HR: 1340h
AN: H23C-1143    [Abstracts]
TI: Ensemble Land Surface Modeling Using Coarse Satellite-Based Precipitation Forcing
AU: * Margulis, S A
EM: margulis@seas.ucla.edu
AF: UCLA, 5732D Boelter Hall Dept. of Civil and Environmental Engineering, Los Angeles, CA 90095
AB: Precipitation is the key forcing variable for land surface hydrologic processes and is largely responsible for variability in soil moisture and surface flux fields. The measurement of precipitation over large scales is difficult due to its inherent spatial and temporal intermittency and the lack of sufficiently dense ground-based monitoring networks in many regions of the globe. Methods for remotely sensing precipitation are now generally available, but provide estimates that are spatially coarse (e.g. tens to hundreds of kilometers), aggregated in time (daily to monthly), and have complex error structures (positive non-detection probability, non-zero false alarm rate, large uncertainty, etc.). Due to the nonlinearity of surface hydrologic processes, these products cannot be directly used in modeling studies. Furthermore, for accurate hydrologic state and flux predictions it is crucial that uncertainty in these estimates be properly incorporated into modeling frameworks. In this paper we present an ensemble forecasting framework that contains a spatio-temporal disaggregation scheme for remotely sensed Global Precipitation Climatology Project-1 degree daily (GPCP-1DD) precipitation product. A detailed study of the error characteristics of the GPCP 1DD precipitation forcing is undertaken and then incorporated in the framework. This approach significantly enhances the utility of the remote sensing product for hydrologic applications. The spatio-temporal disaggregation scheme takes advantage of the ensemble nature of the system by introducing precipitation realizations that are conditioned on the remote sensing data. The ability to use coarse precipitation observations is tested in experiments using data from the SGP97 field experiment. This approach not only captures the large scale spatial variability in precipitation contained in the remote sensing observations, but introduces a more realistic error structure in the precipitation forcing that accounts for errors in storm magnitudes, arrivals, and spatial structure. Results from tests using the remotely sensed precipitation show improvement in both soil moisture and land surface flux estimates over those using sparse ground-based precipitation. Furthermore the general ensemble framework is easily adapted to assimilate other observations (e.g. microwave radiobrightness) using the Ensemble Kalman Filter (EnKF). The combined ensemble data assimilation framework allows for the incorporation of information contained in coarse remote sensing observations of fluxes (precipitation) and states (soil moisture) to better capture the response of the land surface.
DE: 1854 Precipitation (3354)
DE: 1866 Soil moisture
DE: 1878 Water/energy interactions
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting