Abstract:
Soil moisture is a key variable for weather and climate prediction, flood forecasting, and the determination of groundwater recharge. But uncertainties related to the heterogeneity of the land surface and the non-linearity of land-atmosphere interactions severely limit our ability to accurately model and predict soil moisture on regional or continental scales. Remote-sensing techniques, on the other hand, can only indirectly measure surface soil moisture, and the data are of limited resolution in space and time. We present a weak constraint variational data assimilition algorithm which takes into account model as well as measurement uncertainties and optimally combines the information from both the model and the data by minimizing a least-squares performance index. We achieve a dynamically consistent interpolation and extrapolation of the remote sensing data in space and in time, or, equivalently, a continuous update of the model predictions from the data. The algorithm is tested with a synthetic experiment which is designed to mimick the conditions during the 1997 Southern Great Plains (SGP97) experiment in central Oklahoma, USA. A synthetic experiment is best suited to evaluate the performance of the algorithm as the uncertain inputs are known by design. Our data assimilation algorithm is capable of capturing quite well the spatial patterns that arise from the heterogeneity in soil types and the meteorological forcing.