Design of a temporal variational data assimilation method suitable for deep soil moisture retrievals using passive Microwave radiometer data

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 stream flow 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 temporal variational data assimilation methodology is used to derive deep soil moisture profile sensitivities and tendencies 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 architectures. The components are being integrated into the development version of the Air Force Weather Agency (AFWA) - Land Information System (LIS). Design 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.