Prospects for improving land surface model performance via the assimilation of remote sensing products.

An ongoing challenge for hydrologists and remote sensing scientist is the design of experiments to demonstrate the value - in any - of remote sensing observations for efforts to monitor and/or predict surface hydrologic processes at large scales. The need is especially pressing for remote observations of surface geophysical state variables like soil moisture and skin temperature. The most efficient utilization of remote surface state observations is within the context of a data assimilation system designed to merge surface state predictions from numerical models with remote observations of the land surface. Such systems contain at least three components: a numerical land surface model, an emission model to convert land surface model predictions into observable quantities (e.g. brightness temperature), and an assimilation algorithm. The `value' of remote sensing observations therefore depends on a myriad of factors including the quality of non-updated open-loop model predictions, the optimality of the data assimilation approach, and the accuracy of the observational model. One basic benchmark for data assimilation approaches should be the accuracy of model predictions (e.g. evapotranspiration) obtainable from non-updated open-loop model simulations. This talk will address some of the basic issues surrounding such evaluations and examine ways in which remote sensing observations can add skill or value to land surface model predictions. Two key weaknesses of land surface models are their reliance on uncertain measurements of meteorologic forcings (e.g. rainfall) and parameter selection ambiguities presented by their complex representation of surface processes. Both shortcomings represent potential openings for land data assimilation approaches. The first part of the talk will examine the potential of remote L-band microwave brightness temperatures data and an Ensemble Kalman filter to compensate land surface model predictions for errors arising from poor or nonexistent rainfall measurements. Strategies for merging remotely sensed surface soil moisture retrievals with sparsely sampled rainfall rates from a spaceborne radar precipitation mission will also be discussed. The second portion of the talk will evaluate the accuracy of proposed variational assimilation approaches designed to combine remote skin temperature retrievals with a simplified prognostic equation for surface soil temperature. Such approaches offer an attractive alternative to more complex representations of the surface energy balance since the increased accuracy of the more complex approaches must be discounted by the practical difficulties presented by their calibration over heterogeneous landscapes.