HR: 11:35h
AN: H41H-06    [PDF]
TI: Spatio-Temporal Disaggregation of Remote Sensing Observations for Land Surface State and Flux Estimation Using an Ensemble Data Assimilation Framework
AU: * Margulis, S A
EM: margulis@seas.ucla.edu
AF: Dept. of Civil and Environmental Engineering University of California, Los Angeles, 5732D Boelter Hall, Los Angeles, CA 90095 United States
AU: Entekhabi, D
EM: darae@mit.edu
AF: Dept. of Civil and Environmental Engineering Massachusetts Institute of Technology, 77 Mass. Ave., Cambridge, MA 02139 United States
AU: McLaughlin, D
EM: dennism@mit.edu
AF: Dept. of Civil and Environmental Engineering Massachusetts Institute of Technology, 77 Mass. Ave., Cambridge, MA 02139 United States
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) and aggregated in time (daily to monthly). 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 must be properly incorporated into modeling frameworks. In this paper we present a unified ensemble data assimilation framework that contains a spatio-temporal disaggregation scheme for remotely sensed precipitation and incorporates remotely sensed microwave brightness measurements of the land surface in order to estimate distributed land surface states and fluxes. The ability to downscale coarse precipitation and radiobrightness observations is tested in experiments using data from the SGP97 field experiment. The spatio-temporal disaggregation scheme takes advantage of the ensemble nature of the Ensemble Kalman Filter (EnKF) by introducing precipitation realizations that are conditioned on the remote sensing data. 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.
DE: 1640 Remote sensing
DE: 1818 Evapotranspiration
DE: 1854 Precipitation (3354)
DE: 1866 Soil moisture
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2003 Fall Meeting