Microwave Emissivity Model Upgrade for New Instruments
Fuzhong WengNOAA/NESDIS/Office of Research and Applications
Banghua Yan QSS Group Inc
The 3rd JCSDA Science Workshop, April 20-21, 2005, Camp Springs, MD
Kozo Okamoto EMC Visiting Scientist, now at JMA
JCSDA Road Map (2002 - 2010)
Improved JCSDA data assimilation science
2002 2004 2007 2008 2009 2005
OK
Deficiency
2003
Advanced JCSDA community-based radiative transfer model,Advanced data thinning techniques
Sci
ence
Ad
van
ce
By 2010, a numerical weather prediction community will be empowered to effectively assimilate increasing amounts of
advanced satellite observations
2010
AMSU, HIRS, SSM/I, Quikscat,
AVHRR, TMI, GOES assimilated
AIRS, ATMS, CrIS, VIIRS, IASI, SSM/IS, AMSR, more products assimilated
Pre-JCSDA data assimilation science
Radiative transfer model, OPTRAN, ocean microwave emissivity, microwave land emissivity model, and GFS data assimilation system were developed
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
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
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
NPOESS sensors ( CMIS, ATMS…) GOES-R
The CRTM includes scattering & polarization from cloud, precip and surface
The radiances can be assimilated under all conditions with the state-of-the science NWP models
Resources:
Atmospheric Spectroscopy Model
Aerosol and Cloud Optical Model
Surface Emissivity, Reflectivity Models
Forward Radiative Transfer Schemes
Receiver and Antenna Transfer Functions
Jacobian (Adjoint) Model
Atmospheric State Vectors Surface State Vectors
JCSDA Community Radiative Transfer Model
Natural Scenes
Two-Scale Approx.
Scattering/ observations
Geometric Optics
Scattering/ observations
Theory Base
Surface Emissivity Model
Surface Emissivity
• 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
Surface Emissivity Spectra (=530)
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1.0
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Frequency (GHz)
Em
issiv
ity a
t V
-Po
lari
zati
on
Snow
Canopy
Bare Soil
Wet Land
Desert
Ocean
Surface Emissivity Spectra (=530)
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Frequency (GHz)
Em
issiv
ity a
t H
-Po
lari
zati
on
Snow
Canopy
Bare Soil
Wet Land
Desert
Ocean
Weng et al (2001, JGR)
Deficiencies of Snow Modeling
• Not applicable for aged snow
• Limited at frequencies less than 50 GHz
• Not applicable for vertically stratified snow
• Two stream radiative transfer approach
Brightness Temperature Sensitivity to Surface Emissivity
Table 1.
)( dsB TTT = 0.04
Advanced Microwave Sounding UnitSounding Channels
53.7 GHz52.8 GHz
183+-1 GHz 183+-3 GHz
Emissivity Retrieval:
)( ds
dub
TT
TTT
AMSU-A: 23.8, 31.4, 50.3, 89 GHzAMSU-A: 23.8, 31.4, 50.3, 89 GHzAMSU-B: 89, 150 GHz AMSU-B: 89, 150 GHz AVHRR: TsAVHRR: TsRAOBS temperature/q profiles RAOBS temperature/q profiles
Snow Emissivity Data Base
Winter season of 2003: Eastern part of Winter season of 2003: Eastern part of US: persistent snow cover during US: persistent snow cover during FebruaryFebruary
Snow Storms (February 2003)
AMSU Measurements at Hagerstown, MD (39.7N, 77.7W)
210
220
230
240
250
260
270
280
10 12 14 16 18 20 22 24 26 28
Date in February 2003
Tb
(K
)
23.8 GHz 31.4 GHz 50.3 GHz
89.0 GHz 150.0 GHz Ts
Dry snow >12 in Heavy rain Refrozen New snow
Snow Emissivity SpectraSnow Emissivity Spectra31.4 50.3 8923.8 150
Sea Ice Emissivity Spectra
Sea ice types: see Hewison and English, IEEE, 1999
Impacts of Surface Emissivity Operational
Impacts of Surface EmissivityExperimental
Impacts of Surface EmissivityExperimental - Operational
Impacts of AMSU on Forecasts in Polar Region
Anomaly Correlation for 700 hPa Geopotential Height
Snow Microwave Emissivity SpectraSnow Microwave Emissivity Spectra
Snow H-POL Emissivity Spectra
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Snow
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issi
vity
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Grass_after_Snow Wet Snow Powder SnowShallow Snow Medium Snow Deep SnowThin Crust Snow Thick Crust Snow Bottom Crust Snow (A)Bottom Crust Snow (B) Crust Snow RS_Snow (A)RS_Snow (B) RS_Snow (C) RS_Snow (D)RS_Snow (E)
Snow V-POL Emissivity Spectra
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Snow
Em
issi
vity
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Grass_after_Snow Wet Snow Powder SnowShallow Snow Medium Snow Deep SnowThin Crust Snow Thick Crust Snow Bottom Crust Snow (A)Bottom Crust Snow (B) Crust Snow RS_Snow (A)RS_Snow (B) RS_Snow (C) RS_Snow (D)RS_Snow (E)
Sea Ice Microwave Emissivity SpectraSea Ice Microwave Emissivity Spectra
Sea Ice H-POL Emissivity Spectra
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Em
issi
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RS_ Ice (A) RS_Ice (B) New Ice + SnowBare New Ice Broken Ice First Year IceComposite Pack Ice RS_Ice (C) Fast IceRS_Ice (D) RS_Ice (E) RS_Ice (F)Grease Ice
Sea Ice V-POL Emissivity Spectra
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Em
issi
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RS_ Ice (A) RS_Ice (B) New Ice + SnowBare New Ice Broken Ice First Year IceComposite Pack Ice RS_Ice (C) Fast IceRS_Ice (D) RS_Ice (E) RS_Ice (F)Grease Ice
Performance of Ocean Emissivity Model in GDAS
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NESDIS (10m/s) MetOff. (10m/s)
NESDIS (30m/s) MetOff. (30m/s)
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NESDIS (10m/s) MetOff. (10m/s)
NESDIS (30m/s) MetOff. (30m/s)
(a)
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NESDIS (10m/s) MetOff. (10m/s)
NESDIS (30m/s) MetOff. (30m/s)
(c)
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NESDIS (10m/s) MetOff. (10m/s)
NESDIS (30m/s) MetOff. (30m/s)
(d)
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an V
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an H
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cean
H-P
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issi
vity
V-POL (SST = 300 K) H-POL (SST = 300 K)
V-POL (SST =280 K) H-POL (SST = 280 K)
Comparison between Met Office and NESDIS Ocean Emissivity Models
► NESDIS Ocean Emissivity Model (Hollinger, 1971; Stogryn, 1972;
Klein and Swift, 1977 )
►Met Office Ocean Emissivity Model (A semi-empirical model, English and Hewison)
Histogram of Simulated TB Bias at AMSR-E Channels Using Met Office Ocean Emissivity Model
Histogram of Simulated TB Bias at AMSR-E Channels Using NESDIS Ocean Emissivity Model
Summary of Accomplishments
• Microwave emissivity models have been updated for new sensors (SSMIS, MHS) over snow and sea ice conditions
• Microwave snow and sea ice emissivity models are integrated as part of CRTM
• 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
Outstanding Issues
• Ocean microwave emissivity model used in NCEP global data assimilation system is found to cause larger biases in simulated brightness temperatures at lower frequencies, compared to the model developed by NESDIS, especially at high wind speeds
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