Testing of V1. GPM algorithm of rainfall retrieval from microwave brightness temperatures

- preliminary results using TRMM observations

Chuntao Liu

Department of Atmospheric SciencesUniversity of Utah

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

Types of precipitation systems tested

• Different types of precipitation systemsDesert (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 regionsAmazon, 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/

Test setup schematic diagram

Level-2 PFs

Pixel levelV6 1B11 TBs

Pixel levelV6 2A25 rainrate

Pixel levelV6 2A12 rainrate

UU precipitation feature database GPM test version 1

List of cases

Pre-processor+ ERA

environment+ surface class

+ emission class+ topography

Database

Bayesian

Pixel levelRetrieval

rainrate S0

Pixel levelRetrieval

rainrate S1

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

Comparisonsdifferent algorithm output

Different types of systems over different surface types

An example

Statistics of comparison

Step 1:Finding all the PR 2A25 pixels within the 4 degree box surrounding the center of the precipitation featureStep 2:Collocate all the PR and TMI pixels in the box and find the corresponding V6, S0, S1 retrieval for each PR

pixelStep 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

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.

Desert case

High land caseOver Tibet

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_ocean false 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

Mean statistics (Snow)

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).

Snow over ocean

warmrain false 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

Mean statistics (Warm rain)

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.

A warm rain case

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 0.77mcs_deep_SW_US false 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

Mean statistics (MCSs over US)

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.

One MCS caseover SW US

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/

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.

Introduction of GPM Preprocessor

• FunctionWith 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 metre temperature; 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

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

11 categories

Surface classes from MODIS (2001-2004)

0-17 categories dominate