AMSR-E Ocean RainfallAlgorithm Status

AMSR-E Science Team MeetingHuntsville, AL2-3 June, 2010

C. KummerowColorado State University

~ 10 km

TB observed

TB model #1

TB model #2

TB model #3

Bayesian Inversion

~10 km

TB observed

TB model #3

TB model #2

TB model #1

Cloud Resolving Model Database

)|()()|( RTRTR bb PPP ×∝

TRMM PR/TMI & Model Database

GPROF_2008

GPROF2008 Algorithm (Ocean)

Create Geophysical Data Base (TRMM)

Start with non-raining background (1-D Var or OE)

Add observed PR rain profiles to non-raining background

Compute Tb at TMI channels and resolution and compare to observations

Adjust rain profiles to be consistent with PR and TMI

Create Rainfall/Tb Data Base (AMSR-E)

Use adjusted 4 km rain and non-raining profiles to compute Tb for AMSR-E

Create Database (raining and non-raining) pixels in 1K SST and 2 mm TPW bins.

Cluster profiles in each SST/TPW bin to 1000 self-similar Tbs for computational efficiency

Run Retrieval

Determine SST (Reynolds) & TPW (Sensor)

Compare observed Tb to dbase entries within ±1K (SST) and ±2 mm (TPW)

Weight profiles [observed difference vs uncertainty in channel from dbase]

Cloud profile database is partitioned into separate SST and TPW bins

Net change is a 2% increase from PR. Biggest change in colder SST with high TPW

Original Tb differences

Tb differences after adding

rain below PR threshold

Rain is added below threshold of PR and DSD assumption in PR is modified

Tb differences after DSD

modification

Tb from Background and Radar rain structureOptimal combined retrieval would leave all points on the 1:1 line

Database Preparation Steps

All pixels - raining and non-raining are retained for database (11,000,000 per month. Up to 32,000 in each SST/TPW bin)

For retrieval to run, all entries in SST and TPW bin are clustered based upon similar Sfc rainfall and Tb1-9. Use 1000 clusters for raining and 200 for non-raining. Numbers based on trial and error. Seek fewest clusters with no change ( < 1%) in final retrieval.

To account for colder environments (e.g. AMSR-E, SSMI), SST and TPW from existing profiles are reduced artificially by lowering SST while removing lowest layer of atmosphere corresponding to 6°/km. GPM will solve properly.

GPROF2008 continues to be a Bayesian algorithm consisting of:

P(R|Tb) = P(Tb|R)•P(R)/P(Tb)

Pixels are classified only by background SST and TPW. Database entries within ±1K in SST and ±2 mm in TPW are searched for potential solutions. Error covariances are established from fit between observed and simulated Tb in the a-priori database.

No more rain screens. All pixels are compared to database. Bayes’ theorem determines rain or no rain. Consequence: almost all pixels have a very small probability of rain.

No more convective/stratiform separation. Only necessary because CRM database was skewed to convective pixels. Entire code is now exceedingly simple.

GPROF 2008

GPROF 2008

+ TRMM radar

GPROF 2008

Hurricane FloydSeptember 13, 1999

GPROF 2008 example

GPROF2008

Hurricane Floyd Sep 3, 1999 TMI Algorithm Comparison 0.25 x 0.25 gridded

1.28 mm/day

RSS

GSmap

0.96 mm/day

0.99 mm/day

Surface Precipitation Jan 2007

CMORPH Diurnal Precipitation Variability

Coincident Overpass TPW Comparison

Coincident Overpass Comparison

TMI / AME TPWCoincident Overpass February, 8th, 2007

Pre-Processors

TMI_L1CTMI_1B11AME_L1CAME_L2ASSMI_L1C

GPROF08 input file

Spacecraft position Pixel Location Pixel Time, Tbs, EIA

Elevation Land masks Sea Surface Temp Clustered Profiles

GPROF2008

Native Binary Output

Land - All Coast – SSM/I Ocean - Each Sensor

HDF HDF-EOS

NetCDF

Profile Databases Ancillary Datasets

Format Converter*

Sensor Data

Temporary Files

Programs

Input Data

Output Files

SST directory spec Ancillary dir spec Sat, Sensor Info Channel Freqs, Errs Profile dbase names

Ancillary Info

GPROF 2008 Processing Algorithm

Output from GPROF 2008

• Basic DiagnosticsPixelStatusQualityFlagOceanSearchRadiusChiSquared

• Surface PrecipitationSurface PrecipitationSurface RainConvective Fraction

• Precipitation StructureProfile number [1-100] per hydrometeor type and freezing level

• Probability of PrecipitationProbability of Precipitation