Improving surface wind speed forecasts using an offline surface multilayer model with optimal ground forcing

An essential task of numerical weather prediction (NWP) is accurate surface wind speed prediction. However, current NWP 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 data sets 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 the original WRF forecasting, using the GF(o) scheme reduces the root mean square error 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.