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Abstract:
Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and by the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightness observations can cover large scales but have limited resolution and are only indirectly related to the hydrologic variables of interest. We describe a four-dimensional variational assimilation algorithm which makes best use of available information while accounting for both measurement and model uncertainty. The representer method used here is more efficient than a Kalman filter because it avoids explicit propagation of state error covariances.

In a synthetic example which is based on a field experiment we demonstrate estimation performance by examining data residuals. Such tests provide a convenient way to check the statistical assumptions of the approach and to assess its operational feasibility. Internally computed covariances show that the estimation error decreases with increasing soil moisture. An adjoint analysis reveals that trends in model errors in the soil moisture equation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequately retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predictions derived without assimilation of radiobrightness data.