华东地区地面和高空风场的多模式集成精细化预报研究
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南京信息工程大学

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中国民用航空华东地区管理局研发项目“华东地区机场及终端区风场预报预警系统研究”;国家自然科学基金重大研究计划集成项目(批准号:91937301)


Multimodel ensemble forecasts of high-resolution surface and high-level wind forecasts over East China
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NUIST

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    摘要:

    基于欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)、中国国家气象中心业务运行的中尺度数值预报系统(Global/Regional Assimilation and Prediction System Meso, GRAPES-Meso)、美国国家环境预报中心(National Centers for Environmental Prediction, NCEP)的全球预报系统(Global Forecast System, GFS)、GRAPES全球预报系统(GRAPES-GFS)4个模式风场预报资料,利用双线性、反距离加权、三次样条、克里格等插值方法对华东及周边地区(110~130°E, 20~40°N)2020年1~4月逐日地面和高空风0~72 h集合预报资料进行降尺度处理,得到满足机场及终端区气象保障的精细化风场预报。此外,还对精细化风场预报做多模式集成。结果表明,对于风场的精细化格点预报,反距离加权插值方法误差最小,为最优水平插值方法。基于扩展复卡尔曼滤波的多模式集成(Augmented Complex Extended Kalman Filter,ACEKF)可进一步减小风场预报的误差。无论是地面还是高空ACEKF风场预报均方根误差明显小于单模式预报以及消除偏差集合平均(BREM)预报的均方根误差。对华东地区上海、青岛和厦门3个机场地面和高空风的多模式集成风场精细化预报的分析表明,ACEKF多模式集成预报不但均方根误差较BREM、ECMWF和GRAPES-GFS的预报均方根误差小,且随高度变化也不如单模式预报的大,其预报性能更为稳定。

    Abstract:

    Based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Mesoscale of the Global and Regional Assimilation and Prediction System (GRAPES-Meso), the Global Forecast System (GFS) of National Centers for Environmental Prediction (NCEP), and the Global Forecast System of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) wind forecast data over East China and surrounding areas (110°~130°E, 20°~40°N) from January to April , 2020, the bilinear interpolation, inverse distance weighted interpolation, kriging interpolation and cubic spline interpolation were applied to produce the 0~72h downscaling forecasts in order to provide the high-resolution forecast service for the airports and their terminal areas. In addition, multimodel ensemble forecasts of the high-resolution wind have been conducted. The results show that the inverse distance weighted interpolation is the best interpolation scheme for the horizontal interpolation of the wind forecasts. The augmented complex extended Kalman Filter (ACEKF) based multimodel ensemble forecasts further reduce the root-mean-square errors (RMSEs) of the wind fields. No matter for the surface winds or high-level winds, ACEKF forecasts are significantly superior to those of bias-removed ensemble mean (BREM) and individual models in terms of their RMSEs. The surface and high-level wind forecasts at three airports in East China, namely, Shanghai, Qingdao and Xiamen, show that the RMSEs of the ACEKF forecasts are not only smaller than those of BREM, ECMWF and GRAPES-GFS forecasts, but also less variable with altitudes, the performance of the wind forecasts is more stable than that of BREM and individual model forecasts.

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  • 收稿日期:2021-03-18
  • 最后修改日期:2021-04-06
  • 录用日期:2021-04-13
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