TRMM TMI Rainfall Retrieval Algorithm Towards a parametric

TRMM TMI Rainfall Retrieval Algorithm Towards a parametric algorithm for GPM C. Kummerow Colorado State University 2 nd IPWG Meeting Monterey, CA. 25 Oct. 2004

GPROF changes TMI-V 5 : V 6 A 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

August 2003 TMI (78. 1 mm/month) AMSR-E (75. 2 mm/month)

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

PR/TMI Global Bias Map

Rainfall Bias Removal Based on Column Water Vapor

Need for Version 7 Ø Discrepancies (10 -15%) remain between PR and TMI at spatial and temporal scales of interest to climate. These need to be understood and resolved. Ø Increasing number of microwave radiometers require more parametric algorithms. We now have: § TMI § AMSR-E (AMSR) § SSM/I (SSMIS) § Wind. Sat Ø 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

Validation of core satellite algorithm ? Measure Z/Tb Compare Once radiances and rainfall can be matched, data cube turns into ideal algorithm test and verification site that is not limited by infrequent overpasses of the “core” satellite. Compute Z/Tb Data Cube Compute Rainfall Measure Rsfc Compare

Important aspects of Version 7 Ø V 7 Database is essentially PR and is modified only if emission signal of TMI indicates a change is needed. Ø 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. Ø A new validation paradigm will be needed for these databases Ø V 7 eliminates all screening routines (they tend to be sensor dependent and make error modeling impossible. Instead: v Confidence that correct database is being used v Probability of rain v Mean conditional rainfall v Uncertainty in rainfall (inversion uncertainty) v Space/time error model

Rain Rate Sigma Rain Probability of Rain GPROF V 6

General issues with new algorithms ØA 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 ØSome attention has been paid to land extratropics. But … § Coordination is poor § Methologies are different § No work on how to transition from one method to another ØAs 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 Errors February 1, 2000

PR/TMI Bias vs. Column Water Vapor