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