Mitigate forecast error in surface wind speed using an offline single-column model with optimal ground forcing

Current numerical weather prediction models contain significant systematic errors, due in part to indeterminate ground forcing (GF). This study considers an optimal virtual GF (GF_o) derived by training observed and simulated datasets of 10-m wind speeds (WS₁₀) for summer and winter. The GF_o is added to an offline surface multilayer model (SMM) to revise predictions of WS₁₀ in China by the Weather Research and Forecasting model (WRF). This revision is a data-based optimization under physical constraints. It reduces WS₁₀ errors and offers wide applicability. The resulting model outperforms two purely physical forecasts (the original WRF forecast and the SMM with physical GF parameterized using urban, vegetation, and subgrid topography) and two purely data-based revisions (i.e., multilinear regression and multilayer perceptron). Compared with original WRF forecasting, using the GF_o scheme reduces the Root Mean Square Error (RMSE) in WS₁₀ across China by 25% in summer and 32% in winter. The frontal area index of GF_o indicates that it includes both the effects of indeterminate GF and other possible complex physical processes associated with WS₁₀.