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.
国家科技期刊平台
NSTL
万方数据
维普
