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Joint Center for Satellite Data Assimilation Community Radiative Transfer Model CRTM: Surface Emissi

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Title: Joint Center for Satellite Data Assimilation Community Radiative Transfer Model CRTM: Surface Emissi


1
Joint Center for Satellite Data
AssimilationCommunity Radiative Transfer Model
(CRTM) Surface Emissivity Models
  • Fuzhong Weng
  • Sensor Physics Branch
  • Satellite Meteorology and Climatology Division
  • NOAA/NESDIS/Office of Research and Applications

The First Workshop of Remote Sensing and Modeling
of Surface Properties Observatoire de Paris
June 21-23, 2006
2
Outline
  • US Satellite Data Assimilation Program
  • Community Radiative Transfer Model (CRTM)
  • Community Surface Emissivity Model (CSEM)
  • Impacts on NWP Model Forecasts
  • Summary
  • Challenging Issues

3
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
4
CRTM Infrastructure
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
5
CRTM Status
Operational Developments
Supported Instruments
  • All wavelength applications
  • Advanced doubling and adding transfer scheme with
    several optional algorithms
  • Scattering optics for aerosol, 5 type
    hydrometeors
  • Analytic phase matrices (HG, HG-Rayleigh..)
  • Three gaseous absorption scheme (OPTRAN, OSS,
    SARTA)
  • Jacobian schemes
  • 4 IR/MW surface emissivitys
  • Integrated LBL data base (Planning phase)
  • Integrated validation data base (planning phase)
  • 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

6
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
  • Langley/Hampton Univ principal component
    radiative transfer
  • Princeton Univ snow emissivity model
    improvement
  • NESDIS/ORA Snow, sea ice, microwave land
    emissivity models, vector discrete ordinate
    radiative transfer (VDISORT), 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

7
Radiative Transfer Theory
8
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
9
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

10
Radiance errors due to transmittance model
uncertainty
Radiance Jacobians with respect to water vapor,
compared with LBLRTM
11
Radiance Jacobians with respect to temperature,
compared with LBLRTM
12
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
13
Zeeman-Splitting Effects for SSMIS
Algorithm has been developed and is being
implemented into CRTM
Upper-air sounding channel specification
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)
14
Zeeman Effect
Ch20
Ch19
Ch21
Height (km)
Ch22
Ch23
Ch24
SSMIS upper-air sounding Channel weighting
functions
Weighting function (km-1)
15
Zeeman Effect Parameterization
Line-by-line mode (Rosenkranz)
where t, P and G are 2x2 matrixes, i is the layer
index
Transmittance Parameterization (fast model)
Averaged over spectral band
Absorption coefficient, Left circular polarization
Regression, predicting absorption coefficient
16
Predictors for absorption coefficients
  • 300./T, B Earth magnetic field magnitude
  • ?B angle between magnetic field and propagation
    direction.

RMS errors, compared with LBL model
17
Community Surface Emissivity Model
FASTEM-1/3 (English and Hewison, 1998)
OceanEM (full polarimetric, Weng and Liu, 2003)
18
AMSR-E Simulations Using NESDIS and UK Ocean
Emissivity Models
19
AMSR-E Simulations Using NESDIS and UK Ocean
Emissivity Models
20
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
21
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
22
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)
23
Soil Dielectric, Roughness and Reflectivity 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)
24
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)
25
IR Land Emissivity Database
24 Surface types included in the IR emissivity
database (Carter et al., 2002)
26
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)
27
Snow Microwave Emissivity Spectra
28
Sea Ice Microwave Emissivity Spectra
29
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

30
Model Testing and Validation
31
Hurricane Katrina Analysis from AMSU/AMSR-E
Above two figures compare GDAS analysis
temperature field near 250 hPa with 1DVAR
retrievals and 4DVAR analysis. The temperature
field from analysis shows hurricane warm core is
about 2 degree warmer than GDAS analysis. Uses
of cloudy radiances under storm conditions
dramatically improve warm core structure. At 0600
UTC August 25, 2005, Katrina was at tropical
storm intensity, with the minimum central
pressure of 1000 hPa.
32
Hurricane Katrina Analysis from AMSU/AMSR-E
4DVAR
GDAS
The 1DVAR retrieval plus 4DVAR analysis shows
asymmetric surface temperature distribution,
with a 2 K cooling rainband at northeastern side,
which is consistent with the deep convections
shown on NOAA-17 satellite AVHRR channel 4 image.
Again, this feature is attributed to uses of more
AMSR-E radiances at 6 and 10 GHz which are
sensitive to SST
33
Impacts of Surface Emissivity
34
Impacts of Snow and Sea Ice Models
Anomaly Correlation for 700 hPa Geopotential
Height
35
IR and MW Emissivity Relationship
MODIS 3.7 µm
MODIS 8 µm
SSMI 37 GHz V-Pol
SSM/I 37 GHz V-H Pol
36
Predicted IR Emissivity from MW
37
Summary
  • US Joint center for satellite data assimilation
    program has developed a community radiative
    transfer model (CRTM) framework to effectively
    transition to operations fast radiative transfer
    schemes and components,
  • Currently, the version 1 of CRTM 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
    emisivity data base from retrievals. This will
    make the radiative transfer also sensor dependent
  • Version 2 will likely include SSMIS
    Zeeman-affected channels, ensemble IR ocean
    surface emissivity model, SARTA, OSS, RTTOV
    interface, aerosol components.
  • Impacts of CRTM on analysis fields are
    significantly positive. These upgrades improve
    AMSU data utilization rate in polar atmospheres
    (200-300 increase). Impacts of the emissivity
    models on global 6-7 forecasts are also assessed
    and significant
  • IR and MW emissivity displays a strong
    correlation in lower IR emissivity conditions.
    This correlation may allow accurate derivation of
    IR emissivity under cloudy conditions from MW
    emissivity

38
Remaining Issues
  • The surface radiometric models developed for NWP
    applications can capture some basic features
    (e.g. frequency and angular dependent), but many
    scattering models dont generally work for
    special conditions.
  • It is an important first step to develop an
    emissivity data base in radiative transfer scheme
    but we need to carefully design the retrieval
    schemes to ensure the quality (e,g avoiding uses
    of dirty window and surface blind channels and
    cloudy pixel measurements).
  • Perhaps, we are under-utilizing the land data
    assimilation system outputs. It seems that a lot
    of parameters needed in surface radiometric
    models are available today for improving their
    performance.
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