DATA: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

Providing accurate soil moisture (SM) conditions is a critical
step in model initialization in weather forecasting, agricultural planning, and
water resources management. This study develops monthly-to-seasonal (M2S) top
layer SM forecasts by forcing 1- to 3-month ahead precipitation forecasts with
Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern
US (SEUS), and the SM forecasting skill is evaluated in comparison with the
remotely sensed SM observations collected by the Soil Moisture Active Passive
(SMAP) satellite. Our results indicate potential in developing real-time SM
forecasts. The retrospective 18-month (April 2015–September 2016) comparison
between SM forecasts and the SMAP observations shows statistically significant
correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.