Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications

Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightnes s observations can cover large scales but have limited resolution and are o nly indirectly related to the hydrologic variables of interest. We describe a four-dimensional (4-D) variational assimilation algorithm that makes bes t use of available information while accounting for both measurement and mo del uncertainty. The representer method used here is more efficient than a Kalman filter because it avoids explicit propagation of state error covaria nces. In a synthetic example, which is based on a field experiment, we demo nstrate estimation performance by examining data residuals. Such tests prov ide a convenient way to check the statistical assumptions of the approach a nd to assess its operational feasibility. Internally computed covariances s how that the estimation error decreases with increasing soil moisture. An a djoint analysis reveals that trends in model errors in the soil moisture eq uation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequat ely retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predic tions derived without assimilation of radiobrightness data.