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Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
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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*
    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.
  • 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)
    • 7. The South China Sea Monsoon Experiment (SCSMEX)
    • 8. The TRMM Large-Scale Biosphere-Atmosphere Experiment in Amazonia (TRMM LBA)
  • 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
    • 9. 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.
  • 10. 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)
    • 11. Masunaga and Kummerow (2005)
    • 12. Kummerow et al. (2011)
    Six kinds of a-priori rainfall databases !
  • 13. 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).
    • 14. The microphysical processes related to ice-phase in the WDM6 are identical to the WSM6 scheme.
    • 15. WDM6 is double moment scheme for (only) warm rain processes and it predicts a cloud condensation nuclei (CCN) number concentration.
  • Typhoon Jangmi Simulation with Six different Microphysics schemes in the WRF Model
    • Similar distributions of rain and cloud water compared to WSM6
    • 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
    • 23. Less rain water
  • 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.)
    • 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
  • 25.
    • 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
    • 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
  • 31. 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).
    • 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