Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction (NWP) models have always presented large forecasting errors of surface wind speeds over regions with complex terrain. In this study, surface wind forecasts from an operational NWP model, the SMS-WARR (Shanghai Meteorological Service-WRF ADAS Rapid Refresh System), are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features, with the intent of providing clues to better apply the NWP model to complex terrain regions. The terrain features are described by three parameters: the standard deviation of the model grid-scale orography, terrain height error of the model, and slope angle. The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography. The minimum ME (the mean value of bias) is 1.2 m s⁻¹ when the standard deviation is between 60 and 70 m. A positive correlation exists between bias and terrain height error, with the ME increasing by 10%–30% for every 200 m increase in terrain height error. The ME decreases by 65.6% when slope angle increases from (0.5°–1.5°) to larger than 3.5° for uphill winds but increases by 35.4% when the absolute value of slope angle increases from (0.5°–1.5°) to (2.5°–3.5°) for downhill winds. Several sensitivity experiments are carried out with a model output statistical (MOS) calibration model for surface wind speeds and ME (RMSE) has been reduced by 90% (30%) by introducing terrain parameters, demonstrating the value of this study.