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 firstname.lastname@example.org, email@example.com
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
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)
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.
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.
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)
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.
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
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.