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Abstract:
This paper investigates the feasibility of estimating large-scale soil moisture profiles and related land surface variables from 1.4 GHz (L-band) passive microwave measurements, using variational data assimilation. Our four-dimensional assimilation algorithm takes into account both model and measurement uncertainties and provides dynamically consistent interpolation and extrapolation of remote sensing data over space and time. The land surface hydrologic model which forms the heart of the variational algorithm was expressly designed for data assimilation purposes. This model captures key physical processes while remaining computationally efficient.

We test our algorithm with a series of synthetic experiments based on the Southern Great Plains 1997 Hydrology Experiment. These experiments provide insights about three issues which are crucial to the design of an operational soil moisture assimilation system. Our first synthetic experiment shows that soil moisture can be satisfactorily estimated at scales finer than the resolution of the brightness images. This downscaling experiment indicates that brightness images with a resolution of tens of kilometers can yield soil moisture profile estimates on a scale of a few kilometers, provided that micro-meteorological, soil texture, and land cover inputs are available at the finer scale. In our second synthetic experiment we show that adequate soil moisture estimates can be obtained even if quantitative precipitation data are not available. Model error terms estimated from radiobrightness measurements are able to account in an aggregate way for the effects of precipitation events. In our third experiment we show that reductions in estimation performance resulting from a decrease in the length of the assimilation time interval are offset by a substantial improvement in computational efficiency.