Evaluation of soil state representation in the NCAR ensemble analysis system

Abstract

Accurate characterization of soil state is imperative for weather and climate research, hydrological modeling, agriculture, water resources management, and drought monitoring. Limited-area data assimilation (DA) systems can provide high-resolution real-time soil state analyses. However, soil state representations from regional continuously cycling DA systems have not been examined. This study evaluates soil moisture and temperature estimates and spread characteristics from a real-time, limited-area, continuously-cycling ensemble Kalman filter (EnKF) data assimilation (DA) system run at the National Center for Atmospheric Research (NCAR) for 2.7 years over the conterminous United States (CONUS). Soil moisture and temperature data from both the EnKF and operational Global Forecast System (GFS) analysis are compared against the North American Land Data Assimilation System (NLDAS-2) soil state estimates. Results demonstrate the continuously-cycling EnKF was generally able to represent the spatial and temporal characteristics of soil moisture and temperature across the CONUS, demonstrating the feasibility of using limited-area continuously cycling EnKFs as a method to improve simulations of soil moisture and temperature. Soil state estimates from the NCAR ensemble exhibited lower Bias and RMSE compared to GFS analyses over west and southeast CONUS. Additionally, the ensemble spread characteristics suggest that future limited-area continuously cycling EnKF systems could successfully assimilate both in situ and remotely-sensed soil moisture and temperature observations to further improve soil state analyses.