Microwave Emissivity Model Upgrade for New Instruments

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Microwave Emissivity Model Upgrade for New
Instruments
Fuzhong Weng NOAA/NESDIS/Office of Research and
Applications
Banghua Yan QSS Group Inc
Kozo Okamoto EMC Visiting Scientist, now at JMA
The 3rd JCSDA Science Workshop, April 20-21,
2005, Camp Springs, MD
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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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JCSDA Community Radiative Transfer Model
Atmospheric State Vectors
Surface State Vectors
Atmospheric Spectroscopy Model
Surface Emissivity, Reflectivity Models
Aerosol and Cloud Optical Model
Forward Radiative Transfer Schemes
Receiver and Antenna Transfer Functions
Jacobian (Adjoint) Model
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Surface Emissivity Model
Natural Scenes
Theory Base
Two-Scale Approx.
Geometric Optics
Scattering/ observations
Scattering/ observations
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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

Weng et al (2001, JGR)
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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

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Brightness Temperature Sensitivity to Surface
Emissivity
Table 1.  
?? 0.04
 
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Advanced Microwave Sounding UnitSounding Channels
52.8 GHz
53.7 GHz
183-3 GHz
183-1 GHz
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Snow Emissivity Data Base

• Emissivity Retrieval

AMSU-A 23.8, 31.4, 50.3, 89 GHz AMSU-B 89, 150
GHz AVHRR Ts RAOBS temperature/q profiles
Winter season of 2003 Eastern part of US
persistent snow cover during February
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Snow Storms (February 2003)
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Snow Emissivity Spectra
31.4
50.3
89
23.8
150
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Sea Ice Emissivity Spectra
Sea ice types see Hewison and English, IEEE,
1999
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Impacts of Surface Emissivity
Operational
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Impacts of Surface Emissivity
Experimental
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Impacts of Surface Emissivity
Experimental - Operational
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Impacts of AMSU on Forecasts in Polar Region
Anomaly Correlation for 700 hPa Geopotential
Height
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Snow Microwave Emissivity Spectra
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Sea Ice Microwave Emissivity Spectra
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Performance of Ocean Emissivity Model in GDAS
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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)
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Histogram of Simulated TB Bias at AMSR-E Channels
Using Met Office Ocean Emissivity Model
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Histogram of Simulated TB Bias at AMSR-E Channels
Using NESDIS Ocean Emissivity Model
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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

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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