HR: 12:05h
AN: H12A-08    [Abstracts]
TI: The potential of assimilating remotely sensed soil moisture into land surface models
AU: * Gao, H
EM: huiling@princeton.edu
AF: Dept. of Civil and Envi. Engi., Princeton Univ., Princeton, NJ 08540 United States
AU: Pan, M
EM: mpan@princeton.edu
AF: Dept. of Civil and Envi. Engi., Princeton Univ., Princeton, NJ 08540 United States
AU: Wood, E F
EM: efwood@runoff.princeton.edu
AF: Dept. of Civil and Envi. Engi., Princeton Univ., Princeton, NJ 08540 United States
AU: Michele, C D
EM: carlo.demichele@polimi.it
AF: Dept. of Hydraulic, Envi. and surveying Engi. Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano, MI I-20133 Italy
AB: Assimilation of remote sensing data into hydrological modeling has the potential to improve forecasting accuracy; with space-borne, low frequency microwave observations being especially interesting because of its sensitivity to surface soil moisture and its change. However, in conveying the soil moisture information to land surface models, both the brightness temperature and the retrieved soil moisture product suffer from errors introduced by the sensor, atmospheric conditions, and retrieval parameterization. More importantly, under a variety of conditions (e.g. very dry surfaces, heavily vegetated surfaces, or rain conditions during sensor observations) the remote sensing data is not informative. Understanding the statistical dynamics of the errors in both the brightness temperatures and retrieved soil moisture, and the statistical relationships between the surface wetness that influences the microwave signal and the surface land surface modeling layer is fundamental in developing data assimilation procedures that can incorporate space-based radiometric measurements. The work uses statistical methods based on Copula probability distributions to relate space-based soil moisture estimates to either in-situ measurements or land surface model states. The Copula based joint distributions are used to generate ensembles, which are then assimilated into the upper 10-cm soil layer of the Variable Infiltration Capacity land surface model using an Ensemble Kalman Filter (EnKF). The remotely sensed soil moisture product is from a five-year, daily retrieval using measurements from the TRMM 10.7 GHz Microwave Imager (TMI).
DE: 1818 Evapotranspiration
DE: 1833 Hydroclimatology
DE: 1836 Hydrologic budget (1655)
DE: 1854 Precipitation (3354)
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting