Abstract
Abstract. 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.
Highlights
Seasonal climate forecasts provide beneficial information for developing hydrologic forecasts that support the planning and management of water resources
Remote sensing of soil moisture (SM) observations using microwave scanners began in the late 1970s with the Scanning Multichannel Microwave Radiometer (SMMR) and continued with the Special Sensor Microwave/Imager (SSM/I)
SM forecasts have limited skill over the western parts of North Carolina and South Carolina, with the correlation becoming insignificant as a result of increasing forecast lead time
Summary
Seasonal climate forecasts provide beneficial information for developing hydrologic forecasts that support the planning and management of water resources. Accurate soil moisture (SM) forecasting can significantly assist the decision-making for agricultural systems. As rain-fed agriculture heavily depends on actual soil moisture conditions and the stress that crops face during the growing phase, long-range SM forecasts would be more advantageous to improve crop yield forecasts. Remote sensing of SM observations using microwave scanners began in the late 1970s with the Scanning Multichannel Microwave Radiometer (SMMR) and continued with the Special Sensor Microwave/Imager (SSM/I). With the launch of Advanced Microwave Scanning Radiometer (AMSR) there is a decade-long dataset (2002– 2011) of SM estimates from space, and the effort continued with the European Space Agency Soil Moisture and Ocean
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