基于CMA模式体系的京津冀地区复杂地形下冬季的精细化地面要素多模式集成预报研究

The Multi-Model Blending Forecasts of Near-Surface Parameters Based on CMA Model System

基于CMA模式体系的四个模式(CMA-GFS、CMA-REPS、CMA-MESO 3 km、CMA-MESO 1 km)和2020年12月1日至2021年3月15日的近地面要素2 m温度、10 m风速、2 m相对湿度预报,对京津冀地区复杂地形下冬季误差订正后的各要素进行基于贝叶斯模型平均(BMA)方法的多模式集成试验。结果表明,每个模式各要素误差订正后的均方根误差都有明显的减小。BMA方法多模式集成后预报效果优于每一个参加模式,2 m温度BMA预报较几个模式原始误差的改进在0.5~1.4℃,均方根误差减少了20%~40%,10 m风速和2 m相对湿度的均方根误差分别减少了12%~45%和25%~35%。各要素均方根误差水平分布表明不同要素在不同地形高度的地区误差分布明显不同,此方法使得京津冀地区的误差显著减小。此外,BMA预报的概率分布情况可定量地预测各要素的不确定性。

The multi-model integration test of the Bayesian model averaging(BMA)method is carried out for the forecast after correcting the errors of 2 m temperature,10 m wind speed,and 2 m relative humidity from 1 December 2020 to 15 March 2021 in the Beijing-Tianjin-Hebei Region based on the four models(CMA-GFS,CMA-REPS,CMA-MESO 3 km,and CMA-MESO 1 km).The results show that the root-mean-square error of each model’s element is significantly reduced after error calibration.The prediction effect of the BMA multi-model blending is much better than that of calibrated output of every participant model.Compared with the original errors of several models,the improvement of the 2 m temperature integration forecast is between 0.5-1.4℃,and the improvement rate of the root-mean-square error is about 20%-40%.In the meantime,the root-mean-square error of 10 m wind speed and 2 m relative humidity improved by 12%-45%and 25%-35%,respectively.The horizontal root mean square error distribution of each element is significantly different at different terrain heights,and the error distribution of different elements has been significantly reduced throughout the region.In addition,BMA can obtain the full probability density function,which can quantitatively predict the uncertainty of each element.