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
[email protected],
[email protected]
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
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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
Cloud water + DSD Rain water + DSD Snow + , DSD Graupel + , DSD Cloud ice + DSD Hail + , DSD Water Vapor Temperature
Assumptions in some parameters (e.g., microphysics)
* e.g., Goddard Cu mulus Ensemble Mo del (GCE),. ....
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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. simulated
observed
Figure at right is a comparison of Tb from TRMM TMI and simulator. Atmospheric Remote Sensing Laboratory
Obs. Tb vs Sim. 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.
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.
10 GHz H
10 GHz V
19 GHz H
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.
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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)
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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.
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Methodology Different Cloud Microphysics PLIN
Typhoon Jangmi Simulations with WRF model (V3.1)
WSM6
TRMM Observation of Typhoon Sudal 36522
36532
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 ! Atmospheric Remote Sensing Laboratory
Prognostic variable of Single-moment scheme
PLIN
Ty Jangmi Simulation with WRF model
WSM6
Single-Moment
Goddard Thompson
+ Ns, Ng, Nr
WDM6
+ Nccn, Nc, Nr
Morrison
+ Ns, Ng, (Nc, Nr)
Double-Moment 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. Atmospheric Remote Sensing Laboratory
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
Much more snow Less rain water
Increased rain water below 5 km altitude Similar distribution of ice particle compared to WSM6
More snow Less rain water
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Impacts of microphysics on a-priori databases Correctness of simulated DBs PLIN
Modified Radiative Indices WSM6
Petty (1994) Biggerstaff and Seo (2010)
P
GCE
TV TH T V,0 T H,0
S PT V,0 (1 P)T C T V P m 100(1 P)
Simulated Indices
THOM
S m S 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.)
WDM6
MORR PM10
PM19
Observed Indices
PM37
PM85
SM85
The simulated and observed databases show relatively large discrepancy at 85 GHz scattering index (Sm).
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Representativeness of simulated DBs First EOF vector of Radiance indices P m10 P m19 I Pm37 Pm85 S m85
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 / Difference between Obs. and Simulated DBs
/ WSM6, WDM6 / / GCE, THOM, MORR /
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Impacts of microphysics on rainfall retrievals Orbit : 36537 Retrieved rainfall distributions for Ty Sudal PR 2A25
TMI 2A12
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Retrieved rainfall
Scatter plots of PR vs retrieved rain rates for Ty Sudal
PR rainfall
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Retrieval statistics for different rain types (convective vs stratiform)
PR 2A23
Convective Mean Retrived 15.03 11.94 12.84 27.82 13.58 12.78 12.11 7.84 True
Lin WSM6 Goddard Thompson WDM6 Morrison 2a12
Std dev True Retrived 24.82 16.93 15.74 36.46 16.80 16.87 14.55 7.24
Corr 0.44 0.84 0.85 0.78 0.89 0.84 0.54
Rms 36.25 (241.2) 28.68 (240.2) 28.74 (223.8) 29.17 (214.8) 27.18 (212.7) 29.92 (247.1) 38.63 (492.7)
Bias -12.79 (85.1) -15.88 (133.0) -14.98 (116.7) -14.24 (104.9) -15.04 (117.7) -15.71 (129.7) -19.98 (254.9)
Yellow : Convective Blue : Stratiform
Stratiform
Lin WSM6 Goddard Thompson WDM6 Morrison 2a12
Mean Retrive True d 7.71 10.45 10.21 10.17 11.23 10.83 9.73 6.68
Std dev True
Retrived
Corr
Rms
Bias
14.52
10.28 12.14 11.01 12.36 11.97 9.44 5.40
0.46 0.77 0.72 0.65 0.76 0.71 0.52
13.59 (176.3) 9.34 (89.4) 10.02 (98.1) 11.42 (101.7) 9.58 (88.5) 10.29 (105.8) 13.05 (195.4)
-2.47 (32.0) 0.28 (2.7) 0.04 (0.4) 1.06 (9.4) 0.66 (6.1) -0.45 (4.6) -3.50 (52.4)
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Comparison of averaged hydrometeor amounts In the databases Cloud water
Rain water
Snow
Graupel
PLIN WSM6 GCE
0.26 (24.7%)
0.79 (75.3%)
0.07 (13.5%)
0.44 (86.5%)
0.19 (19.5%)
0.78 (80.5%)
0.28 (49.6%)
0.28 (50.4%)
0.33 (29.5%)
0.79 (70.5%)
0.27 (54.5%)
0.23 (45.5%)
THOM
0.31 (27.9%)
0.79 (72.1%)
0.79 (92.7%)
0.06 (7.3%)
WDM6
0.11 (12.5%)
0.80 (87.5%)
0.16 (36.9%)
0.27 (63.1%)
MORR
0.23 (22.5%)
0.77 (77.5%)
0.45 (73.4%)
0.16 (26.6%)
Cloud water
Rain water
Snow
Graupel
PLIN WSM6 GCE
0.30 (25.1%)
0.90 (74.9%)
0.10 (22.0%)
0.37 (78.0%)
0.26 (22.0%)
0.91 (78.0%)
0.33 (60.3%)
0.21 (39.7%)
0.37 (30.5%)
0.85 (69.5%)
0.45 (71.6%)
0.18 (28.4%)
THOM
0.36 (27.1%)
0.97 (72.9%)
1.18 (94.8%)
0.07 (5.2%)
WDM6
0.14 (12.4%)
1.00 (87.6%)
0.21 (42.9%)
0.28 (57.1%)
MORR
0.28(21.9%)
0.98 (78.1%)
0.63 (75.5%)
0.20 (24.5%)
PLIN ~ Too much graupel
In the retrieval s
WDM6 ~
THOM ~ Too much snow
Increased rain water and reduced cloud water
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Conclusions 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
WSM6 Goddard Thompson WDM6 Morrison
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.
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