Impact of Bias-Correction Methods on Effectiveness of Assimilating SMAP Soil Moisture Data into NCEP Global Forecast System Using the Ensemble Kalman Filter

Improving numerical weather prediction was one of the main justifications for National Aeronautics and Space Administration's Soil Moisture Active/Passive (SMAP) Mission. The ensemble Kalman filter (EnKF) has been extensively applied to assimilate the SM observations into numerical weather predication models. Implementation of EnKF requires the observations and model simulations to be Gaussian distributed and not biased from each other. In this letter, we tested the impacts of three bias-correction methods on effectiveness of assimilating SMAP retrievals into the National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction Global Forecast System (GFS). They are: 1) global cumulative distribution function (CDF) matching with only one CDF for all grids and time series; 2) monthly CDF matching with one CDF for each grid; 3) the linear transformation technique that matches monthly mean and standard deviation of the SMAP retrievals and model simulations for each grid; and 4) assimilating SMAP SM data into GFS without any bias-correction procedure. With respect to the global land data assimilation (DA) system precipitation product, the results demonstrate that the effectiveness of assimilating SMAP retrievals into GFS is significantly impacted by the bias-correction methods. Relative to other DA cases, the monthly CDF matching produces the best precipitation forecast performance. Improvements of the three-hourly GFS precipitation prediction with SMAP assimilation using the monthly CDF matching can reach to 8% and 10% in sparsely and densely vegetated areas, respectively, and marginally positive in medium vegetation areas. Based on these results, assimilating SMAP retrievals into the GFS with the EnKF algorithm using the monthly CDF matching method is suggested for enhancing accuracy of the precipitation forecasts.