Initial Test Results using a Temporal Variational Data Assimilation Method to Retrieve Deep Soil Moisture

A key environmental variable is deep soil moisture. Soil moisture at depth significantly impacts off-road mobility, hydrological stream flow estimation, background simulation, site selection, and other applications. The Microwave Imager Sounder (MIS) threshold Soil Moisture Environmental Data Record (EDR) requirement is for near-surface soil moisture estimates. This work enhances the Soil Moisture EDR toward objective performance at deep soil levels. Our goal is to identify a pathway to the soil moisture performance objective (soil moisture at depths between 0-80 cm) for US Army and civilian use, and to identify and mitigate algorithm impediments to its potential performance. Interactions and community involvement with a variety of agencies that will use the NPOESS surface and deep soil moisture products are also underway.

A variational data assimilation methodology is used to derive temporal deep soil moisture profile sensitivities for use with future MIS data. We have successfully completed our individual system component tests. This work will present the current results of our system tests. The components are being integrated into the Air Force Weather Agency (AFWA) – Land Information System (LIS) framework. The Fast All-Season Soil Strength (FASST) land surface model and related adjoint sensitivities are used in a 4D variational (4DVAR) solver. We have adopted the Fletcher non-Gaussian 4DVAR framework, as soil moisture variables have skewed data distributions, and are therefore non-Gaussian. The 4DVAR component tests are based on lognormal probability distributions. Current test results will be shown.

This research was supported by the DoD Center for Geosciences/Atmospheric Research at Colorado State University under Cooperative Agreement W911NF-06-2-0015 with the Army Research Laboratory and by the NPOESS Microwave Imager Sounder Algorithm Development Project.