TRMM TMI Rainfall Retrieval Algorithm

C. Kummerow Colorado State University

2nd IPWG MeetingMonterey, CA. 25 Oct. 2004

Towards a parametric algorithm for GPM

GPROF changes TMI-V5 : V6A net reduction of approx. 5%.

Databases- Created new databases with updated model runs from

(Tao’s group; Greg Tripoli and Grant Petty)- Changed all databases to ascii for distribution- Added bright band calculations to melting layers

Background Tbs- Changed from selecting the clearest Tb for the background Tb to calculating the clear air Tb by removing the wind speed and liquid water components

Freezing level- Interpolating across adjacent pixels

Convective Fraction- Better account for the texture of the convection

AMSR-E (75.2 mm/month)

TMI(78.1 mm/month)

August 2003

Comparison with 16 month of GV data(PR and Combined have changed since)

Courtesy of David Wolff

PR/TMI Global Bias Map

Rainfall Bias RemovalBased on Column Water Vapor

Need for Version 7

Discrepancies (10-15%) remain between PR and TMI at spatial and

temporal scales of interest to climate. These need to be understood

and resolved.

Increasing number of microwave radiometers require more parametric

algorithms. We now have: TMI AMSR-E (AMSR) SSM/I (SSMIS) WindSat

Need to add more comprehensive error model. Currently know random

and sampling errors. Know very little about systematic biases. Cloud models used in the retrievals Regional/temporal changes in cloud properties

With initial assumptions

With updated assumptions

Compute Z/Tb

Measure Z/Tb

Compute Rainfall

Measure Rsfc

Compare

Compare

Once radiances and rainfall can be matched, data cube turns into ideal algorithm test and verification site that is not limited by infrequent over-passes of the “core” satellite.

Data Cube

Validation of core satellite algorithm ?

Important aspects of Version 7

V7 Database is essentially PR and is modified only if emission signal of TMI indicates a change is needed.

Database is more representative of observed rainfall profiles but can only be constructed for regimes (defined perhaps by SST or CWV) observed by PR. Code for SSMI, AMSR will retain CRM for colder surfaces until GPM is available.

A new validation paradigm will be needed for these databases

V7 eliminates all screening routines (they tend to be sensor dependent and make error modeling impossible. Instead:

Confidence that correct database is being used Probability of rain Mean conditional rainfall Uncertainty in rainfall (inversion uncertainty) Space/time error model

Rain Rate Probability of Rain

Sigma Rain GPROF V6

General issues with new algorithms

A number of different algorithms exist for constructing the a-priori databases for future parametric algorithms. But …

They currently exist only for tropical oceans. Have no way of judging if one method is better than another

Some attention has been paid to land and extratropics. But … Coordination is poor Methologies are different No work on how to transition from one method to another

As the number of microwave sensors increases, sampling becomes much better. But….

Standards don’t exist (even simple things like version numbers) Quality assurance becomes more difficult Coordinated Version management is needed

Rainfall Detection Errors

Rainfall Detection ErrorsFebruary 1, 2000

PR/TMI Bias vs. Column Water Vapor