Soil Moisture Background Error Covariance Estimation in a Land-Atmosphere Coupled Model

Monday, 15 December 2014
Liao-Fan Lin1, Mohammad Ebtehaj1, Alejandro N Flores2, Jingfeng Wang1 and Rafael L Bras1, (1)Georgia Institute of Technology Main Campus, Atlanta, GA, United States, (2)Boise State University, Boise, ID, United States
The objective of this study is to estimate space-time dynamics of the soil moisture background error in a coupled land-atmosphere model for better understanding the land-atmosphere interactions and soil moisture dynamics through data assimilation. To this end, we conducted forecast experiments in eight calendar years from 2006 to 2013 using the Weather Research and Forecasting (WRF) model coupled with the Noah land surface model and estimated the background error statistics based on the National Meteorological Center (NMC) methodology. All the WRF-Noah simulations were initialized with the National Centers for Environmental Prediction (NCEP) FNL operational global analysis dataset. In our study domain, covering the contiguous United States, the results show that the soil moisture background error exhibits strong seasonal and regional patterns, with the highest magnitude occurring during the summer at the top soil layer over most regions of the Great Plains. It is also revealed that the soil moisture background errors are strongly biased at some regions, especially Southeastern United States, and bias impacts the magnitude of the error from top to bottom soil layer in an increasing order. Moreover, we also found that the estimated background error is not sensitive to the selection of WRF physics schemes of microphysics, cumulus parameterization, and land surface model. Overall, this study enhances our understanding on the space-time variability of the soil moisture background error and promises more accurate land-surface state estimates via variational data assimilation.