HR: 1340h
AN: H13G-1397    [Abstracts]
TI: Satellite-Derived Soil Moisture for Enhanced Data Assimilation and Numerical Weather Prediction
AU: * Gouweleeuw, B
EM: bingo@hsb.gsfc.nasa.gov
AF: NASA/GSFC, Mail Code 614.3, Greenbelt, MD 20771 United States
AU: Owe, M
EM: manfred.owe@nasa.gov
AF: NASA/GSFC, Mail Code 614.3, Greenbelt, MD 20771 United States
AB: Accurate initialization of land surface conditions in fully-coupled Numerical Weather Prediction (NWP) models is essential for accurate short term to long range meteorological and hydrological prediction. Because land surface parameters, such as soil moisture, temperature, and vegetation water content are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores of these models. This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. Improved accuracy in defining initial conditions for NWPs along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), may be assumed to lead to improved short-term to long-range weather forecasting capability. The analysis presented here combines global data sets of satellite-remote sensing-derived land surface parameters with observational and modeled values in a data assimilation scheme in order to (a) provide continuous model bias corrections for uncoupled LSM baseline data generation; and (b) provide improved initial conditions for numerical weather prediction models.
DE: 1816 Estimation and forecasting
DE: 1840 Hydrometeorology
DE: 1855 Remote sensing (1640)
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: Fall Meeting 2005