Forecasting surface temperature and wind speed at Winter Olympics stations over complex terrain based on the CMA-BJ model products

Beijing Winter Olympic service has made a clear request for prediction of weather elements at individual sites. The 2 m temperature forecast bias is less than ±2℃, and 10 m wind speed forecast bias is less than 30% of observation average. This paper proposes a forecast method based on analog ensemble (AnEn) nested linear regression (LR)—analog ensemble nested linear regression (AnEn-Ne). When certain conditions are met, the nested linear regression is activated to provide revised forecasts. The real-time operational forecasts during the Winter Olympic period (1 November 2021 to 15 March 2022) show that the AnEn-Ne method has a better forecast effect. Compared with that of the CMA-BJ, the forecast accuracy of the AnEn-Ne is improved significantly; compared with that of AnEn and LR, the forecast accuracy of the AnEn-Ne is obviously improved, and the forecasts meet the service demand at the Winter Olympic stations. The verification of forecasted elements in complex terrain area shows that, despite an obvious systematic deviation of 2 m temperature in the forecast by the CMA-BJ, the forecasts are strongly correlated with observations and show an obvious representation of observations, and the influence of complex terrain can be effectively eliminated after correction. The CMA-BJ forecast Bias of 10 m wind speed demonstrates an oscillation feature, and the correlation between the model forecasts and observations is weak, indicating a poor model representation of surface wind. The differences in 10 m wind speed between stations are obvious after correction. Improving the representation of the CMA-BJ model on surface wind speed prediction in complex terrain areas can further improve the accuracy of the method for the correction of 10 m wind speed prediction.