HR: 15:15h
AN: H23D-06 [Abstracts]
TI: Impact study of soil moisture on simulation of AMSR-E brightness temperature in NCEP data assimilation system
AU: * Zheng, W
EM: Weizhong.Zheng@noaa.gov
AF: SAIC, 4600 Powder Mill Road, Beltsville, MD 20705, United States
AU: * Zheng, W
EM: Weizhong.Zheng@noaa.gov
AF: NOAA/NCEP/EMC, 5200 Auth Road, Camp Spings, MD 20746, United States
AU: Ek, M
EM: Michael.Ek@noaa.gov
AF: NOAA/NCEP/EMC, 5200 Auth Road, Camp Spings, MD 20746, United States
AU: Meng, J
EM: Jesse.Meng@noaa.gov
AF: SAIC, 4600 Powder Mill Road, Beltsville, MD 20705, United States
AU: Meng, J
EM: Jesse.Meng@noaa.gov
AF: NOAA/NCEP/EMC, 5200 Auth Road, Camp Spings, MD 20746, United States
AU: Zhan, X
EM: Xiwu.Zhan@noaa.gov
AF: NOAA/NESDIS/STAR, 5200 Auth Road, Camp Springs, MD 20746, United States
AU: Liu, J
EM: Jicheng.Liu@noaa.gov
AF: NOAA/NESDIS/STAR, 5200 Auth Road, Camp Springs, MD 20746, United States
AU: Derber, J
EM: John.Derber@noaa.gov
AF: NOAA/NCEP/EMC, 5200 Auth Road, Camp Spings, MD 20746, United States
AB:
Soil moisture is one of most important factors not only in numerical weather and climate prediction by
influencing water and energy exchange between land and atmosphere, but also in satellite data assimilation
by determining brightness temperature simulation for satellite surface sensitive channels. This paper
investigates impact of soil moisture on brightness temperature simulation for Advanced Microwave Scanning
Radiometer (AMSR-E) low-frequency channels in the National Centers for Environmental Prediction
(NCEP)'s Gridpoint Statistical Interpolation (GSI) analysis system. It is found that
inaccurate soil moisture in the NCEP models results in large errors in brightness temperatures simulated
through the JCSDA's Community Radiative Transfer Model (CRTM). For channels at C-band (6.925 GHz) and
X-band (10.65 GHz), AMSR-E observed brightness temperatures contaminated by Radio-Frequency
Interference (RFI) are corrected with the algorithm developed by Wu et al. (2009). Application of the soil
moisture retrieved from AMSR-E in GSI and its potential to improve data assimilation will be discussed.
DE: 1812 Drought
DE: 1821 Floods
DE: 1840 Hydrometeorology
DE: 1855 Remote sensing (1640)
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2009 Joint Assembly