基于复卡尔曼滤波技术的华东区域风的多模式集成预报研究

Multi-Model Ensemble Forecasts of Wind over East China by Using Augmented Complex Extended Kalman Filter

基于欧洲中期天气预报中心的业务预报系统(EC)、美国国家环境预报中心的全球预报系统(GFS)、我国的中尺度数值业务预报系统(CMA-MESO)和全球预报系统(CMA-GFS)这4个预报系统的华东及周边地区(20°~40°N、110°~130°E)2020年1—4月逐日地面和高空风的0~72 h预报资料,利用复卡尔曼滤波方法(augmented complex extended Kalman filter,ACEKF)对其进行多模式集成预报试验,并对结果进行检验和评估。结果表明,ACEKF方法的预报效果优于多模式消除偏差集合平均、多模式超级集合预报等方法和单一模式的预报,能够进一步降低风速预报的误差,提高风场预报的预报准确率。ACEKF在高空风速预报上的改进效果要优于地面风速预报,在地形复杂地区改进效果更优,在所有预报时效的均方根误差和距平相关系数上均有体现。

Based on EC,GFS,CMA-MESO and CMA-GFS,the 0-72 h ensemble forecasts of daily surface and high-altitude zonal wind and meridional wind from January to April 2020 from the four models for East China and surrounding areas(20°-40°N,110°-130°E)are evaluated with the augmented complex extended Kalman filter(ACEKF)method.The results show that the ACEKF method outperforms the bias-removed ensemble mean,super-ensemble forecast and single-mode forecasts,and can further reduce the wind speed forecast errors.ACEKF can improve the upper-air wind speed forecasts better than those at ground level.In complex terrain areas the improved wind speed forecast is much better.These results are also reflected in the root-mean-square error and anomaly correlation coefficient for all forecast times.