Abstract:
This paper describes a new four-dimensional data assimilation (4DDA) algorithm which is designed to estimate soil moisture profiles and associated water and energy fluxes from passive microwave measurements. The model is based on a simplified set of state equations which describe the near-surface water and energy transport processes of most interest for data assimilation purposes. The computational region is divided into one-dimensional vertical cells (or pixels). Moisture transport in each pixel is described with Richards' equation while energy transport is described with a force-restore model. The surface forcings and parameters in different pixels are assumed to be random fields which are correlated over time and/or space. The measurement operator, which relates brightness temperature observations to the system states, is based on a simplified radiative transfer model. The estimated states are derived from a variational least-squares algorithm. This algorithm is tested on a synthetic data set which is designed to mimic conditions during the 1997 Southern Great Plains (SGP97) experiment in central Oklahoma. The paper presents preliminary results as well as an assessment of the computational and operational feasibility of the approach.