Initialization of Soil Moisture in a Global Climate Model: A North American Case Study
Jeffrey P. Walker and Paul R. Houser
Accurate initialization and forecasting of land surface soil moisture in fully-coupled climate system models is critical for seasonal-to-interannual climatological and hydrological prediction. This results from the soil moisture feedback to precipitation and atmospheric circulations, through its control on partitioning of the available energy into latent and sensible heat exchange. To overcome such limitations in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a one-dimensional Kalman filter assimilation of near-surface soil moisture observations has been added to the Catchment-based Land Surface Model (CLSM) of Koster et al. (2000) and Ducharne et al. (2000) used by NSIPP. In this paper, the CLSM is run off-line from the atmospheric and ocean simulation models, forced by bias corrected European Centre for Medium-Range Weather Forecasts (ECMWF) re-analysis data (Berg et al., 2001). The atmospheric forcing data set, which extends from 1979 to 1993, includes four observations per day of the following fields; two meter air and dew point temperatures, ten meter wind speed, convective and total precipitation, long and short-wave downward radiation, and surface pressure. The CLSM is run off-line from the ocean and atmospheric simulation models so as to produce the best possible land surface initialization states from an optimal merging of the best possible land surface forecast using the best possible atmospheric forcing with remotely sensed observations of the land surface. To correct for soil moisture forecast errors resulting from incorrect initial conditions, inaccurate meteorological forcing data and an imperfect forecast model, remotely sensed measurements of near surface soil moisture are assimilated into the CLSM using a one-dimensional extended Kalman filter. The soil moisture estimates from the assimilation are compared with the limited number of ground-based point measurements of soil moisture. The near-surface soil moisture observations used in the assimilation are from the Scanning Multi-channel Microwave Radiometer (SMMR) satellite 6.6GHz (C-band) channel covering the period 1979 to 1987 (Owe et al., 2001). The soil moisture observations are derived simultaneously with the vegetation optical depth, by utilizing information in the Microwave Polarization Difference Index (MPDI) and a soil temperature estimate from the 37GHz channel. The ground truth data for this time period is limited to 19 stations in Illinois, 6 stations in Iowa and a transect of 89 points in New Mexico (Robock et al., 2000).
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