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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 - PowerPoint PPT Presentation
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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 [email protected], [email protected]
Atmospheric Remote Sensing Laboratory
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 retrievals5. Conclusions
Atmospheric Remote Sensing Laboratory
Cloud water + DSDRain water + DSDSnow + , DSDGraupel + , DSDCloud ice + DSDHail + , DSD
Water Vapor Temperature
RTM
Simulated TB
Cloud Model
e.g., Plane-Parallel , MC models
* e.g., Goddard Cumulus Ensem-ble Model (GCE),. ....
Current physically-based PMW rain-fall algorithms heavily rely on CRM simulations.
Assumptions in some parame-ters (e.g., microphysics)
Forward models pro-vide prior in-formation
Introduction
Atmospheric Remote Sensing Laboratoryobservedsimulated
simulated observedTB computation
e.g., The parametric rainfall algorithm: Cloud model + TRMM PR/TMI observations(1st version, Shin & Kummerow, 2003)
Introduction
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.
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.
CRM-based rainfall retrieval algorithms have been evolved to use CRMs and observations simultane-ously.
Simulated precipitation field
Atmospheric Remote Sensing Laboratory
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.
85 GHz H/V
Obs. Tb vs Sim. Tb
Assumptions in microphysics still have great impacts on CRM+OBS.-based DBs.
10 GHz H 10 GHz V 19 GHz H
19 GHz V 21 GHz v 37 GHz H
37 GHz V
Atmospheric Remote Sensing Laboratory
Introduction
Passive Mi-crowave Rainfall
Observations
Cloud Resolv-ing Model
Simulations
TRMM field campaigns The Kwajalein Experiment (KWAJEX) The South China Sea Monsoon Experiment (SCSMEX) The TRMM Large-Scale Biosphere-Atmosphere Experi-
ment in Amazonia (TRMM LBA)
Atmospheric Remote Sensing Laboratory
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 reflectivi-
ties 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 pro-duce excessive ice particles above freezing level and it may bring wrong retrieval results in microwave remote sensing of precipitation.
Atmospheric Remote Sensing Laboratory
36522 36532
Methodology
Typhoon Jangmi Simulations with WRF model (V3.1)
PLIN
WSM6
Goddard
Thompson
WDM6
Morrison
TRMM Observation of Typhoon Sudal
Six kinds of a-priori rainfall databases !
Parametric rainfall algorithmShin and Kummerow (2003)Masunaga and Kummerow (2005)Kummerow et al. (2011)
Different Cloud Microphysics
Atmospheric Remote Sensing Laboratory
Ty Jangmi Simulation with WRF model
PLIN
WSM6
Goddard
Thompson
WDM6
Morrison
Single-Moment
Double-Moment
Prognostic variable of Single-moment scheme
+ Ns, Ng, Nr
+ Nccn, Nc, Nr
+ Ns, Ng, (Nc, Nr)
Single moment schemes have differences in their cold rain pro-cesses (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 con-centration.
Atmospheric Remote Sensing Laboratory
Typhoon Jangmi Simulation with Six different Microphysics schemes in the WRF Model
More rain wa-ter 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
Similar distribu-tions of rain and cloud water compared to WSM6
Reduction of snow near and above the melt-ing layer
More snow Less rain water
Atmospheric Remote Sensing Laboratory
Impacts of microphysics on a-priori databases
Modified Radiative IndicesPetty (1994)Biggerstaff and Seo (2010)
H,0V,0
HV
TTTTP
VCV,0 TP)T(1PTS
P)100(1Pm SSm
For the emission indices, TBs agree well. (The biases at 10 GHz channel from six data-bases are quite small, espe-cially when the WSM6 and WDM6 schemes are used.)
The simulated and observed databases show relatively large discrepancy at 85 GHz scattering index (Sm).
Observed Indices
Sim
ulat
ed In
dice
s
PLIN
WSM6
GCE
THOM
WDM6
MORR
PM10 PM19 PM85PM37 SM85
Correctness of simulated DBs
Atmospheric Remote Sensing Laboratory
m85
m85
m37
m19
m10
SPPPP
I
First EOF vector of Radiance indices
Observed database shows a positive variation for attenua-tion indices and negative variation for the scattering index
Simulated DBs generally fol-low 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 /
/ GCE, THOM, MORR /Difference between Obs. and Simulated
DBs
Representativeness of simulated DBs
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Impacts of microphysics on rainfall retrievals
PR 2A25
TMI 2A12
Retrieved rainfall distributions for Ty SudalOrbit : 36537
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Scatter plots of PR vs retrieved rain rates for Ty Sudal
PR rainfall
Ret
rieve
d ra
infa
ll
Atmospheric Remote Sensing Laboratory
Mean Std devTrue Retrived True Retrived Corr Rms Bias
Lin
27.82
15.03
36.46
24.82 0.44 36.25 (241.2) -12.79 (85.1)WSM6 11.94 16.93 0.84 28.68 (240.2) -15.88 (133.0)Goddard 12.84 15.74 0.85 28.74 (223.8) -14.98 (116.7)Thompson 13.58 16.80 0.78 29.17 (214.8) -14.24 (104.9)WDM6 12.78 16.87 0.89 27.18 (212.7) -15.04 (117.7)Morrison 12.11 14.55 0.84 29.92 (247.1) -15.71 (129.7)2a12 7.84 7.24 0.54 38.63 (492.7) -19.98 (254.9)
Mean Std dev
True Re-trived True Retrived Corr Rms Bias
Lin
10.17
7.71
14.52
10.28 0.46 13.59 (176.3) -2.47 (32.0)WSM6 10.45 12.14 0.77 9.34 (89.4) 0.28 (2.7)Goddard 10.21 11.01 0.72 10.02 (98.1) 0.04 (0.4)Thompson 11.23 12.36 0.65 11.42 (101.7) 1.06 (9.4)WDM6 10.83 11.97 0.76 9.58 (88.5) 0.66 (6.1)Morrison 9.73 9.44 0.71 10.29 (105.8) -0.45 (4.6)2a12 6.68 5.40 0.52 13.05 (195.4) -3.50 (52.4)
Retrieval statistics for differ-ent rain types (convective vs stratiform)
Convective
Stratiform
PR 2A23
Yellow : ConvectiveBlue : Stratiform
Atmospheric Remote Sensing Laboratory
Comparison of averaged hy-drometeor amounts
Cloud water Rain water Snow GraupelPLIN 0.26 (24.7%) 0.79 (75.3%) 0.07 (13.5%) 0.44 (86.5%)
WSM6 0.19 (19.5%) 0.78 (80.5%) 0.28 (49.6%) 0.28 (50.4%)
GCE 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 GraupelPLIN 0.30 (25.1%) 0.90 (74.9%) 0.10 (22.0%) 0.37 (78.0%)
WSM6 0.26 (22.0%) 0.91 (78.0%) 0.33 (60.3%) 0.21 (39.7%)
GCE 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%)
In the databases
In the retrieval s
THOM ~ Too much snow
PLIN ~ Too much graupel
WDM6 ~ Increased rain water and reduced cloud water
Atmospheric Remote Sensing Laboratory
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 mi-crophysics on rainfall estimations are evaluated under the frame of para-metric rainfall algorithm for extreme rain events (Typhoons).
Major difference in six microphysics schemes exists in their cold rain pro-cesses (ice initiation, sedimentation property of solid particles).
PLIN and THOM schemes produce too much graupel and snow, respec-tively, while the ice processes seem to be comparable to those from WSM6 and WDM6.
This study suggests that uncertainties associated with cloud microphysics af-fect 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.
PLIN
WSM6
Goddard
Thompson
WDM6
Morrison