A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations

Because of its long-term persistence, accurate initialization of land surface soil moisture in fully coupled global climate models has the potential to greatly increase the accuracy of climatological and hydrological prediction. To improve the initialization of soil moisture in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a one-dimensional Kalman filter has been developed to assimilate near-surface soil moisture observations into the catchment-based land surface model used by NSIPP. A set of numerical experiments was performed using an uncoupled version of the NSIPP land surface model to evaluate the assimilation procedure. In this study, true land surface data were generated by spinning-up the land surface model for 1987 using the International Satellite Land Surface Climatology Project (ISLSCP) forcing data sets. A degraded simulation was made for 1987 by setting the initial soil moisture prognostic variables to arbitrarily wet values uniformly throughout North America. The final simulation run assimilated the synthetically generated near-surface soil moisture observations from the true simulation into the degraded simulation once every 3 days. This study has illustrated that by assimilating near-surface soil moisture observations, as would be available from a remote sensing satellite, errors in forecast soil moisture profiles as a result of poor initialization may be removed and the resulting predictions of runoff and evapotranspiration improved. After only 1 month of assimilation the root-mean-square error in the profile storage of soil moisture was reduced to 3% vol/vol, while after 12 months of assimilation, the root-mean-square error in the profile storage was as low as 1% vol/vol.