A Synthetic Study on the Influence of Error in Surface Soil Moisture Observations on Assimilation
Jeffrey P. Walker and Paul R. Houser
Accurate initialization and forecasting of surface soil moisture in fully-coupled climate system models is critical for seasonal-to-interannual climatological and hydrological prediction, because of its feedback to precipitation and atmospheric circulation. To provide accurate initial soil moisture, the NASA Seasonal-to-Interannual Prediction Project (NSIPP) has added the capability to assimilate near-surface soil moisture data to its land surface model.
Vegetation cover impedes remotely sensed observations of the near-surface soil moisture. To examine the amount of error there may be in these observations, while still being useful for assimilation, a set of numerical experiments has been undertaken using the NSIPP land surface model off-line from the GCM. In this study, "true" soil moisture data were generated for North America by spinning-up the land surface model and then running for 1987 using the ISLSCP forcing data sets. By adding perturbations to the initial soil moisture and the forcing data, a degraded simulation was made to imitate the likely error in soil moisture forecasts as a result of erroneous initial conditions and forcing. The final simulations using the perturbed initial condition and forcing data assimilated degraded near-surface soil moisture "observations" from the "true" simulation. The "observations" were degraded by various amounts to imitate erroneous observations.