Land analysis enhancements at the Air Force Weather Agency using the NASA Land Information System (LIS)

Through a successful partnership between the Air Force Weather Agency (AFWA) and National Aeronautical and Space Administration (NASA), AFWA has integrated the NASA Land Information System (LIS) into operations, to improve the AFWA AGRMET model. This collaboration has not only enabled a comprehensive land surface modeling and data assimilation system at AFWA, but also has provided a framework for transitioning the state-of-the-art terrestrial hydrological modeling research to AFWA operations. The AFWA AGRMET model employed real time precipitation observations and analyses, global forecast model and satellite data to generate global estimates of soil moisture, soil temperature and other land surface states at 48km spatial resolution. The integration of the AGRMET capabilities into LIS and the Initial Operational Implementation (IOC) of LIS in February, 2009, marked a significant milestone in the advancement of AFWA capabilities over the existing AFWA Agriculture Meteorology (AGRMET) land surface system. The IOC implementation provided a fourfold spatial resolution increase over the AGRMET model. In more recent efforts, the precipitation analyses were improved by incorporating the NOAA Climate Prediction (CPC) Morphed Precipitation analysis (CMORPH) as an added enhancement to the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder (SSMI/S)-only precipitation analysis. Another key focus of the LIS transition has been the incorporation of advanced data assimilation that enables the use of soil moisture retrievals from the Advanced Scatterometer (ASCAT) and AFWA Snow Depth analysis (SNODEP) through algorithms such as the Ensemble Kalman Filter (EnKF). LIS has also been coupled with the Weather Research and Forecasting (WRF) model enabling a hydrometeorological system that can be used for the high-resolution initialization of the internal WRF operational model. More recently, additional surface models (e.g. FLake lake model) and capabilities of uncertainty estimation have also been implemented in the AFWA configuration of LIS. This presentation provides a summary of these recent enhancements enabled through the collaborative work between AFWA and NASA.