879 Upgrading the Operational USAF Land Information System Configuration: Improvements to Soil Moisture DA

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Mahdi Navari, NASA GSFC/Earth System Science Interdisciplinary Center/Univ. of Maryland, Greenbelt, MD; and S. V. Kumar, S. Wang, E. M. Kemp, K. R. Arsenault, Y. Yoon, J. Wegiel, D. M. Mocko, J. Geiger, and C. D. Peters-Lidard

The NASA land information system (LIS) is an open source software framework providing state-of-the-art land surface models (LSMs) and data assimilation (DA) capabilities and has been adopted by the US Air Force (USAF) as the operational land characterization environment. This presentation describes the new developments in the operational assimilation of remote sensing soil moisture estimates in the USAF LIS configuration. The soil moisture assimilation in the USAF LIS is conducted at a global scale, employing near real-time soil moisture estimates from the NOAA NESDIS Soil Moisture Operational Product System (SMOPS). The assimilation is carried out using a one-dimensional Ensemble Kalman Filter (EnKF) algorithm within LIS.

The current operational USAF LIS environment employs the Noah land surface model (LSM) version 3.6.1. The configuration is being updated to include two new land surface models, Noah- multiparameterization (Noah-MP) LSM and the Joint UK Land Environment Simulator (JULES) LSM. Noah-MP provides improved representations of the subsurface soil moisture through formulations of conceptual groundwater modules instead of the free drainage concept in Noah3.6. In this presentation, the impact of the improved subsurface formulations on soil moisture assimilation will be examined. The presentation will also describe the impact of assimilating both passive and active soil moisture remote sensing retrievals from sensors such as ASCAT and SMAP. The impact of assimilation on soil moisture and other land surface variables will evaluated by comparing to a large suite of available reference datasets.

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