Implementation of a Temporal Variational Data Assimilation Method to Retrieve Deep Soil Moisture

Our goal is to identify paths 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. This work will also enable the Army to more accurately determine the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Soil Moisture Environmental Data Record (EDR) impacts upon DoD-related trafficability, off-road mobility, counter-mine operations, and hydrological streamflow estimation. 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 NPOESS Microwave Imager/Sounder (MIS) data. We have successfully completed our individual system component tests. Our current focus is full system integration within targeted DoD operational frameworks. The components are being integrated into the Air Force Weather Agency (AFWA) – Land Information System (LIS) framework. Implementation details of the various system components will be discussed.

The land surface model and its respective 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 solver component tests are based on lognormal probability distributions.

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 MIS Algorithm Development Project.