Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements

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


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


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


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)


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.


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


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.


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


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


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


Impacts of microphysics on rainfall retrievals

PR 2A25

TMI 2A12

Retrieved rainfall distributions for Ty SudalOrbit : 36537


Scatter plots of PR vs retrieved rain rates for Ty Sudal

PR rainfall

Ret

rieve

d ra

infa

ll


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


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


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

Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements

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