Improving the Forecasts of Coastal Wind Speeds in Tianjin, China, Based on the WRF Model with Machine Learning Algorithms

Characterized by sudden changes in strength, complex influencing factors, and significant impacts, the wind speed in the circum-Bohai Sea area is relatively challenging to forecast. On the western side of Bohai Bay, as the economic center of the circum-Bohai Sea, Tianjin exhibits a high demand for accurate wind forecasting. In this study, three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts of the Weather Research and Forecast (WRF) model for Tianjin. The results showed that the random forest (RF) achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost, while the support vector machine (SVM) performed slightly worse (especially for stronger winds), but it required an approximately 15 times longer computing time. The back propagation (BP) neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE. In regard to wind speed frequency forecasting, the RF method commendably corrected the forecasts of the frequency of moderate (force 3) wind speeds, while the BP method showed a desirable capability for correcting the forecasts of stronger (force > 6) winds. In addition, the 10-m u and v-components of wind (Ua_10/Va_10), 2-m relative humidity (RH2) and temperature (T2), 925-hPa u (Ua_925), sea level pressure (SLP), and 500-hPa temperature (T_500) were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin, indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed. This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.