Development and Implementation Progress of Community Radiative Transfer Model (CRTM)

1
Development and Implementation Progress of
Community Radiative Transfer Model (CRTM)
Yong Han JCSDA/NESDIS
P. van Delst, Q. Liu, F. Weng, Y. Chen, D. Groff,
B. Yan, N. Nalli, R. Treadon, J. Derber and Y.
Han at JCSDA
JCSDA Workshop, May 31, 2006 Greenbelt Marriott
Hotel
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Community Contributions

• Community Research Radiative transfer science
• AER. Inc Optimal Spectral Sampling (OSS) Method
• NRL Improving Microwave Emissivity Model (MEM)
  in deserts
• NOAA/ETL Fully polarmetric surface models and
  microwave radiative transfer model
• UCLA Delta 4 stream vector radiative transfer
  model
• UMBC aerosol scattering
• UWisc Successive Order of Iteration
• CIRA/CU SHDOMPPDA
• UMBC SARTA
• Princeton Univ snow emissivity model
  improvement
• NESDIS/ORA Snow, sea ice, microwave land
  emissivity models, vector discrete ordinate
  radiative transfer (VDISORT), advanced
  double/adding (ADA), ocean polarimetric,
  scattering models for all wavelengths
• Core team (ORA/EMC) Smooth transition from
  research to operation
• Maintenance of CRTM (OPTRAN/OSS coeff.,
  Emissivity upgrade)
• CRTM interface
• Benchmark tests for model selection
• Integration of new science into CRTM

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Outline

• Major progress
• CRTM-v1 implementation
• Ongoing projects

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Major Progress

• CRTM has been integrated into the GSI at NCEP/EMC
  (Dec. 2005)
• Beta version CRTM has been released to the public
• CRTM with OSS (Optimal Spectral Sampling) has
  been preliminarily implemented and is being
  evaluated and improved.
• New postdoc Yong Chen has recently joined CRTM
  development team.

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CRTM-v1 implementation
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CRTM Major Modules
public interfaces
Forward CRTM
CRTM Initialization
CRTM Destruction
Jacobian CRTM
SfcOptics (Surface Emissivity Reflectivity
Models)
AerosolScatter (Aerosol Absorption Scattering
Model)
AtmAbsorption (Gaseous Absorption Model)
CloudScatter (Cloud Absorption Scattering Model)
still developing
RTSolution (RT Solver)
Source Functions
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Gaseous Transmittance Model (AtmAbsorption)Compac
t OPTRAN
A0
Level 0
A1
Level 1
estimate layer transmittance
Channel transmittance definition
An-1
Level n-1
spectral response function
An
Level n
Surface
K absorption coefficient of an absorber A
integrated absorber amount Pj predictors aj
constants obtained from regression

• Currently water vapor and ozone are the only
  variable trace gases and other trace gases are
  fixed.
• The model provides good Jacobians and is very
  efficient in using computer memory

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Radiance errors due to transmittance model
uncertainty
Radiance Jacobians with respect to water vapor,
compared with LBLRTM
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Surface Emissivity/Reflectivity Module and
Sub-modules
IR EM module over land
IR EM module over ocean
IR EM module over Snow
IR EM module over Ice
Surface Emissivity/ Reflectivity Module
MW EM module over land
MW EM module over ocean
MW EM module over Snow
MW EM module over Ice
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IR Sea Surface Emission Model (IRSSE)
c0 c4 are regression coefficients, obtained
through regression against Wu-Smith model (1997).
The IRSSE model is a parameterized Wu-Smith model
for rough sea surface emissivity
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IR emissivity database for land surfaces
Surface types included in the IR emissivity
database (Carter et al., 2002)
Surface Type Surface Type
Compacted soil Grass scrub
Tilled soil Oil grass
Sand Urban concrete
Rock Pine brush
Irrigated low vegetation Broadleaf brush
Meadow grass Wet soil
Scrub Scrub soil
Broadleaf forest Broadleaf(70)/Pine(30)
Pine forest Water
Tundra Old snow
Grass soil Fresh snow
Broadleaf/Pine forest New ice
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NESDIS Microwave Land Emission Model (LandEM)

• (1) Three layer medium

desert, canopy,
(2) Emissivity derived from a two-stream
radiative transfer solution and modified
Fresnel equations for reflection and transmission
at layer interfaces
Weng, et al, 2001
Conditions using LandEM over land f lt 80
GHz, use LandEM f gt 80 GHz, e_v e_h 0.95
over snow f lt 80 GHz, use LandEM f gt 80 GHz,
e_v e_h 0.90
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Microwave empirical snow and ice surface
emissivity model
(1) Emissivity Database
(2) Snow type discriminators are used to pick up
snow type and emissivity
Tb,j e.g. AMSU window channel
measurements
(3) Supported sensors AMSU, AMSRE, SSMI, MSU,
SSMIS
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Microwave Ocean Emissivity Model
FASTEM-1 (English and Hewison, 1998)
Model inputs satellite zenith angle, water
temperature, surface wind speed, and
frequency Model outputs emissivity
(Vertical polarization) and emissivity
(horizontal polarization)
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Cloud Absorption/Scattering LUT

• Six cloud types water, ice, rain, snow, graupel
  and hail
• NESDIS/ORA lookup table (Liu et al., 2005) mass
  extinction coefficient, single scattering albedo,
  asymmetric factor and Legendre phase
  coefficients. Sources
• IR spherical water cloud droplets (Simmer,
  1994) non-spherical ice cloud particles (Liou
  and Yang, 1995 Macke, Mishenko et al. Baum et
  al., 2001).
• MW spherical cloud, rain and ice particles
  (Simmer, 1994).

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RTSolution Advanced Doubling-Adding Method (ADA)
AtmOptics Optical depth, single scattering
Albedo, asymmetry factor, Legendre coefficients
for phase matrix
Planck functions Planck_Atmosphere Planck_Surface
SfcOptics Surface emissivity reflectivity
Compute the emitted radiance and reflectance at
the surface (without atmosphere)
Compute layer transmittance, reflectance matrices
by doubling method.
(New algorithm) compute layer sources from above
layer transmittance and Reflectance
analytically.
Loop from bottom to top layers
Combine (transmittance, reflectance, upwelling
source) current level and added layers to new
level
Output radiance
1.7 times faster then VDISORT 61 times faster
than DA Maximum differences between ADA,VDISORT
and DA are less than 0.01 K.
Liu and Weng, 2006
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Ongoing Development
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Fast RT algorithm for SSMIS upper-Air sounding
channels affected by Zeeman-splitting
Zeeman Effect
Ch20
Ch19
Ch21
Height (km)
Ch22
Ch23
Ch24
SSMIS upper-air sounding Channel weighting
functions
Weighting function (km-1)
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Predictors for estimating absorption coefficients
Channels Predictors
19, 20 ?, cos?B, cos2?B, ?cos2?B, B-1, B-2, cos2?B/B2
21 ?, cos2?B, B-1, B-2, B-3, B-4, cos2?B/B2
22, 23, 24 ?, ?2, cos2?B, B-1

• 300./T, B Earth magnetic field magnitude
• ?B angle between magnetic field and propagation
  direction.

RMS errors, compared with LBL model
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Nick Nalli's Ensemble IR Ocean Surface Emissivity
Model

• Properly accounts for reflected downwelling
  radiance. Conventional approach to modeling IR
  surface-leaving radiance results in systematic
  underestimation of surface leaving radiance.
• The approach shows good agreement with M-AERI
  from CSP and AEROSE. Amounts to a 0.15-0.3
  correction in emissivity 0.1-0.2K correction in
  bias.
• Work beginning on integration into the CRTM.

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Ongoing Development (Cont.)

• CRTM-OSS improvement OSS LUT-generation software
  transfer from AER to JCSDA.
• UMBC SARTA forward algorithm implementation
  SARTA TL and AD model development (Dr. Yong Chen)
• RTTOV transmittance module integration (Dr. Roger
  Saunders)
• OPTRAN-v7 improvement and integration
• Aerosol component development
• Visible component development
• CRTM test and validation

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Summary

• CRTM has been successfully integrated in the
  NCEP/EMC GSI.
• CRTM-v1 is implemented with the following models
  OPTRAN, IRSSE, LandE, NESDIS MW snow/ice
  empirical surface emissivity models and ADA
  radiative transfer solver.
• CRTM-OSS has been preliminarily implemented,
  tested and evaluated. Several areas have been
  identified for improvement. The OSS LUT software
  is being transferred to JCSDA.
• Ongoing development projects also include fast
  RT algorithm for SSMIS Zeeman-affected channels,
  ensemble IR ocean surface emissivity model,
  integrations of OPTRAN-v7, SARTA and RTTOV and
  developments of aerosol and visible components.