1 / 17

TRMM TMI Rainfall Retrieval Algorithm

TRMM TMI Rainfall Retrieval Algorithm. Towards a parametric algorithm for GPM. C. Kummerow Colorado State University. 2nd IPWG Meeting Monterey, CA. 25 Oct. 2004. GPROF changes TMI-V5 : V6 A net reduction of approx. 5%. Databases - Created new databases with updated model runs from

merlin
Télécharger la présentation

TRMM TMI Rainfall Retrieval Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. GPROF changes TMI-V5 : V6 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

  3. August 2003 TMI (78.1 mm/month) AMSR-E (75.2 mm/month)

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

  5. PR/TMI Global Bias Map

  6. Rainfall Bias RemovalBased on Column Water Vapor

  7. 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

  8. With initial assumptions

  9. With updated assumptions

  10. Measure Z/Tb Compare Compute Z/Tb Compute Rainfall Compare Measure Rsfc Validation of core satellite algorithm ? 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

  11. 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

  12. Rain Rate Probability of Rain Sigma Rain GPROF V6

  13. 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

  14. Rainfall Detection Errors

  15. Rainfall Detection Errors February 1, 2000

  16. PR/TMI Bias vs. Column Water Vapor

More Related