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Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements

Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements. Ju-Hye Kim and Dong-Bin Shin*. Outline. 1. Introduction (motivation) 2. Methodology (characteristics of different microphysics schemes) 3. Impacts of microphysics on a-priori databases

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Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements

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  1. Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements Ju-Hye Kim and Dong-Bin Shin*

  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. simulated observed 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.

  5. Obs. Tb vsSim. Tb • The liquid portion of the • profile is matched, the CRMs • consistently specify ice • particles of an incorrect size • and density, which in turn • leads to lower than observed Tb. 10 GHz H 19 GHz H 10 GHz V • A better choice would be to • continue the development of • the Cloud Resolving Model • physics to insure that • simulations properly match the • observed relationship between ice scattering and the rainfall • column. 19 GHz V 21 GHz v 37 GHz H 85 GHz H/V 37 GHz V Assumptions in microphysics still have great impacts on CRM+OBS.-based DBs.

  6. Introduction Cloud Resolving Model Simulations Passive Microwave Rainfall Observations TRMM field campaigns • The Kwajalein Experiment (KWAJEX) • The South China Sea Monsoon Experiment (SCSMEX) • The TRMM Large-Scale Biosphere-Atmosphere Experiment in Amazonia (TRMM LBA)

  7. Zhou et al. (2007) • used the GCE model to simulate China Sea Monsoon and compared their simulated cloud products with TRMM retrieval products Lang et al. (2007) , Han et al. (2010) • Land et al. (2007) compared the calculated TBs and simulated reflectivities from cloud-radiative simulations (GCE model) of TRMM LBA domain with the direct observations of TRMM TMI and PR • Han et al. (2010) also evaluated five cloud microphysical schemes in the MM5 using observations of TRMM TMI and PR Grecu and Olson (2006) • constructed a-priori database from observation of TRMM PR and TMI only to reduce forward error related to cloud and radiative transfer calculations, and compared their retrieval results to products from GPROF version-6 operational algorithm Many studies pointed out that CRMs (mainly GCE model) tend to produce excessive ice particles above freezing level and it may bring wrong retrieval results in microwave remote sensing of precipitation.

  8. Methodology Different Cloud Microphysics PLIN TRMM Observation of Typhoon Sudal WSM6 TyphoonJangmi Simulations with WRF model (V3.1) 36532 36522 Goddard Thompson WDM6 Morrison Parametric rainfall algorithm • Shin and Kummerow (2003) • Masunaga and Kummerow (2005) • Kummerow et al. (2011) Six kinds of a-priori rainfall databases !

  9. Prognostic variable of Single-moment scheme Ty JangmiSimulation with WRF model Single-Moment PLIN WSM6 + Ns, Ng, Nr Goddard + Nccn, Nc, Nr Thompson + Ns, Ng, (Nc, Nr) WDM6 Double-Moment Morrison • Single moment schemes have differences in their cold rain processes (ice initiation, sedimentation property of solid particles). • The microphysical processes related to ice-phase in the WDM6 are identical to the WSM6 scheme. • WDM6 is double moment scheme for (only) warm rain processes and it predicts a cloud condensation nuclei (CCN) number concentration.

  10. Typhoon Jangmi Simulation with Six different Microphysics schemes in the WRF Model • Similar distributions of rain and cloud water compared to WSM6 • Reduction of snow near and above the melting layer • More rain water and more ice particle than WSM6 • Increased rain water below 5 km altitude • Similar distribution of ice particle compared to WSM6 • Much more snow • Less rain water • More snow • Less rain water

  11. Impacts of microphysics on a-priori databases • Correctness of simulated DBs PLIN  Modified Radiative Indices Petty (1994) Biggerstaff and Seo (2010) WSM6  GCE  THOM  • For the emission indices, TBs agree well. (The biases at 10 GHz channel from six databases are quite small, especially when the WSM6 and WDM6 schemes are used.) • 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

  12. Representativeness of simulated DBs First EOF vector of Radiance indices • Observed database shows a positive variation for attenuation indices and negative variation for the scattering index • 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 /

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

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

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

  16. 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

  17. Conclusions PLIN • A-priori databases with six microphysics schemes are built by the WRF model V3.1 and TRMM PR observations and the impacts of the different microphysics on rainfall estimations are evaluated under the frame of parametric rainfall algorithm for extreme rain events (Typhoons). • Major difference in six microphysics schemes exists in their cold rain processes (ice initiation, sedimentation property of solid particles). • 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. • 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

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