HR: 08:30h
AN: IN41B-02    [Abstracts]
TI: Soil Moisture Data Assimilation with the Noah Land Surface Model
AU: Luo, Y
EM: yluo@iges.org
AF: Center for Research on Environment and Water, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705, United States
AU: * Houser, P R
EM: phouser@iges.org
AF: Center for Research on Environment and Water, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705, United States
AU: Zhan, X
EM: xiwu.zhan@noaa.gov
AF: Center for Satellite Applications and Research, NOAA/NESDIS, World Weather Building, Suite 701, Camp Springs, MD 20746, United States
AB: The Ensemble Kalman Filter (EnKF) is an essential tool in land surface data assimilation, as near-real-time land observations such as MODIS and AMSR-E land satellite products have become available, and the high- performance uncoupled Land Information System (LIS) infrastructure has become available as a test bed at NCEP. Currently the Kalman Filter data assimilation technique has been implemented in LIS and works with the Noah Land Surface Model (LSM). Our previous study has shown that temporal and spatial variability from the standard AMSR-E and modeled soil moisture alone compare poorly with in-situ observations. In this study, to use AMSR-E to provide an improved analysis of land fields that can be directly used as initial conditions for weather and climate prediction, our focus is to reduce the possible biases existing in observations and model forecasts, and investigate the efficiency and benefits of assimilating the AMSR-E data products into the Noah model. To this end using the 1-D EnKF scheme, a pair of comparison experiments for individually assimilating AMSR-E soil moisture retrievals and SCAN in-situ measurements into the Noah model have been carefully designed and will be carried out over a local domain which mostly covers Mississippi and Arkansas, United States of America. Next, for better assimilation performance we will employ several bias-correction algorithms in the data assimilation framework to reduce the observation and model biases. Lastly, the assimilation results will be evaluated with SCAN data, and the performance for each bias correction option will be compared as well. The simulation and evaluation results will be presented in the meeting.
DE: 1847 Modeling
DE: 1855 Remote sensing (1640)
DE: 1866 Soil moisture
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting