HR: 17:15h
AN: H44A-06    [Abstracts]
TI: Impact of gage-based and remotely-sensed precipitation datasets on land data assimilation and quantitative precipitation forecasts
AU: * Gochis, D J
EM: gochis@rap.ucar.edu
AF: National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80307 United States
AU: Yu, W
EM: weiyu@ucar.edu
AF: National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80307 United States
AU: Chen, F
EM: feichen@ucar.edu
AF: National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80307 United States
AB: One of the greatest sources of uncertainty in land surface initial conditions arises from uncertainty in precipitation products used during data assimilation or 'spin-up' cycles. While coupled land-atmosphere forecasts are often of short duration, land surface model spin-up periods may extend for many months prior to coupled model initialization thus allowing land surface hydrological responses to precipitation forcing, such as runoff and vertical and horizontal soil moisture fluxes, to undergo significant evolution. In this paper we explore the impact of using differing precipitation datasets on the spin-up of land surface conditions and their subsequent effect on mesoscale model forecasts. The recently enhanced Noah-distributed modeling framework is applied in a high resolution land data assimilation mode for a period preceding the 2002 IHOP field campaign in the Great Plains region. Comparisons of simulated patterns of soil moisture heterogeneity, runoff and land surface fluxes will be made. Subsequently, coupled Weather Research and Forecast (WRF) model quantitative precipitation forecasts (QPF's) for the IHOP field campaign will be assessed with an emphasis on diagnosing the impacts of the different precipitation datasets on surface initial conditions and forecasted precipitation.
DE: 1218 Mass balance (0762, 1223, 1631, 1836, 1843, 3010, 3322, 4532)
DE: 1814 Energy budgets
DE: 3354 Precipitation (1854)
SC: Hydrology [H]
MN: Fall Meeting 2005