Annual Report on JCSDA Community Radiative Transfer Model (CRTM)

1
Annual Report onJCSDA Community Radiative
Transfer Model (CRTM)

• Fuzhong Weng
• NOAA/NESDIS/Office of Research and Applications
• and
• Joint Center for Satellite Data Assimilation

JCSDA Science Steering Committee Meeting, College
Park, MD August 7-8, 2006
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Outline

• FY05 Major Accomplishments
• Overview of CRTM Version-1
• Impacts of CRTM on NWP Model Forecasts
• Summary
• Next Steps

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Community Contributions

• Community Research Radiative transfer science
• AER. Inc Optimal Spectral Sampling (OSS) Method
• University of Colorado Microwave radiative
  transfer model
• UCLA Delta 4 stream vector radiative transfer
  model
• UMBC Aerosol transmittance model
• UWisc Scattering model using successive order
  of iteration
• Princeton Univ Snow emissivity model
  improvement
• STAR Snow, sea ice, microwave land emissivity
  models, vector discrete ordinate radiative
  transfer (VDISORT), ocean polarimetric,
  scattering models
• Core team (STAR/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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Major Accomplishments

• CRTM integration into the GSI at NCEP/EMC
• Fast optical LUT for cloud and precipitation
  scattering/absorption, asymmetric factor
• Microwave snow and sea ice emissivity models
  updated for SSMIS and MHS
• Advanced Double and Adding radiative transfer
  solver
• Fast Zeeman splitting parameterization
• UCLA delta-4-stream method
• AER software for OSS coefficients generation
• University of Wisconsin SOI scattering schedule
• CU discrete ordinate microwave radiative transfer
  model
• PU intercomparison results on snow emissivity
  model

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JCSDA Road Map (2002 - 2010)
By 2010, a numerical weather prediction community
will be empowered to effectively assimilate
increasing amounts of advanced satellite
observations
The radiances can be assimilated under all
conditions with the state-of-the science NWP
models
Resources
NPOESS sensors ( CMIS, ATMS) GOES-R
OK
Deficiency
The CRTM includes scattering polarization from
cloud, precip and surface
Advanced JCSDA community-based radiative transfer
model, Advanced data thinning techniques
The radiances from advanced sounders will be
used. Cloudy radiances will be tested under
rain-free atmospheres, and more products (ozone,
water vapor winds) are assimilated
AIRS, ATMS, CrIS, VIIRS, IASI, SSM/IS, AMSR,
more products assimilated
Science Advance
A beta version of JCSDA community-based radiative
transfer model (CRTM) transfer model will be
developed, including non-raining clouds, snow and
sea ice surface conditions
Improved JCSDA data assimilation science
The radiances of satellite sounding channels were
assimilated into EMC global model under only
clear atmospheric conditions. Some satellite
surface products (SST, GVI and snow cover, wind)
were used in EMC models
AMSU, HIRS, SSM/I, Quikscat, AVHRR, TMI, GOES
assimilated
Pre-JCSDA data assimilation science
Radiative transfer model, OPTRAN, ocean microwave
emissivity, microwave land emissivity model, and
GFS data assimilation system were developed
2002
2008
2009
2003
2010
2004
2007
2005
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Community Radiative Transfer Model
public interfaces
Forward CRTM
CRTM Initialization
CRTM Clean-up
Jacobian CRTM
SfcOptics (Surface Emissivity Reflectivity
Models)
AerosolScatter (Aerosol Absorption Scattering
Model)
AtmAbsorption (Gaseous Absorption Model)
CloudScatter (Cloud Absorption Scattering Model)
RTSolution (RT Solver)
Source Functions
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CRTM Supported Instruments

• TIROS-N to NOAA-18 AVHRR
• TIROS-N to NOAA-18 HIRS
• GOES-8 to 13 Imager channels
• GOES-8 to 13 sounder channel 08-13
• GOES-R ABI
• Terra/Aqua MODIS Channel 1-10
• METEOSAT-SG1 SEVIRI
• Aqua AIRS
• Aqua AMSR-E
• Aqua AMSU-A
• Aqua HSB
• NOAA-15 to 18 AMSU-A
• NOAA-15 to 17 AMSU-B
• NOAA-18 MHS
• TIROS-N to NOAA-14 MSU
• DMSP F13 to15 SSM/I
• DMSP F13,15 SSM/T1
• DMSP F14,15 SSM/T2
• DMSP F16 SSMIS

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Radiative Transfer Theory
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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.
Analytically compute layer sources from above
layer transmittance and reflectance.
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, JAS
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Gaseous Transmittance Model (AtmAbsorption)
A0
Level 0
A1
Level 1
estimated 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
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Radiance Jacobians with respect to temperature,
compared with LBLRTM
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Optimal Spectral Sampling for Gaseous Absorption

• OSS has been integrated into CRTM. Tests and
  evaluations have been performed on different
  computing environments.
• Initial results show the need to improve the
  implementation for computational efficiency.
  Several areas have been identified for
  improvement.

OSS algorithm
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Zeeman-Splitting Effects for SSMIS
Algorithm has been developed and is being
implemented into CRTM
Upper-air sounding channel specification
Channel Center frequency (GHZ) 1st IF (MHz) 2nd IF (MHz) Bandwidth per passband (MHz) Polarization NEdT (K)
19 63.283248 -285.271 0 1.35 LCP 1.76
20 60.792668 -357.892 0 1.35 LCP 1.80
21 60.792668 -357.892 -2 1.26 LCP 1.27
22 60.792668 -357.892 -5.5 2.62 LCP 0.7
23 60.792668 -357.892 -16 7.17 LCP 0.43
24 60.792668 -357.892 -50 26.33 LCP 0.44
Chan 19 the two passbands are centered
on the 62.9980GHz (15) and 63.5685GHz (17) Chan
20 24 the two IF-1 passbands are centered on
the 60.4348GHz(7) and 61.1506GHz (9)
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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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Zeeman Effect Parameterization
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Predictors for 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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SSMIS Simulations vs. Measurements

• (SABER sounding data inputs)

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SSMIS Simulations vs. Measurements (cont.)

• (SABER sounding data inputs)

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Community Surface Emissivity Model
FASTEM-1/3 (English and Hewison, 1998)
OceanEM (full polarimetric, Weng and Liu, 2003)
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Surface Emissivity Modeling

• Open water two-scale roughness theory
• Sea ice Coherent reflection
• Canopy Four layer clustering scattering
• Bare soil Coherent reflection and surface
  roughness
• Snow/desert Random media

Weng et al (2001, JGR)
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IR Ocean Emissivity Model
c0 c4 are regression coefficients, obtained
through regression against Wu-Smith model.
The IR model is a parameterized Wu-Smith model
for rough sea surface emissivity
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Microwave Land Emissivity 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
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Optical Properties for Vegetation Canopy
q

• Geometric optics is applied because the leaf size
  is typically larger than wavelength
• Wegmuller et al.s derivation
• Canopy leaves are oriented
• Matzlers dielectric constant

b
d
H
d 0.20 mm
d - leaf thickness H - canopy height LAI -
leaf area index md - dry matter content b - leaf
orientation angle q - incident angle of EM wave
d 0.10 mm
Single Scattering Albedo, w
d 0.05 mm
LAI 2 md 0.5 q 53.1
Frequency (GHz)
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Soil Dielectric and Roughness Models
Effective dielectric constant (Dobson et al.,
1985)
h
mv - volumatric moisture e - dielectric constant
of soil solids rb - density of soil rs - density
of solids S - sand fraction C - clay fraction h
- roughness height q- cross-polarization factor
Reflectivity (Choudhury et al. 1979)
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Optical Properties of Dense Medium
Small perturbation method (Tsang at al., 1985)
rp








d








Sub-surface
a 0.5 mm q 53.1
fa 0.3
fa 0.6
Single Scattering Albedo, w
fa - ice-volume fraction d - snow depth a -
snow particle size
fa 0.9
Frequency (GHz)
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IR Land Emissivity Database
24 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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Snow Microwave Emissivity Spectra
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Sea Ice Microwave Emissivity Spectra
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Other Snow Emissivity Models(Eric Wood,
Princeton University )

• All Seasons LSMEM (Drusch et al., 2001, 2004 Gao
  et al., 2004)
• Calculates microwave emission from a surface
  partially covered with vegetation and/or snow
• Snow component based on the semi-empirical HUT
  emission model
• Treats snowpack as a single homogeneous layer
• Dielectric constants of ice and snow calculated
  from different optional models
• Inputs include snow depth, density, temperature,
  grain size and ground temperature
• DMRT (Tsang et al, 2000)
• Calculates Tb from a densely packed medium
• A quasi-crystalline approximation is used to
  calculate absorption characteristics
• with particles allowed to form clusters
• The distorted Born approximation is used to
  calculate the scattering coefficients
• Inputs include snow depth, snow temperature,
  fractional volume and grain size
• MEMLS (Metzler, 1998)
• Calculates Tb from a multi-layer snow medium
• The absorption coefficient is derived from snow
  density, frequency and temperature
• The scattering depens on snow density, frequency
  and correlation length
• Inputs include snow depth, temperature,density,
  ground temperature and correlation length

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Model Testing and Validation
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AMSR-E Simulations Using NESDIS and UK Ocean
Emissivity Models
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AMSR-E Simulations Using NESDIS and UK Ocean
Emissivity Models
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SSMIS Cloudy Radiance Assimilation
The warm Core of Katrina is captured very well
from SSMIS 54 GHz (Liu andWeng, GRL, 2006)
SSMIS sounding channel radiances under all
weather conditions are assimilated through GSI
in GDAS. Shown is the temp difference between
test and control at Sigma level of 0.5
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Direct SSMIS Cloudy Radiance Assimilation
DMSP F-16 SSMIS radiances is at the first time
assimilated using NCEP 3Dvar data analysis. The
new data assimilation improves the analysis of
surface minimum pressure and temperature fields
for Hurricane Katrina. Also, Hurricane 48-hour
forecast of hurricane minimum pressure and
maximum wind speed was significantly improved
from WRF model
Significance Direct assimilation of satellite
radiances under all weather conditions is a
central task for Joint Center for Satellite Data
Assimilation (JCSDA) and other NWP centers. With
the newly released JCSDA Community Radiative
Transfer Model (CRTM), the JCSDA and their
partners will be benefited for assimilating more
satellite radiances in global and mesoscale
forecasting systems and can improve the severe
storm forecasts in the next decade
The initial temperature field from control run
(left panels) w/o uses of SSMIS rain-affected
radiances and test run (right panels) using
SSMIS rain-affected radiances
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Summary

• US Joint center for satellite data assimilation
  program has developed a community radiative
  transfer model (CRTM) framework to effectively
  transition to operations the various fast
  radiative transfer schemes and components
• Currently, the CRTM Version-1 has been used in
  NCEP GSI, including OPTRAN, IR/MW emissivity
  models, and ADA
• For surfaces that cant be simulated well to the
  accuracy needed for NWP, we develop a class of
  emissivity data base from retrievals. This
  process also made the radiative transfer also
  sensor-dependent from surface models
• The version 2 will likely include the SSMIS
  Zeeman-affected channels, ensemble IR ocean
  surface emissivity model, SARTA, OSS, RTTOV
  interface, and aerosol components.
• Preliminary tests show that impacts of CRTM on
  analysis fields are positive. Impacts of the
  emissivity models alone on global 6-7 forecasts
  are also assessed and significant.

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Next Steps

• Version control community distribution
• CRTM repository site, IBM (development) server
  accessed outside NOAA fire wall
• Version control
• A list of capabilities and components
• Global and region sets for testing (ITSC,
  Cloudsat matched with NWPs global vs regional)
• CRTM including fraction coverage, type, and
  inhomogeneity
• Surface (main and subtype)
• Atmosphere (e.g. aerosol, cloud and precip)
• Interaction with LDAS, other groups
• A simple weighting or aggregation
• CRTM extended to shorter wavelengths
• Passway to shorter wavelength
• Visible/UV with molecular scattering
• Earth curvature effect
• Currently, UMBC IR (HIRS)
• Interface between NWP surface physical models vs.
  emissivity models
• Snow density, and particle profiles
• Sand and clay, and particle profiles
• Vegetation type, canopy water profile