Precipitation Estimation from NOAA/AMSU Data
Izabela Dyras, Danuta Serafin-Rek
Satellite Research Department, Institute of Meteorology and Water Management, Poland.
Izabela.Dyras@imgw.pl
INTRODUCTION
The data available from an Advanced Microwave Sounding Unit (AMSU) on board of NOAA-16 and NOAA-15
satellites have enhanced the possibilities of the new meteorological products derivation.
The presented algorithms are the modification of the regression algorithms developed by Grody and Crosby to
infer cloud liquid water path (LWP) and rain detection over the land surfaces from passive microwave
measurements of AMSU.
The methods were developed using satellite observations of July 2001 with the ground-based precipitation
measurements and radiosonde observations from Central Europe region.
The AMSU data have been processed using the Advanced ATOVS Processing Package (AAPP ver.3.0) and
visualised using ER Mapper 6.2 software. Presented examples are calculated for 9 July 2001.
ALGORITHMS:
REGRESSION ANALYSIS OF AMSU
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Brightness Temperature 31.4, 50.3, 89, 150
GHz
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AMSU-A 23.8 GHz
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TB31,4
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TB50,3
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TB89
TB150
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Brightness Temperature at 23.8 GHz
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The influence of the water vapour, cloud water and
precipitation on brightness temperature was investigated
using channels at: 23.8, 31.4, 50.3, 89.0, 150.0,183.3 GHz
as their weighting functions' maxima are in the lower and
middle troposphere. The plot shows the brightness
temperature from 31.4, 50.3, 89.0 and 150.0 GHz channels
as a function of temperature at 23.8 GHz for all analyzed
cases. Very high correlation - 0.97 was found between
temperatures at 31.4 and 23.8 GHz.
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AMSU-A 50.3 GHz
AMSU-A 31.4 Ghz
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270
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170
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[K]
300
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[K]
CLOUD LIQUID WATER
150
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Liquid Water Path
1.5
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1.0
Q- satellite zenith angle,
TB(89) and TB(150) brightness temperature at 89.0 and
150.0 GHz respectively.
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LWP_sat = cosq(11.699 + 0.038* TB(89) - 0.0756* TB(150))
0.3
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On the left: the areas of high LWP derived from AMSU
correspond to the regions covered by thick clouds observed on
METEOSAT image.
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On the right: the scatter plot of the LWP derived from satellite
microwave data against the values calculated from radiosonde
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measured data. Bias for LWP_sat is equal 0.6 kg/m and R =0.33.
METEOSAT - IR 09.07.2001, 12.00 GMT
PRECIPITATION
AMSU-B 183.3+7 GHz
LW P=cosq*(11,699+0,038*T89-0,0756*T150)
[kg/m2]
3.0
LWP - AMSU/NOAA
LWP
[kg/m2]
AMSU-B 150.0 GHz
AMSU-B 89.0 GHz
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LW P - radiosonde
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[kg/m2]
Precipitation Probability - Crosby test
Rain Detection
Rain Rate
CONCLUSIONS
P = 1/(1+exp(-f)) [%]
f = 10.5 + 0.184*TB(23.8)- 0.221* TB(89)
SI_land = 42.72+TB(89)-TB(150)
RR = 64.47 + 0.146 * TB(23.8) 0.364 * TB(89)
ü While developing regression algorithms for atmospheric
moisture and cloud water content from satellite data only the
data from aerological measurements at the same time
should be considered, also the weather type should be taken
into account.
ü Due to AMSU/NOAA radiometers' low spatial resolution it is
very important to pay attention to the geographical
rectification of the data.
ü Rain rate algorithms should be developed based on the
network of hydro- meteorological or radar stations.
ü Algorithms have to be tested for different seasons.
ü Another problem concerns the microwave data analysis in
wintertime, the impact of snow and ice needs the detailed
investigation.
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[%]
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R R = 6 4.4 7 + 0.1 4 6 * T 2 3 -0 .3 6 4 * T 8 9
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SI
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Rain - AMSU/NOAA
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09.07.2001, 12.00 GMT
RESULTS
ý The presented analysis was performed on 23 satellite images
chosen from July 2001, when the extremely high rain rates
were noted in Poland.
ý The best results were obtained in retrieving cloud liquid water
path and convective clouds identification.
ý The algorithms for rain intensity evaluation are not satisfactory
and need further research.
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REFERENCES
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R a in - syn o p tic d a ta
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09.07.2001, 12.00 GMT
FUTURE WORKS
ý It is difficult to develop correct algorithm for rain rate estimation,
as the precipitation is highly variable in time and space. Further
investigation is planned.
ý The temporal resolution of the polar orbiting satellites is not
sufficient for precipitation monitoring, however, coupled with
METEOSAT/MSG data wil provide valuable information concerning
rain, moisture distribution for hydrological models, numerical
weather prediction or in climatology.
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CIRA AMSU Website
http://www2.ncdc.noaa.gov/docs/klm/html
NOAA KLM User's Guide