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Brigham Young UniversityBYU ScholarsArchive
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
Follow this and additional works at: https://scholarsarchive.byu.edu/iemssconference
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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 ([email protected].),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