1. Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements Ju-Hye Kim and Dong-Bin Shin* Department of Atmospheric Sciences Yonsei University, Seoul, Republic of Korea jhkim07@yonsei.ac.kr, dbshin@yonsei.ac.kr

2. Outline 1. Introduction (motivation) 2. Methodology (characteristics of different microphysics schemes) 3. Impacts of microphysics on a-priori databases 4. Impacts of microphysics on PMW rainfall retrievals 5. Conclusions

3. Cloud water + DSD Rain water + DSD Snow + , DSD Graupel + , DSD Cloud ice + DSD Hail + , DSD Water Vapor Temperature Introduction Current physically-based PMW rainfall algorithms heavily rely on CRM simulations. Simulated TB RTM e.g., Plane-Parallel , MC models Forward models provide prior information Cloud Model * e.g., Goddard Cumulus Ensemble Model (GCE),. .... Assumptions in some parameters (e.g., microphysics)

4. observed simulated Introduction CRM-based rainfall retrieval algorithms have been evolved to use CRMs and observations simultaneously. e.g., The parametric rainfall algorithm: Cloud model + TRMM PR/TMI observations (1st version, Shin & Kummerow, 2003) Simulated precipitation field TB computation simulated observed Realistic set of 3-D geophysical parameters are created from combination of TRMM PR/TMI and CRM. Figure at left is a comparison of surface rainfall from TRMM PR and simulator. Once 3-D geophysical parameters are constructed, TB can be computed for any current or planned sensor. Figure at right is a comparison of Tb from TRMM TMI and simulator.

7. The South China Sea Monsoon Experiment (SCSMEX)

11. Masunaga and Kummerow (2005)

12. Kummerow et al. (2011)Six kinds of a-priori rainfall databases !

14. The microphysical processes related to ice-phase in the WDM6 are identical to the WSM6 scheme.

16. Reduction of snow near and above the melting layer

17. More rain water and more ice particle than WSM6

18. Increased rain water below 5 km altitude

19. Similar distribution of ice particle compared to WSM6

20. Much more snow

21. Less rain water

22. More snow

24. The simulated and observed databases show relatively large discrepancy at 85 GHz scattering index (Sm).WDM6  Simulated Indices MORR  SM85 PM37 PM10 PM19 PM85 Observed Indices

26. Simulated DBs generally follow the pattern of the Obs. DB. (smaller variability in 10, 19, and 37 GHz attenuation indices. Larger variability in 85 GHz attenuation index). / PLIN / / WSM6, WDM6 / Difference between Obs. and Simulated DBs / GCE, THOM, MORR /

27. Impacts of microphysics on rainfall retrievals Orbit : 36537 Retrieved rainfall distributions for Ty Sudal PR 2A25 TMI 2A12

28. Scatter plots of PR vs retrieved rain rates for Ty Sudal Retrieved rainfall PR rainfall

29. Retrieval statistics for different rain types (convective vsstratiform) PR 2A23 Convective Yellow : Convective Blue : Stratiform Stratiform

30. Comparison of averaged hydrometeor amounts In the databases PLIN ~ Too much graupel In the retrieval s THOM ~ Too much snow WDM6 ~ Increased rain water and reduced cloud water

32. Major difference in six microphysics schemes exists in their cold rain processes (ice initiation, sedimentation property of solid particles).

33. PLIN and THOM schemes produce too much graupel and snow, respectively, while the ice processes seem to be comparable to those from WSM6 and WDM6.

34. This study suggests that uncertainties associated with cloud microphysics affect significantly PMW rainfall measurements (at least for extreme events).  Both intensity and distribution of retrieved rainfalls are better represented by the WDM6, WSM6 and Goddard microphysics-based DBs.WSM6 Goddard Thompson WDM6 Morrison