NN1+CL_NN2+CL_NN3

// Transform to boolean map

CL_N5{dom=bool}:=iff(CL_N4>0,1,0)

// Final cloud mask is being filtered using 3x3 majority filter removing individual pixels and assigning

//1 or 0 (true - false) domain

cloud_mask_N.mpr{dom=BOOL.dom;vr=0:1} = MapFilter(CL_N5.mpr,MAJORITY.fil)

// (3) TWILIGHT TIME CLOUD MASK //



// temperature for the northern Great Plains using maximum and minimum temperature, Agron J. //

//95:323-328(2003)

// Td=-0.0360 (T-mean)+0.96789(T-min)+0.0072(T-max-T-min)+1.0119 (in degree Celsius)

Td_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-

273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Night


273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Day


273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Twilight


b_mean_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Night

b_mean_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Day

b_mean_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Twilight


SITU OBSERVATIONS

80


t_std.mpr{dom=VALUE.dom;vr=-10000000.0:10000000.0:0.1} = MapMaplistStatistics(t_clim.mpl,

Std, 0, 2)



Ts_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_T-273.15)+(1.18116*

(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Night


(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Day




deltat_T.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_T)*Night

deltat_T.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_T)*Day

deltat_T.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-

Ts_T)*Twilight

// Step A: deltat has positive values for clouds and negative values if no clouds

// Land cloud mask only, <t_std greater than 1>

//cloud mask adaptation step A: iff IR_108<283.15 Kelvin (10 degrees Celsius) in combination with

//positive values from the previous step results in clouds

// Medium threshold for <deltat_T> is used to exclude cooler areas

CL_T1.mpr{dom=BOOL.dom;vr=0:1}=iff(((%6>1)and((b_mean_T<283.15)and (deltat_T>5))),1,0)

CL_TT1.mpr{dom=BOOL.dom;vr=0:1} = CL_T1*Night

CL_TT1.mpr{dom=BOOL.dom;vr=0:1} = CL_T1*Day

CL_TT1.mpr{dom=BOOL.dom;vr=0:1} = CL_T1*Twilight

// Step B: temperature threshold sea surface only <t_std less than 1> and difference between

//T-min and actual IR_108 temperature greater than 9 Kelvin

CL_T2.mpr{dom=BOOL.dom;vr=0:1} = iff((%6<1)and((%1-%4_2)>9),1,0)

CL_TT2.mpr{dom=BOOL.dom;vr=0:1} = CL_T2*Night

CL_TT2.mpr{dom=BOOL.dom;vr=0:1} = CL_T2*Day

CL_TT2.mpr{dom=BOOL.dom;vr=0:1} = CL_T2*Twilight


SITU OBSERVATIONS

81

// Step C: Low cloud mask over the sea (Normalizing IR_108 and IR_120 with IR_039 by multiplying

//temperature of the two bands) results in scaled values whose difference with the mean of IR_108 and

//IR_120 indicates presence of low clouds and (dif<2K).

ratall_T.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Night

ratall_T.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Day

ratall_T.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Twilight

difnew_T.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_T-b_mean_T)*Night

difnew_T.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_T-b_mean_T)*Day

difnew_T.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_T-b_mean_T)*Twilight

CL_T3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=iff((%6<1)and((difnew_T)<2),1,0)

CL_TT3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:= CL_T3*Night

CL_TT3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:= CL_T3*Day

CL_TT3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:= CL_T3*Twilight

// Add all corrected cloud masks from previous steps

CL_T4.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_TT1+CL_TT2+CL_TT3

// Transform to boolean map

CL_T5{dom=bool}:=iff(CL_T4>0,1,0)

// Final cloud mask is being filtered using 3x3 majority filter removing individual pixels and assigning

//1 or 0 (true - false) domain

cloud_mask_T.mpr{dom=BOOL.dom;vr=0:1} = MapFilter(CL_T5.mpr,MAJORITY.fil)

// STEP 3: COMBINING ALL MASKED CLOUDS //

Day_clouds.mpr{dom=BOOL.dom;vr=0:1} = iff(illum_cond%7="day",cloud_mask_D,?)

Night_clouds.mpr{dom=BOOL.dom;vr=0:1} = iff(illum_cond%7="night",cloud_mask_N,?)

Twilight_clouds.mpr{dom=BOOL.dom;vr=0:1}= iff(illum_cond%7="twilight",cloud_mask_T,?)

// Total clouds in the field of view

Final_clouds.mpr{dom=BOOL.dom;vr=0:1}=

iff((Day_clouds>0)or(Night_clouds>0)or(Twilight_clouds>0),1,0)

// Clouds at different times of the day

Final_clouds_D.mpr{dom=BOOL.dom;vr=0:1} = Final_clouds*Day

Final_clouds_N.mpr{dom=BOOL.dom;vr=0:1} = Final_clouds*Night

Final_clouds_T.mpr{dom=BOOL.dom;vr=0:1} = Final_clouds*Twilight


SITU OBSERVATIONS

82

// STEP 4: CLOUD HEIGHT CALCULATION //

// (1) DAY TIME //

// For the pixels assigned clouds the cloud height is derived:

// using the wet adiabatic lapse rate (0.6 degree per 100 meter): dew point temperature - average

//IR_108 and IR_120 temperature * 0.6 *100

// using dry adiabatic lapse rate (1 degree per 100 meter): T-max - dew point temperature * 100

Height_D.mpr{dom=value.dom;vr=0:30000}:=iff(Final_clouds_D=true,(((Td_D-

b_mean_D)*0.6)*100)+((%2-Td_D)*100),?)

// Calculate cloud height above 50m to avoid false assignment in low levels

Height_Dab50.mpr{dom=VALUE.dom;vr=-10000000:10000000}= iff(Height_D>50,Height_D,?)

// Transform the heights computed into different height classes

Height_CLS_D.mpr{dom=Final_cls}:=iff(Height_Dab50<1500,"Low

clouds",iff(Height_Dab50<3000,"Middle clouds","High clouds"))

// (2) NIGHT TIME //

// For the pixels assigned clouds the cloud height is derived,




Height_N.mpr{dom=value.dom;vr=0:30000}:=iff(Final_clouds_N=true,(((Td_N-

b_mean_N)*0.6)*100)+((%2-Td_N)*100),?)


Height_Nab50.mpr {dom=VALUE.dom;vr=-10000000:10000000}= iff(Height_N>50,Height_N,?)


Height_CLS_N.mpr{dom=Final_cls}:=iff(Height_Nab50<1500,"Low

clouds",iff(Height_Nab50<3000,"Middle clouds","High clouds"))

// (3) TWILIGHT TIME //

// For the pixels assigned clouds the cloud height is derived,





SITU OBSERVATIONS

83

Height_T.mpr{dom=value.dom;vr=0:30000}:=iff(Final_clouds_T=true,(((Td_T-

b_mean_T)*0.6)*100)+((%2-Td_T)*100),?)


Height_Tab50.mpr{dom=VALUE.dom;vr=-10000000:10000000}= iff(Height_T>50,Height_T,?)


Height_CLS_T.mpr{dom=Final_cls}:=iff(Height_Tab50<1500,"Low

clouds",iff(Height_Tab50<3000,"Middle clouds","High clouds"))

// STEP 5: GLUING THE HEIGHT IMAGES TOGETHER //

Height_%9_%7.mpr{dom=VALUE.dom;vr=-10000000:10000000}:=

MapGlue(Height_Dab50,Height_Nab50,Height_Tab50)

// STEP 6: GLUING THE DIFFERENT HEIGHT CLASSES TOGETHER//

Height_cls_glued%9_%7.mpr{dom=Final_cls.dom}=MapGlue(Height_CLS_D,Height_CLS_N,

Height_CLS_T)

// STEP 7: SEGMENTATION OF THE GLUED HEIGHT CLASSES //

Final_cloud_seg%9_%7.mps:=SegmentMapFromRasAreaBnd(Height_Cls%9_%7,8,Smooth,unique)


SITU OBSERVATIONS

84

Appendix B: Samples of Batch Files

! import_t.bat (Batch file for the first image)

Similarly:

! import_tt.bat (Batch file for the second image)

D:\MSGDataRetriever\gdal_translate.exe --config GDAL_CACHEMAX 30 -projwin -2643355.2 3759505.2

4707632.6 -3681494.7 -of ILWIS MSG(\\Pc2133-24002\Rawdata20051101\,200603071515,(4,9,10),N,T,1,1)

D:\MSG_CGIS_Avgt_s\T2

! import_ttt.bat (Batch file for the third image)




! import_tttt.bat (Batch file for the fourth image)




! import_v (Batch file for the visible bands-1, 2, and 3)


4707632.6 -3681494.7 -of ILWIS MSG(\\Pc2133-24002\Rawdata20051101\,200603071530,(1,2,3),N,B,1,1)

D:\MSG_CGIS_Avgt_s\V

! generateangles.bat (Batch file for generating satellite and solar angles - works in java

environment)

java -cp .;operation.jar;Jama-1.0.1.jar;numericalMethods.jar AngleMaps 2006 03 07 15.30

! del_file.bat (Batch file for deleting unused files generated by the generate angles batch file)

del sataz*.*

del sunaz*.*

del satzen*.*

del sunzen*.*

del msgzen

del solzen

Projection

window in

the MSG

field of

view

External

drive where

the data is

located

Year-month-

date-time

(yyyymmdd

hhmm)

Bands 4,

9, and 10

Folder to which

the data is to be

stored and the

name of data

file

METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS

85

Appendix C: Sample of CGIS Weather Station Data

TIM

ES

TA

MP

(Lo

cal

Tim

e)

(H

rs)

RE

CO

RD

RN

TA

_A

vg

deg

C

Av

g

RH

_A

vg

%

Av

g

VP

_A

vg

kP

a

Av

g

RIS

_A

vg

W/m

2

Av

g

PA

R_

T_

Av

g

my

mo

l/(m

2*

s)

Av

g

PA

R_

D_

Av

g

my

mo

l/(m

2*

s)

Av

g

Su

n_

stat

e_A

vg

Fra

ctio

n

Av

g

WS

_M

ax

m/s

Max

WS

_M

in

m/s

Min

WS

_W

Vc(

1)

m/s

WV

c

WS

_

WV

c(

2)

m/s

WV

c

P_

To

t

mm

To

t

Bat

t_v

olt

_M

in

Min

PT

emp

Sm

p

2/27/2006

17:30 96 21.35 60.57 1.539 108.9 240.1 219.8 0 0 0 0 0 0 13.8 24.1

2/27/2006

18:00 97 21.02 61.23 1.524 42.34 100.6 94.9 0 0 0 0 0 0 13.14 22.7

2/27/2006

18:30 98 20.64 63.02 1.532 5.221 23.79 20.57 0 0 0 0 0 0 12.99 21.15

2/27/2006

19:00 99 19.96 68.36 1.594 0.013 12.23 12.81 0 0 0 0 0 0.4 12.95 19.84

2/27/2006

19:30 100 19.54 70.03 1.59 0.04 12.8 14.03 0 0 0 0 0 0 12.94 18.92

2/27/2006

20:00 101 19.11 72.5 1.603 0.067 12.44 12.24 0 0 0 0 0 0 12.92 18.12

2/27/2006

20:30 102 18.84 72.78 1.583 -0.027 15.33 14.49 0 0 0 0 0 0 12.91 17.54

2/27/2006

21:00 103 18.88 72.66 1.584 0.027 14.78 14.59 0 0 0 0 0 0 12.9 17.21

2/27/2006

21:30 104 18.57 77.07 1.648 0 14.26 13.99 0 0 0 0 0 0 12.89 16.91

2/27/2006

22:00 105 17.79 82 1.67 0.054 14.19 13.73 0 0 0 0 0 0 12.88 16.59

2/27/2006

22:30 106 17.67 82.6 1.669 0.027 15.19 14.97 0 0 0 0 0 0 12.87 16.29

2/27/2006

23:00 107 17.81 81 1.651 0.107 13.32 14.43 0 0 0 0 0 0 12.86 16.09

2/27/2006

23:30 108 18.01 78.32 1.616 0.013 14.75 15.04 0 0 0 0 0 0 12.86 15.97

2/28/2006

0:00 109 17.79 79.7 1.622 -0.013 14.95 14.01 0 0 0 0 0 0 12.85 16


86

2/28/2006

0:30 110 18.2 76.65 1.601 -0.067 15.16 14.18 0 0 0 0 0 0 12.85 16.29

2/28/2006

1:00 111 17.86 79.39 1.623 0.013 14.33 14.5 0 0 0 0 0 0 12.85 16.46

2/28/2006

1:30 112 17.4 82.1 1.631 0.121 14.47 14.45 0 0 0 0 0 0 12.85 16.39

2/28/2006

2:00 113 17.36 81.6 1.618 0.08 14.46 14.29 0 0 0 0 0 0 12.84 16.32

2/28/2006

2:30 114 17.47 80.1 1.597 -0.067 13.86 14.08 0 0 0 0 0 0 12.83 16.24

2/28/2006

3:00 115 17.09 83.9 1.634 0.027 14.62 14.37 0 0 0 0 0 0 12.83 16.19

2/28/2006

3:30 116 16.87 85.1 1.634 0.013 14.53 14.54 0 0 0 0 0 0 12.83 15.97

2/28/2006

4:00 117 16.62 86.1 1.626 0.121 13.95 14.48 0 0 0 0 0 0 12.83 15.65

2/28/2006

4:30 118 16.93 82.8 1.596 0.054 14.36 14.02 0 0 0 0 0 0 12.83 15.24

2/28/2006

5:00 119 16.31 87.6 1.623 0.054 14.5 14.42 0 0 0 0 0 0 12.82 15.04

2/28/2006

5:30 120 16.35 86.6 1.609 0 14.38 13.97 0 0 0 0 0 0 12.82 14.9

2/28/2006

6:00 121 16.1 88.4 1.616 0.201 14.47 14.45 0 0 0 0 0 0 12.82 14.78

2/28/2006

6:30 122 15.84 88.2 1.585 13.28 31.1 29.36 0 0 0 0 0 0 12.81 14.44

2/28/2006

7:00 123 16.5 83.9 1.573 51 114.3 97.9 0 0 0 0 0 0 12.83 14.66

2/28/2006

7:30 124 16.84 83.8 1.606 114 237 226.6 0 0 0 0 0 0 13.07 15.65

2/28/2006

8:00 125 18.12 80.9 1.682 149.1 317.3 314.4 0 0 0 0 0 0 13.61 17.14

2/28/2006

8:30 126 19.57 73.67 1.675 261.1 534.9 492.8 0 0 0 0 0 0 13.92 19.42

2/28/2006

9:00 127 20.9 67.47 1.667 453.9 892 703.1 0 0 0 0 0 0 13.84 22.87

2/28/2006 128 21.03 64.53 1.607 264.1 562.3 530.3 0 0 0 0 0 0 13.82 23.93


87

9:30

2/28/2006

10:00 129 21.44 61.33 1.566 399 810 737 0 0 0 0 0 0 13.79 24.92

2/28/2006

10:30 130 21.37 66.43 1.689 280 587 569.9 0 0 0 0 0 0 13.79 25.22

2/28/2006

11:00 131 20.24 75.69 1.791 180.1 385.9 382.4 0 0 0 0 0 0 13.79 24.3

2/28/2006

11:30 132 19.2 81.9 1.823 194.1 419.4 412.3 0 0 0 0 0 0 13.82 23.24

2/28/2006

12:00 133 20.09 78.51 1.845 281.3 593.3 581.4 0 0 0 0 0 0 13.84 23.24

2/28/2006

12:30 134 21.64 64.22 1.658 527.5 1080 1052 0 0 0 0 0 0 13.79 25.28

2/28/2006

13:00 135 22.9 59 1.646 748.5 1518 1133 0 0 0 0 0 0 13.74 27.59

2/28/2006

13:30 136 23.95 54.99 1.635 697 1389 1163 0 0 0 0 0 0 13.69 29.92

2/28/2006

14:00 137 23.11 58.73 1.66 251.5 519.8 509.6 0 0 0 0 0 0 13.68 29.22

2/28/2006

14:30 138 21.9 63.13 1.655 73.24 179.5 175.2 0 0 0 0 0 0 13.7 26.47

2/28/2006

15:00 139 19.27 82.4 1.833 19.57 66.67 63.91 0 0 0 0 0 1.2 13.03 23.21

2/28/2006

15:30 140 15.67 95 1.69 9.81 42.47 40.45 0 0 0 0 0 4.8 12.98 19.97

2/28/2006

16:00 141 15.12 95.5 1.64 16.3 56.4 54.61 0 0 0 0 0 2 12.97 17.99

2/28/2006

16:30 142 15.49 92.8 1.631 39.47 101.4 99.7 0 0 0 0 0 0.2 13.01 17.01

2/28/2006

17:00 143 16.14 88.5 1.623 40.14 94.9 93.8 0 0 0 0 0 0.2 13.16 16.54

2/28/2006

17:30 144 16.58 85 1.603 34.44 79.12 78.91 0 0 0 0 0 0 13.12 16.24

2/28/2006

18:00 145 16.94 84.4 1.628 18.81 45.97 43.65 0 0 0 0 0 0 12.94 16.17

2/28/2006

18:30 146 17.04 85.8 1.666 1.863 14.62 15.17 0 0 0 0 0 0 12.9 16.12


88

2/28/2006

19:00 147 17.02 87.9 1.703 0.027 14.99 14.01 0 0 0 0 0 0 12.88 16.04

2/28/2006

19:30 148 17.27 86.4 1.7 0.147 14.02 14.64 0 0 0 0 0 0 12.88 15.97

2/28/2006

20:00 149 17.79 82.5 1.681 0.04 14.75 14.37 0 0 0 0 0 0 12.87 15.97

2/28/2006

20:30 150 17.73 81.3 1.648 -0.054 15 16.19 0 0 0 0 0 0 12.86 15.92

2/28/2006

21:00 151 17.9 76.9 1.577 0.067 13.3 14.58 0 0 0 0 0 0 12.86 15.75

2/28/2006

21:30 152 17.79 76.48 1.557 0.04 14.52 14.2 0 0 0 0 0 0 12.85 15.75

2/28/2006

22:00 153 17.79 76.28 1.552 -0.013 14.52 14.29 0 0 0 0 0 0 12.85 15.82

2/28/2006

22:30 154 18 73.94 1.525 0.013 13.71 14.33 0 0 0 0 0 0 12.84 15.9

2/28/2006

23:00 155 18.14 71.57 1.49 0.04 14.14 14.66 0 0 0 0 0 0 12.84 16.04

2/28/2006

23:30 156 18.05 70.54 1.46 0.094 14.41 14.51 0 0 0 0 0 0 12.83 16.02

3/1/2006

0:00 157 17.93 71.16 1.461 -0.027 14.62 14.21 0 0 0 0 0 0 12.83 16.09

3/1/2006

0:30 158 18.13 70.02 1.456 0 14.73 14.22 0 0 0 0 0 0 12.83 16.32

3/1/2006

1:00 159 17.56 75.27 1.509 0.08 13.62 14.11 0 0 0 0 0 0 12.83 16.17

3/1/2006

1:30 160 16.64 81.1 1.535 -0.013 14.66 14.55 0 0 0 0 0 0 12.82 15.75

3/1/2006

2:00 161 16.21 86.1 1.585 0.054 14.65 13.93 0 0 0 0 0 0 12.82 15.41

3/1/2006

2:30 162 15.62 92.6 1.642 0 13.96 14.47 0 0 0 0 0 0 12.82 15.02

3/1/2006

3:00 163 15.27 95.2 1.65 0.147 14.23 13.97 0 0 0 0 0 0 12.81 14.68

3/1/2006

3:30 164 15.36 94.6 1.649 0.013 14.53 13.94 0 0 0 0 0 0 12.81 14.52

3/1/2006 165 15.56 93.8 1.656 0.013 15.27 15.3 0 0 0 0 0 0 12.8 14.47


89

4:00

3/1/2006

4:30 166 15.49 93.4 1.643 -0.08 13.57 13.34 0 0 0 0 0 0 12.8 14.47

3/1/2006

5:00 167 15.49 93.3 1.641 0.054 14.36 14.74 0 0 0 0 0 0 12.8 14.52

3/1/2006

5:30 168 15.43 94.1 1.648 0.08 14.46 14.42 0 0 0 0 0 0 12.79 14.56

3/1/2006

6:00 169 15.38 94.7 1.654 0.121 14.6 13.49 0 0 0 0 0 0 12.79 14.56

3/1/2006

6:30 170 15.27 94.8 1.644 5.817 19.05 18.71 0 0 0 0 0 0 12.79 14.44

3/1/2006

7:00 171 15.38 94.7 1.655 26.39 54.58 52.65 0 0 0 0 0 0 12.8 14.49

3/1/2006

7:30 172 15.79 93.7 1.68 60.65 118.5 116.2 0 0 0 0 0 0 12.93 14.88

3/1/2006

8:00 173 16.53 91 1.71 135.4 264.5 262.8 0 0 0 0 0 0 13.32 15.8

3/1/2006

8:30 174 16.94 88.8 1.714 190 398.3 390.8 0 0 0 0 0 0 13.86 17.01

3/1/2006

9:00 175 17.29 86.3 1.702 247.5 515 508.2 0 0 0 0 0 0 13.95 18.33

3/1/2006

9:30 176 17.65 87.9 1.773 318.5 655.5 651.1 0 0 0 0 0 0 13.92 19.76

3/1/2006

10:00 177 18.01 85.6 1.768 291.7 606.1 601.2 0 0 0 0 0 0 13.9 20.88

3/1/2006

10:30 178 18.49 82 1.743 454.8 919 881 0 0 0 0 0 0 13.87 22.22

3/1/2006

11:00 179 20.36 70.09 1.673 789.5 1567 1090 0 0 0 0 0 0 13.8 25.34

3/1/2006

11:30 180 20.86 67.35 1.66 700.8 1396 1027 0 0 0 0 0 0 13.74 27.69

3/1/2006

12:00 181 20.83 65.85 1.62 483.4 966 949 0 0 0 0 0 0 13.73 27.82

3/1/2006

12:30 182 21.21 64.31 1.62 422.9 845 841 0 0 0 0 0 0 13.74 27.53

3/1/2006

13:00 183 21.57 66.74 1.718 602.2 1224 1047 0 0 0 0 0 0 13.74 28.01


90

3/1/2006

13:30 184 22.75 61.11 1.689 923 1855 1223 0 0 0 0 0 0 13.69 30.05

3/1/2006

14:00 185 23.47 58.13 1.68 633.3 1285 1182 0 0 0 0 0 0 13.66 30.95

3/1/2006

14:30 186 23.7 55.84 1.636 603.3 1272 1091 0 0 0 0 0 0 13.65 31.51

3/1/2006

15:00 187 23.74 55.36 1.626 560.7 1183 1019 0 0 0 0 0 0 13.65 31.62

3/1/2006

15:30 188 22.81 65.4 1.816 314.9 660.1 560.1 0 0 0 0 0 0 13.65 30.46

3/1/2006

16:00 189 22.24 66.99 1.796 111.4 242.3 235.6 0 0 0 0 0 0 13.69 28.01

3/1/2006

16:30 190 20.77 73.3 1.792 18.06 46.11 44.32 0 0 0 0 0 1.6 13.01 25.22

3/1/2006

17:00 191 17.1 81.6 1.591 13.25 42.1 40.68 0 0 0 0 0 0.2 12.98 21.26

3/1/2006

17:30 192 16.91 89.1 1.716 20.23 58.35 55.64 0 0 0 0 0 0 12.97 19.36

3/1/2006

18:00 193 17.29 85.7 1.689 11.29 40.74 38.16 0 0 0 0 0 0 12.93 18.33

3/1/2006

18:30 194 17.63 78.76 1.587 2.384 15.68 16.8 0 0 0 0 0 0 12.91 17.59

3/1/2006

19:00 195 17.22 83.1 1.63 0.067 16.11 18.32 0 0 0 0 0 0 12.9 16.94

3/1/2006

19:30 196 17.06 83.5 1.623 0.04 13.28 14.89 0 0 0 0 0 0 12.88 16.51

3/1/2006

20:00 197 17.34 80.3 1.589 0.121 14.81 13.92 0 0 0 0 0 0 12.87 16.36

3/1/2006

20:30 198 17.56 78.97 1.584 0.054 14.37 14.47 0 0 0 0 0 0 12.86 16.24

3/1/2006

21:00 199 17.27 81.6 1.608 -0.04 14.08 14.99 0 0 0 0 0 0 12.85 16

3/1/2006

21:30 200 16.92 84.4 1.625 0.027 14.32 14.58 0 0 0 0 0 0 12.85 15.7

3/1/2006

22:00 201 17.14 83 1.621 -0.067 14.29 14.63 0 0 0 0 0 0 12.84 15.48

3/1/2006 202 16.54 85.6 1.61 0.067 13.88 13.61 0 0 0 0 0 0 12.83 15.07


91

22:30

3/1/2006

23:00 203 15.97 89 1.614 0.04 14.38 14.45 0 0 0 0 0 0 12.83 14.68

3/1/2006

23:30 204 16.35 85.6 1.59 0.013 14.37 13.99 0 0 0 0 0 0 12.82 14.4

3/2/2006

0:00 205 16.64 84.1 1.592 0.013 14.18 15.23 0 0 0 0 0 0 12.82 14.3

3/2/2006

0:30 206 16.34 86.2 1.601 -0.04 14.18 12.82 0 0 0 0 0 0 12.81 14.13

3/2/2006

1:00 207 16.45 84.7 1.585 0.107 14.6 14.58 0 0 0 0 0 0 12.81 14.01

3/2/2006

1:30 208 16 87.8 1.595 0.067 14.21 14.58 0 0 0 0 0 0 12.8 13.9

3/2/2006

2:00 209 15.93 87.5 1.582 0.08 14.38 14.05 0 0 0 0 0 0 12.79 13.82

3/2/2006

2:30 210 15.35 91.2 1.589 0.054 14.25 14.21 0 0 0 0 0 0 12.79 13.66

3/2/2006

3:00 211 14.97 92.7 1.577 0.08 14.46 14.48 0 0 0 0 0 0 12.79 13.42

3/2/2006

3:30 212 14.5 94.5 1.56 0.067 13.67 13.93 0 0 0 0 0 0 12.79 13.19

3/2/2006

4:00 213 14.83 92.9 1.566 0.013 15 14.55 0 0 0 0 0 0 12.78 13.05

3/2/2006

4:30 214 14.49 94.9 1.565 0.054 14.06 14.12 0 0 0 0 0 0 12.78 12.98

3/2/2006

5:00 215 14.47 94.2 1.551 0.08 14.55 14.66 0 0 0 0 0 0 12.78 12.98

3/2/2006

5:30 216 14.44 93.2 1.531 -0.027 13.94 13.38 0 0 0 0 0 0 12.78 12.77

3/2/2006

6:00 217 14.1 94.9 1.526 0.255 14.42 14.09 0 0 0 0 0 0 12.77 12.56

3/2/2006

6:30 218 13.87 94.9 1.504 15.79 35.93 33.38 0 0 0 0 0 0 12.77 12.42

3/2/2006

7:00 219 14.64 89.4 1.487 124.7 180.3 148.2 0 0 0 0 0 0 12.85 13

3/2/2006

7:30 220 16.91 80.5 1.55 267.4 467.5 311 0 0 0 0 0 0 13.6 15.46


SITU OBSERVATIONS

92

Appendix D: Storms over CGIS Weather Station used for Developing Cloud

height – Rainfall Intensity Regression Function

Storm 1 (28/02/2006: 1330-1600UTC)

Height Height Class R/ Intensity

(m) Class centre (mm/hr)

5217.5 5000-5500 5250 6.0

5219.3 5000-5500 5250 6.8

5022.2 5000-5500 5250 2.2

4665.5 4500-5000 4750 0.4

4350.8 4000-4500 4250 0.2

Storm 2 (07/03/2006: 1530-1930UTC)



3962.9 3500-4000 3750 0.8

5204.6 5000-5500 5250 2.4

5815.7 >5500 5750 4.0

5951.3 >5500 5750 4.2

5910.5 >5500 5750 12.4

5523.7 >5500 5750 11.0

4853.8 4500-5000 4750 0.8

4272.8 4000-4500 4250 0.4

Storm 3 (07/03/2006: 2230 - 08/03/2006:0200UTC)



5842.1 >5500 5750 0.2

5751.2 >5500 5750 1.4

5585.6 >5500 5750 2.6

5390.3 5000-5500 5250 2.6

5123.9 5000-5500 5250 3.0

4838.9 4500-5000 4750 3.0

4689.2 4500-5000 4750 2.8

4656.5 4500-5000 4750 1.4

Storm 4 (01/04/2006: 2100 – 02/04/2006: 0500UTC)



2882.0 2500-3000 2750 0.4

3455.6 3000-3500 3250 1.0

3672.8 3500-4000 3750 6.2

3974.3 3500-4000 3750 6.4

4235.3 4000-4500 4250 1.8

4724.9 4500-5000 4750 2.4

4935.5 4500-5000 4750 3.8

4822.1 4500-5000 4750 6.0

4565.9 4500-5000 4750 5.8

4241.3 4000-4500 4250 4.4

4102.1 4000-4500 4250 5.6

3899.6 3500-4000 3750 6.8

3616.1 3500-4000 3750 9.6

3491.6 3000-3500 3250 11.4

3212.6 3000-3500 3250 7.8

2849.9 2500-3000 2750 3.6

2540.3 2500-3000 2750 1.0

Storm 5 (20/04/2006:1200 – 1230UTC)



2931.7 2500-3000 2750 4.0

4022.0 4000-4500 4250 5.8

Storm 6 (05/05/2006: 1730 - 2000UTC)



4061.0 4000-4500 4250 2.8

4306.4 4000-4500 4250 6.4

4894.7 4500-5000 4750 10.6

5154.5 5000-5500 5250 23.8

Storm 7 (05/05/2006: 2030 - 2230UTC)



5045.0 5000-5500 5250 38.4

4767.5 4500-5000 4750 51.0

4499.9 4000-4500 4250 38.2

4337.3 4000-4500 4250 10.8

4208.9 4000-4500 4250 3.2

Storm 8 (10/05/2006:0200 – 0600UTC)



4453.1 4000-4500 4250 1.0

4501.1 4500-5000 4750 3.6

4407.8 4000-4500 4250 23.0

4398.5 4000-4500 4250 45.6

4682.3 4500-5000 4750 44.6

4988.6 4500-5000 4750 25.8

4888.1 4500-5000 4750 12.0


SITU OBSERVATIONS

93

4704.5 4500-5000 4750 10.4

4854.8 4500-5000 4750 9.6

Storm 9 (10/05/2006:0630 – 0930UTC)



4904.9 4500-5000 4750 14.2

4824.1 4500-5000 4750 21.0

4920.4 4500-5000 4750 33.8

4820.8 4500-5000 4750 16.2

4773.5 4500-5000 4750 5.2

4674.8 4500-5000 4750 1.4

4615.4 4500-5000 4750 0.2

Storm 10 (12/05/2006:1400 – 1900UTC)



5708.0 >5500 5750 2.6

5774.0 >5500 5750 18.6

5754.7 >5500 5750 26.8

5392.4 5000-5500 5250 12.8

4957.1 4500-5000 4750 3.8

4954.1 4500-5000 4750 3.4

5003.9 5000-5500 5250 3.6

4752.8 4500-5000 4750 2.8

4561.1 4500-5000 4750 1.2

4469.6 4000-4500 4250 0.6

4281.5 4000-4500 4250 0.2

Storm 11 (14/05/2006:1900 – 2130UTC)



5779.3 >5500 5750 0.8

5417.3 5000-5500 5250 8.8

5571.1 >5500 5750 9.4

5302.6 5000-5500 5250 3.6

5365.9 5000-5500 5250 2.8

Storm 12 (16/05/2006:1400 – 1500UTC)



2522.9 2500-3000 2750 0.2

2653.1 2500-3000 2750 2.6

3014.6 3000-3500 3250 2.6

5680.0 >5500 5750 0.6

Storm 13 (22/07/2006:0430 – 0630UTC)



2513.6 2500-3000 2750 0.4

2513.9 2500-3000 2750 1.0

2407.1 0.8

2387.0 0.4

2512.7 2500-3000 2750 0.2

Storm 14 (05/08/2006:1400 – 1630UTC)



6151.7 >5500 5750 0.4

6176.3 >5500 5750 7.6

5906.3 >5500 5750 10.8

5588.9 >5500 5750 3.4

5325.2 5000-5500 5250 1.2

5150.6 5000-5500 5250 0.4

Storm 15 (06/08/2006:0100 – 0430UTC)



2430.1 0.8

2494.6 6.2

2630.8 2500-3000 2750 6.6

2766.7 2500-3000 2750 2.4

2799.1 2500-3000 2750 2.6

2758.6 2500-3000 2750 2.4

2631.4 2500-3000 2750 1.2

2538.1 2500-3000 2750 0.2


SITU OBSERVATIONS

94

Appendix E: Storms over CGIS Weather Station used for Developing Cloud

height – Total Rainfall Regression Function

Storm 1 (07/03/06: 1530-1930UTC)

Height Rainfall

(m) (mm)

3962.9 0.8

5204.6 1.6

5815.7 2.4

5951.3 1.8

5910.5 10.6

5523.7 0.4

4853.8 0.4

Avg H.: 5317.5

Total Rainfall: 18.0

Storm 2 (07/03/06: 2230 - 08/03/06:0200UTC)

Height Rainfall

(m) (mm)

5842.1 0.2

5751.2 1.4

5585.6 1.2

5390.3 1.4

5123.9 1.6

4838.9 1.4

4689.2 1.4

Avg H.: 5317.3

Total Rainfall: 8.6

Storm 3 (18/03/06: 1230 - 1530UTC)

Height Rainfall

(m) (mm)

2436.8 0.2

2453.5 1.4

2841.5 0.6

3416.0 0.2

3669.5 0.2

3628.7 0.2

Avg H.: 3074.3

Total Rainfall: 2.8

Storm 4 (27/03/06: 1700 - 2130UTC)

Height Rainfall

(m) (mm)

4495.1 0.8

3950.9 1.8

3165.2 1.0

2697.8 2.6

2766.2 2.6

2895.2 2.2

3194.3 0.4

3526.7 0.6

3562.7 0.2

Avg H.: 3361.6


Storm 5 (01/04/06: 2100 – 02/04/06: 0500UTC)

Height Rainfall

(m) (mm)

2882.0 0.4

3455.6 0.6

3672.8 5.6

3974.3 0.8

4235.3 1.0

4724.9 1.4

4935.5 2.4

4822.1 3.6

4565.9 2.2

4241.3 2.2

4102.1 3.4

3899.6 3.4

3616.1 6.2

3491.6 5.2

3212.6 2.6

2849.9 1.0

Avg H.: 3917.6


Storm 6 (14/04/06: 1530 – 1800UTC)

Height Rainfall

(m) (mm)

1774.7 0.2

2143.7 3.4

2791.1 0.6

3140.0 0.8

3221.9 0.2

Avg H.: 2614.3

Total Rainfall: 5.2


SITU OBSERVATIONS

95

Storm 7 (05/05/06: 2030 - 2230UTC)

Height Rainfall

(m) (mm)

5045.0 21.6

4767.5 29.4

4499.9 8.8

4337.3 2.0

4208.9 1.2

Avg H.: 4571.7


Storm 8 (10/05/06:0630 – 0930UTC)

Height Rainfall

(m) (mm)

4904.9 9.4

4824.1 11.6

4920.4 12.2

4820.8 4.0

4773.5 1.2

4674.8 0.2

Avg H.: 4819.8


Storm 9 (12/05/06:1400 – 1900UTC)

Height Rainfall

(m) (mm)

5708.0 2.6

5774.0 16.0

5754.7 10.8

5392.4 2.0

4957.1 1.8

4954.1 1.6

5003.9 2.0

4752.8 0.8

4561.1 0.4

4469.6 0.2

Avg H.: 5132.8


Storm 10 (20/07/06:2200 – 21/07/06:0130UTC)

Height Rainfall

(m) (mm)

2423.3 0.2

2415.5 0.2

2411.6 0.4

2429.9 0.6

2469.5 0.4

2481.5 0.2

2507.0 0.2

Avg H.: 2448.3

Total Rainfall: 2.2

Storm 11 (22/07/06:0430 – 0630UTC)

Height Rainfall

(m) (mm)

2513.6 0.4

2513.9 0.6

2407.1 0.2

2387.0 0.2

Avg H.: 2455.4

Total Rainfall: 1.4

Storm 12 (05/08/06:1400 – 1630UTC)

Height Rainfall

(m) (mm)

6151.7 0.4

6176.3 7.2

5906.3 2.6

5588.9 0.8

5325.2 0.4

Avg H.: 5829.7



SITU OBSERVATIONS

96

Appendix F: Sample of Rain gauge (tipping bucket) Rainfall Data (Nairobi-

Dagoretti Meteorological Station)

10/22/06 17:46:22.5 11.684

10/22/06 17:47:30.0 11.938

10/22/06 17:49:28.0 12.192

10/22/06 17:51:35.5 12.446 10/22/06 17:53:20.5 12.7

10/22/06 17:56:16.5 12.954

10/22/06 18:09:55.5 13.208

10/22/06 18:19:16.5 13.462

10/22/06 18:20:42.0 13.716

10/22/06 18:20:42.5 13.97

10/22/06 18:21:39.0 14.224

10/22/06 18:22:12.0 14.478

10/22/06 18:22:45.0 14.732

10/22/06 18:23:53.0 14.986

10/22/06 18:27:24.0 15.24

10/22/06 18:27:24.5 15.494

10/22/06 18:28:02.0 15.748

10/22/06 18:28:02.5 16.002

10/22/06 18:30:54.5 16.256

10/22/06 18:30:55.0 16.51

10/22/06 18:31:31.0 16.764

10/22/06 18:32:28.5 17.018

10/22/06 18:32:51.0 17.272

10/22/06 18:33:08.0 17.526

10/22/06 18:33:28.5 17.78

10/22/06 18:33:46.5 18.034

10/22/06 18:34:00.5 18.288

10/22/06 18:34:17.0 18.542

10/22/06 18:34:37.5 18.796

10/22/06 18:34:38.0 19.05

10/22/06 18:34:56.0 19.304

10/22/06 18:34:56.5 19.558

10/22/06 18:35:17.0 19.812

10/22/06 18:35:17.5 20.066

10/22/06 18:35:36.0 20.32

10/22/06 18:35:36.5 20.574

10/22/06 18:36:02.0 20.828

10/22/06 18:36:18.5 21.082

10/22/06 18:36:52.5 21.336

10/23/06 13:29:57.0 21.59

Date/Time (Local) (Hrs) Accumulative Rainfall (mm)

10/13/06 11:28:47.0 0

10/13/06 23:41:31.5 0.254

10/13/06 23:43:13.0 0.508

10/13/06 23:44:13.0 0.762

10/13/06 23:49:10.0 1.016

10/13/06 23:56:54.5 1.27

10/13/06 23:58:27.5 1.524

10/14/06 00:00:57.5 1.778

10/14/06 00:02:32.5 2.032

10/14/06 00:03:43.0 2.286

10/14/06 00:07:18.0 2.54

10/14/06 00:14:23.5 2.794

10/14/06 00:14:24.0 3.048

10/14/06 00:37:49.0 3.302

10/14/06 00:42:19.0 3.556

10/14/06 00:42:19.5 3.81

10/14/06 00:44:26.0 4.064

10/14/06 00:47:29.5 4.318

10/14/06 00:49:02.0 4.572

10/14/06 00:50:46.5 4.826

10/17/06 02:41:33.0 5.08

10/17/06 02:41:33.5 5.334

10/17/06 02:42:09.0 5.588

10/17/06 09:00:02.5 5.842

10/17/06 09:01:56.0 6.096

10/17/06 09:01:56.5 6.35

10/17/06 09:03:04.0 6.604

10/17/06 09:03:04.5 6.858

10/17/06 09:10:43.0 7.112

10/17/06 09:11:13.5 7.366

10/17/06 09:12:23.5 7.62

10/17/06 09:14:05.5 7.874

10/17/06 09:22:19.5 8.128

10/17/06 09:22:20.0 8.382

10/17/06 09:25:48.0 8.636

10/21/06 06:54:16.0 8.89

10/22/06 16:43:23.5 9.144

10/22/06 16:43:24.0 9.398

10/22/06 16:47:09.5 9.652

10/22/06 17:36:14.0 9.906

10/22/06 17:37:00.0 10.16

10/22/06 17:37:00.5 10.414

10/22/06 17:38:34.0 10.668

10/22/06 17:43:36.0 10.922

10/22/06 17:45:09.5 11.176

10/22/06 17:45:49.0 11.43

meteosat second generation (msg) cloud mask, cloud ... · cloud heights were greater than 3000m....

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