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Chuntao Liu Department of Atmospheric Sciences University of Utah

Testing of V1. GPM algorithm of rainfall retrieval from microwave brightness temperatures - preliminary results using TRMM observations. Chuntao Liu Department of Atmospheric Sciences University of Utah. Motivation.

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Chuntao Liu Department of Atmospheric Sciences University of Utah

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  1. Testing of V1. GPM algorithm of rainfall retrieval from microwave brightness temperatures - preliminary results using TRMM observations Chuntao Liu Department of Atmospheric Sciences University of Utah

  2. Motivation • Testing the performance of GPM GMI algorithm for traditional microwave channels (10, 19, 22, 37, 85, 89 GHz, no high frequency channels) with different types of precipitation systems over different types of land surface. • Focus is over land, especially over different rainy regions and over some difficult surface types. • The comparisons to TRMM PR retrievals

  3. Types of precipitation systems tested • Different types of precipitation systems Desert (Sahel 0-30E, 20-25N) High land (ground elevation > 3000 m) Isolated deep systems over different regions ( < 1000 km2, PR echo top > 12 km) MCS deep systems over different land regions (> 20000 km2, PR echo top > 12 km) Large shallow systems over different land regions (> 6000 km2, PR echo top < 8 km) Snow case over land (> 4000 km2, surface temperature < -5Co) Snow case over ocean (> 4000 km2, surface temperature < 1Co) Warm rain (> 4000 km2 , PR echo top < 4.5 km, surface T > 7Co) • Different land regions Amazon, Argentina, Australia, Central Africa, Southern China, India, Maritime continents, South east US, South west US. • 10 cases of each type are randomly chosen from 12 years of TRMM observations • All tests are downloadable at: ftp://trmm.chpc.utah.edu/pub/trmm/tmp/for_dave/

  4. Test setup schematic diagram GPM test version 1 UU precipitation feature database Level-2 PFs Database Pre-processor + ERA environment + surface class + emission class + topography List of cases Pixel level V6 1B11 TBs Bayesian Pixel level V6 2A25 rainrate Pixel level V6 2A12 rainrate Pixel level Retrieval rainrate S0 Pixel level Retrieval rainrate S1 Comparisons different algorithm output Different types of systems over different surface types • Notes: • There were two version of databases of GPM algorithm been tested. The first version (D1 2011-11-07) has less profiles than the second one (D2 2011-11-28). • Currently all test been done with V6 2A25. Will do this with V7 once UU database been reprocessed

  5. An example

  6. Statistics of comparison Step 1: Finding all the PR 2A25 pixels within the 4 degree box surrounding the center of the precipitation feature Step 2: Collocate all the PR and TMI pixels in the box and find the corresponding V6, S0, S1 retrieval for each PR pixel Step 3: Calculate following parameters Tr: total # of pixels with PR 2A25 rainrate > 0.1 mm/hr Fr: # of pixels with Retrieval rainrate > 0.1 mm/hr, but PR 2A25 rainrate < 0.1 mm/hr Mr: # of pixels with PR 2A25 detects rain > 0.1 mm/hr but retrieval rainrate < 0.1 mm/hr Pr: total rain volume within 4x4 box from PR 2A25 Rr: total rain volume within 4x4 box from retrieval False alarm: Fr / Tr *100% Missed rain: Mr / Tr *100% Correlation: correlation coefficient between 2A25 and retrieval with pixels of both rainrate > 0.1 mm/hr Volume rain ratio : Rr/Pr

  7. Mean statistics (desert and high land) desert false alarm % miss rate% correlation vlrain ratio V6 4.70 83.50 0.09 0.69 S0(2011-11-28) 54.60 51.50 0.12 0.63 S1(2011-11-28) 56.80 56.00 0.11 0.62 S0(2011-11-07) 44.20 57.60 0.11 0.71 S1(2011-11-07) 35.70 65.40 0.09 0.50 highland false alarm % miss rate% correlation vlrain ratio V6 44.30 35.80 0.17 1.56 S0(2011-11-28) 83.70 23.30 0.11 1.35 S1(2011-11-28) 77.80 22.50 0.11 1.20 S0(2011-11-07) 68.60 29.40 0.08 0.98 S1(2011-11-07) 69.90 28.40 0.02 0.99 GPM algorithm performs a bit better on detecting the rainfall than V6 2A12.

  8. Desert case

  9. High land case Over Tibet

  10. Mean statistics (Snow) snow_land false alarm % miss rate% correlation vlrain ratio V6 4.60 96.00 -0.04 0.12 S0(2011-11-28) 8.20 91.80 NaN 0.12 S1(2011-11-28) 7.40 92.70 NaN 0.11 S0(2011-11-07) 17.30 82.20 NaN 0.27 S1(2011-11-07) 15.90 82.30 0.09 0.22 snow_oceanfalse alarm % miss rate% correlation vlrain ratio V6 3.40 98.70 NaN 0.11 S0(2011-11-28) 38.20 82.80 0.05 0.27 S1(2011-11-28) 41.20 80.20 0.04 0.29 S0(2011-11-07) 42.90 81.50 -0.04 0.30 S1(2011-11-07) 44.20 79.30 -0.07 0.31 As expected, without high frequency’s help, it is almost hopeless for snow cases. But GPM does show some detection somehow (see the case in the next slide).

  11. Snow over ocean

  12. Mean statistics (Warm rain) warmrainfalse alarm % miss rate% correlation vlrain ratio V6 51.50 86.60 0.13 0.49 S0(2011-11-28) 98.40 66.60 0.17 0.63 S1(2011-11-28) 91.60 67.90 0.10 0.62 S0(2011-11-07) 83.80 68.90 0.08 0.92 S1(2011-11-07) 82.40 69.80 0.13 0.92 Neglecting the high false alarm rate. As expected (or over expected), GPM does a better job detecting warm rain than V6 2A12, which only relying on the ice scattering.

  13. A warm rain case

  14. Mean statistics (MCSs over US) mcs_deep_SE_US false alarm % miss rate% correlation vlrain ratio V6 15.50 23.80 0.27 0.88 S0(2011-11-28) 27.00 20.30 0.21 0.90 S1(2011-11-28) 26.00 24.00 0.24 0.77 S0(2011-11-07) 27.60 19.10 0.17 0.93 S1(2011-11-07) 27.80 22.10 0.14 0.77 mcs_deep_SW_USfalse alarm % miss rate% correlation vlrain ratio V6 25.80 19.10 0.39 1.05 S0(2011-11-28) 44.40 21.30 0.20 0.78 S1(2011-11-28) 37.60 31.20 0.26 0.72 S0(2011-11-07) 43.60 25.70 0.08 0.67 S1(2011-11-07) 43.00 25.20 0.13 0.64 There are a lot to understand for these cases. In general, GPM does decent job retrieve the rain. There are some problems for the extreme cases, such as hurricanes (mcs_deep_se_us_08). One thing is noticed that the Bayesian provides retrievals from different channels of different footprint sizes, so the continuity of rainrate within the system could get worse, that is why there are low correlations to the PR 2A25, though the rain volume is about right.

  15. One MCS case over SW US

  16. There are many more cases online to chew on, I do not list all them here • ftp://trmm.chpc.utah.edu/pub/trmm/tmp/for_dave/

  17. Some thoughts • It seems that with some surface emissivity classification and using low frequency channel information, some difficult cases can be rescued, such as desert, snow and warm rain cases, though false alarm is high too. • With even large database, some of the extreme cases could be resolved by the algorithm. • Discontinuity of rain rate is something that might be able to improve on.

  18. Introduction of GPM Preprocessor • Function With the input time and location of each pixel, it adds parameters of atmospheric environment from ECMWF, surface topography (0.1ox0.1o resolution), emissivity class (0.5ox0.5o monthly), and surface class (0.1ox0.1o annual average from MODIS, currently only from 2001-2004 ) • The parameters read in from ECMWF 2 metretemperature; Surface pressure; 10 metre wind speed; Total column water vapour; Total column ice water; Total column liquid water; Skin temperature; Sea surface temperature; Total Cloud Cover; 10 metre wind direction; Geopotential; Temperature; Specific humidity; Cloud liquid water content

  19. Note: Color scale shifts between 0-1000 and 1000 above

  20. 11 categories

  21. Surface classes from MODIS (2001-2004) 0-17 categories dominate

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