Atmospheric Remote Sensing Laboratory

Report
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
Atmospheric Remote Sensing Laboratory
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),. ....
Atmospheric Remote Sensing Laboratory
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.
Atmospheric Remote Sensing Laboratory
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)
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
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.
Atmospheric Remote Sensing Laboratory
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
Atmospheric Remote Sensing Laboratory
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
Atmospheric Remote Sensing Laboratory
Retrieved rainfall
Scatter plots of PR vs retrieved
rain rates for Ty Sudal
PR rainfall
Atmospheric Remote Sensing Laboratory
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)
Atmospheric Remote Sensing Laboratory
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
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
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.
Atmospheric Remote Sensing Laboratory

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