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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Canopy

Bare Soil

Wet Land

Desert

Ocean

Surface Emissivity Spectra (=530)

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Bare Soil

Wet Land

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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)

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280

10 12 14 16 18 20 22 24 26 28

Date in February 2003

Tb

(K

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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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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

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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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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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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)

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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)

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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