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International Congress on EnvironmentalModelling and Software

6th International Congress on EnvironmentalModelling and Software - Leipzig, Germany - July

2012

Jul 1st, 12:00 AM

Cloud detection and analysis using LAPS systemB. Rajkovic

J. Markovic

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Rajkovic, B. and Markovic, J., "Cloud detection and analysis using LAPS system" (2012). International Congress on EnvironmentalModelling and Software. 242.https://scholarsarchive.byu.edu/iemssconference/2012/Stream-B/242

International Environmental Modelling and Software Society (iEMSs)1

2012 International Congress on Environmental Modelling and Software2

Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany3

R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.)4

http://www.iemss.org/society/index.php/iemss-2012-proceedings5

Cloud detection and analysis using LAPS6

system7

B. Rajkovic a and J. Markovicb8

aFaculty of Physics, Belgrade University Belgrade, Serbia (bora@ff.bg.ac.rs.),9

SEEVCCC, Republic Hydrometeorological Service of Serbia10

b Faculty of Physics, Belgrade University Belgrade, Serbia, (dzeca.markovic@gmail.)11

SEEVCCC, Republic Hydrometeorological Service of Serbia12

Abstract: This paper presents assimilation of radar reflectivity using the Local Analysis13

and Prediction System (LAPS). Assimilation process has two stages. The first stage is14

decoding of the reflecitivity from. vol or .xml formatted files (produced is by the native15

radar software) and writing it in the netCDF format, the format of all LAPS input data16

. The second stage is LAPS’s reflectivity analysis. Its first phase is projecting the data17

onto the horizontal planes without taking into account data points present above and18

bellow. In the second phases vertical interpolation is performed, thus obtaining 3D field19

of the reflectivity.This separation in the data analysis greatly increases the speed of the20

analysis, making LAPS very fast (efficient) system.21

Keywords: Radar; Reflectivity; Remapping; Clouds; 3D Var analysis22

1 INTRODUCTION23

LAPS (The Local Analysis and Prediction System) was created with the aim of data as-24

similation, nowcasting and model initialization. This system is designed to exploit various25

data sources which produce spatially and temporally diverse data, taking into account26

the strengths of each source. Its goal is to adequately resolve meso and small scale27

systems in the atmosphere and produce gridded analysis which can be used by the fore-28

cast models. LAPS can ingest the following data: gridded background models, surface29

data (SYNOP, METAR, automatic weather stations), vertical soundings, satellite (image,30

sounder, cloud top pressure, cloud drift winds), radar (reflectivity and radial velocity from31

Doppler radar), PIREPS and ACARS from aircraft, wind profiler / RASS, GPS Global32

Positioning System.33

Beside standard analyses (temperature, wind, humidity, pressure) LAPS performs cloud34

analysis, as well, all with an adjustable spatial and temporal resolution. This paper deals35

with a detailed analysis of clouds using three-dimensional temperature and radar reflec-36

tivity analyses as an important inputs for the cloud analysis. This analysis is based on37

combining the data from METAR stations, satellite, PIREPS and ACARS reports from38

aircraft with temperature and radar reflectivity analysis, using volume radar data. After39

the analysis of cloud cover several derived fields are calculated, cloud type, cloud droplet40

size, cloud liquid water/ice, etc. This is performed by using ambient temperature, tem-41

perature profile and Smith-Feddes model, simple 1-D cloud model described by Hines42

et al. [1989], and with one additional condition particulary used for cumulonimbus (radar43

reflectivity is greater than 45 dBZ). After this step of data combination a dynamic ad-44

justment of velocity and temperature field is performed in accordance with fundamental45

Rajkovic and Markovic / Cloud detection ...

equations (thermodynamics, motion and continuity) within the desired level of accuracy.46

The analysis described above was a candidate for generation of a model initial condition47

shown by McGinley and Smart [2001].48

The paper is organised in four sections, introduction, cloud analysis, results, conclusions49

and acknowledgments and references.50

2 CLOUD ANALYSIS51

The first step in a three-dimensional cloud cover analysis is using METAR stations re-52

ports, which indicate levels with clouds mapped onto the vertical LAPS grid. In this stage53

we assume cloud thickness of 1000m thus defining top of clouds. If there is no other54

data this cloud thickness becomes final. Grid points above the overcast layer are not55

initialized because the cloud profile is unknown , Albers et al. [1996]. These data are56

further combined with pilot reports to refine positions of cloud bases and tops. Clouds57

horizontal extent is obtained through horizontal objective analysis (Barnes approach) us-58

ing r−5 weight given to each station (where r is distance of the station from the analyzed59

grid point). This constitutes the preliminary analysis.60

Figure 1: An illustration of the transformation from radar beam data to LAPS griddeddata. On the apcisa we have distance from the left edge of the domain while on theordinate we have height in km.

Next, satellite data are inserted into the preliminary analysis to resolve better cloud top61

height. The satellite cloud top temperature is converted to a cloud top height, using62

LAPS three-dimensional temperature analysis. This information from satellite gives an63

additional detailed survey of tops of clouds that already exist in the previous analysis or64

it will create new cloud cover over points where cloud were not detected either in METAR65

Rajkovic and Markovic / Cloud detection ...

0

-100

0

100

0

0

0 - 10 dBz

10 - 20

20 - 30

30 - 40

40 - 50

50 - 60

60 - 70

70 - 80

80 - 90

-100 0 100

-100

0

100

-100 0 100

0

Figure 2: Reflectivity data for several elevations in the conidial, polar projected on tox-y Cartesian plane. Relflectivity is for elevations 4-7 from top left to bottom right. Theaxes are in km and show west-east (apcisa) and north-south distance (ordinate) from theradar.

nor in aircraft data. Also, a set of rules and several error checks are required to resolve66

situations if there are conflicts among METAR, aircraft and satellite data. In general,67

METAR data are given advantage in case of warm and low clouds.68

Finally, the three-dimensional radar reflectivity field is inserted to provide an additional69

detail in the analysis. In the preprocessing procedure data is written in NetCDF format.70

Our volume data from Fruska Gora radar are in vol format that consists of acsii header71

and binary written reflectivity data. First these data are decoded, using Fortran code and72

then written in the NetCDF format, each elevation in a separate file. Our radar is set73

to 12 different elevations so that twelve NetCDF files are made and used in remapping74

subroutine. Recently a new software has been introduced which produces reflectivty75

data in the xml format, slightly different then vol format but the principels of decoding are76

very similar. Our examples will be for the older vol files for which an example of intensive77

cloud system.78

3 RESULTS79

The radar reflectivity analysis, which is used in a cloud analysis, is based on polar to80

Cartesian transformation on LAPS grid. This transformation has two steps, depending81

on how high LAPS resolution grids are. For all LAPS grid points, reflectivity is computed82

averaging all value gates within volume centred on the LAPS grid point. Also, it is as-83

sumed that radar beam has zero width so only those grid volumes that are filled with84

gates are assigned with radar reflectivity. In the case of lower resolution (dx > 5km),85

another requirement has to be fulfilled, that in one LAPS grid volume must be at least86

Rajkovic and Markovic / Cloud detection ...

Figure 3: Reflectivity mapped onto laps grid middle levels 8 (3100 m), 10 (4300 m) 12(5600 m) and 14 (7200 m) from top left to bottom right. On the apcisa is west-eastdistance in km and on the ordinate north-south distance also in km. Radar is positionedin the center of the domain. Legend shows reflectivity intensities in the correspondingcolor scheme.

4 valid reflectivity data. For higher resolution this criterion is relaxed. In this stage of87

remapping procedure there is possibility to produce sparse arrays if the grid resolution88

is less than 10 km. For medium/high resolution (< 5km) there is a need for a horizontal89

analysis where un-illuminated points are replaced with average data of immediate grid90

neighbours with assistance of Barnes weighting. There is a vertical gap filling with lin-91

ear interpolation up to 2km, because of space between successive antenna increasing92

elevations. There is also a possibility of filling in echo below radar horizon (due to the93

earths curvature). Namely, as radar energy travels through the atmosphere it does not94

propagate in a straight line but it is refracted by the air. Beam propagates downward95

as it moves from the radar but at a rate less than the curvature of the Earths surface,96

shown by Doviak and Zrnic [1993]. If we have two or more radars, taking the nearest97

radar data to each LAPS grid points performs mosaic procedure. This radar reflectivity98

analysis is then inserted in the cloud analysis with several quality control checks. Echoes99

can be added in it as clouds if they exist above pre-existing cloud base and if echo top is100

> 2000m. A visible satellite is also used to detect false echoes in situations where there101

are weak echoes and the visible satellite indicates no clouds.102

Laps system has been designed to utilize grids with very different resolutions, from about103

10 km up to the resolutions of several hundreds of meters. Our radars data have 500104

m bins (radial distance between two radar signals), one degree in azimuth direction and105

twelve elevations. We have designed LAPS grid with grid spacing equal to radars reso-106

lution with LAPS domain equal to the radars domain. The reflectivity data lies on native107

polar conidial iso-surfaces. Eventually, they will be projected onto LAPSs 3D Cartesian108

Rajkovic and Markovic / Cloud detection ...

grid. In fig 1 we present height north plane. On the apcisa we have distance from the left109

edge of the domain while on the ordinate we have height in km. The two lines represent110

two different elevations (0.5 and 1.5 degree). Data point that is to be interpolated onto111

grid lies at the center of differently colored segments, consisting of several bins. Crosses112

with same colors are grid points that obtained values from the corresponding bins. Since113

radar energy travels through the atmosphere it does not propagate in a straight line but114

it is refracted by the air, so positions of crosses do not lie on the respective lines. A115

closer inspection of the picture shows that the nearest bins from radar are not consid-116

ered, they are permanent reflections from the nearby topography the so-called clutters.117

Finally, in the last figure, we show the operational maximum reflectivity for the same mo-

Figure 4: The maximumreflectivity data plot. Axesare distance from radar inkm.

-200 -100 0 100 200

-200

-100

0

100

200

Maximum reflectivityradar domain [ -250 250 km ]

118

ment. Following the two steps in the data assimilation we present the first figure with

Figure 5: The operationalmaximum reflectivity plot.On the right panel is west-east projection of maxi-mum reflectivity data whilesouth-north projection is inthe upper panel.

119

the reflectivity on the native polar conidial system projected onto the x-y Cartesian plane.120

Figure 2 has 4th to 7th elevation. Data from this system is remapped to the Cartesian121

LAPS grid. In Figure 3 we show these results for some of the LAPS levels. The usual way122

Rajkovic and Markovic / Cloud detection ...

of presenting the radar data in a condensed form is the so called maximum reflectivity. It123

represents maximum reflecitivity in a column for each grid point in x-y plane . Figure 4124

has our maximum reflectivity.125

4 CONCLUSIONS AND RECOMMENDATIONS126

The so-called vol. format data was successfully transformed into the netCDF format127

and ingested into the LAPS analysis system. The results of the analysis in the form128

of pictures showed good agreement with the figures that are operationally produced by129

Serbian meteorological service. The next step will be assimilation the radial velocity data130

and also assimilation the differential reflectivity from Doppler radars that can be useful in131

microphysical cloud diagnostic.132

ACKNOWLEDGMENTS133

The research presented in this paper was realized as a part of the project ”Studying134

climate change and its influence on the environment: impacts, adaptation and mitigation”135

(No. 43007) financed by the Ministry of Education and Science of the Republic of Serbia136

within the framework of integrated and interdisciplinary research over the period 2011-137

2014.138

REFERENCES139

Albers S., McGinley, J., Birkenheuer, D., Smart, J.(1996): The Local Analysis and Pre-140

diction System (LAPS): Analyses of clouds, precipitation, and temperature. Weather and141

Forecasting, 11, 273-287142

Doviak, R. J. and Zrnic D. S. (1993): Doplar radar and weather observations. Academic143

Press144

Haines, A., Luers,K., Cerbus, A. (1989): The role of the Smith-Feddes model in improving145

the forecasting of aircraft icing. Preprints, Third Conf. on Aviation Weather Systems,146

Anahaim, Cal. Amer. Meteor. Soc., 258-263147

McGinley, J.A. and J.R. Smart, 2001: On providing a cloud-balanced initial condition for148

diabatic initialization. Preprints, 18th Conf. on Weather Analysis and Forecasting, Ft.149

Lauderdale, FL, Amer. Meteor. Soc.150