1. Megha Tropiques MADRAS algorithm status: BRAIN Franck Chopin (LMD/ICARE) Nicolas Viltard (CETP)

2. Principle of BRAIN BRAIN is a bayesian-based algorithm meant to retrieve rain and precipitation profile from TMI data Its retrieval database is made of co-located PR and TMI data It works over land and ocean with slightly different principles and database (only 85 Ghz over land) Colocation example: Orbit 10915 Diamonds: PR pixels Bold Diamons: nadir PR Pixel Plus: TMI pixels Bold stars: Middle of TMI swath => Position of PR and TMI relative center swath changes during the TRMM revolutions Blue line : nadir PR Pixel Green Lines: Middle of TMI swath

3. Principle of BRAIN database building

4. Flow-diagram of BRAIN retrieval part DATABASE of Profiles and TB Retrieval databaseTest Database Bayesian approach for retrieval Retrieved rain Retrieval Error assessment

5. BRAIN database characteristics and “sanity checks” Database histogram of rain intensity occurence Error and S. Dev of error for validation dbase Brain vs. PR for validation dbase

6. Retrieval example “Reference”: PR rain at 37 Ghz resolution

7. Flow-diagram of BRAIN TB simulation DATABASE of Profiles and TB Retrieval databaseTest Database Bayesian approach for retrieval Retrieved rain Retrieval Error assessment TB Simulation from profiles RTM Error assessment Micropysics Testing TMI TB

8. Tbs observed Tbs simulated from PR swath +cloud model TB simulation from dbase “scenes” and comparison with TMI

9. Tbs observed Tbs simulated from PR swath +cloud model The 85GHz is particularly sensitive to ice parameterization and specially the density-diameter law used in RTM Two realisations of TB 85 Ghz H, with only the mass-diameter that was changed... TB simulation and influence of ice parametrisation in RTM

10. Flow-diagram of BRAIN for other satellites DATABASE of Profiles and TB Retrieval databaseTest Database TB Simulation from profiles Bayesian approach for retrieval Retrieved rain Retrieval Error assessment Database for Other platforms RTM Error assessment Microphysics Testing TMI TB

11. Example: adaptation for SSM/I beta version No transfert radiative performed, just Tb and rain resolution changed

12. V21 H19 H37 H85 GREEN : SSMI HISTOGRAM RED : RESCALED TRMM HISTOGRAM HISTOGRAM COMPARISON BETWEEN SSM/I AND TRMM

13. Flow-diagram of BRAIN integrated with all platforms DATABASE of Profiles and TB Retrieval databaseTest Database TB Simulation from profiles Bayesian approach for retrieval Retrieved rain Retrieval Error assessment Database for Other platforms RTM Error assessment Microphysics Testing Combining different instruments for global estimates TMI TB

14. Conclusions Still a lot of work to be done... Adaptation to MADRAS (code part) should start early 2006 (6 months) MADRAS beta database should start also early 2006 (3 months) Complete base with radiative transfer should be done by end of 2007 with improved ice-phase (probable start after AMMA) Use of 157 Ghz will be studied in parallel Open questions What about coupling of MADRAS and SAPHIR ? Should we use ancillary data ? What about coupling with MSG ? => nicolas.viltard@cetp.ipsl.fr