基于卷积神经网络的京津冀地区高分辨率格点预报偏差订正试验
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北京市自然科学基金(8212025;8222051);国家重点研发计划项目(2018YFF0300102);北京市气象局科技项目(BMBKJ202004011);国家自然科学基金(42275012)


A study of error correction for high-resolution gridded forecast based on a convolutional neural network in the Beijing-Tianjin-Hebei Region
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    摘要:

    为了进一步提高RISE系统高分辨率网格化预报产品的准确率,同时考虑到深度学习近年来在地学领域的有效应用,采用2019—2021年高分辨率RISE系统数据,设计出卷积神经网络模型Rise-Unet,实现了未来4~12 h地面2 m温度、2 m相对湿度、10 m-U风速以及10 m-V风速预报结果的订正。订正试验结果表明,采用均方根误差和平均绝对误差作为评分标准,与RISE原始预报结果相比,基于Rise-Unet模型可以有效提高温湿风预报结果的准确率。该基于深度学习的Rise-Unet偏差订正技术可应用于RISE系统的后处理模块,对提升RISE系统百米级分辨率或其他高分辨率模式系统格点预报水平具有重要的科学意义和应用价值。

    Abstract:

    Precise weather monitoring and accurate weather forecast are two of the most decisive factors for the success of the Winter Olympics.Considering the particularity of the 2022 Beijing Winter Olympic Games (the only Winter Olympic Games held under the climate dominated by the continental East Asian winter monsoon and the only Winter Olympic Games held in inland areas) and the rigid demand for the goal of “hundred-meter resolution and minute-updated level” high-precision forecast,the Beijing Institute of Urban Meteorology has developed a new generation of the Rapid-refresh Integrated Seamless Ensemble system—RISE—that can provide 500-and even 100-m resolution spatial grid forecast data products with 10-min updated frequency for the Beijing Winter Olympics.In order to improve the prediction accuracy of the RISE system,and considering the successful use of deep learning in the field of geoscience in recent years,this paper develops a convolution neural network-based model,Rise-Unet,using the high-resolution RISE data from 2019 to 2021 to correct the prediction results of 2-m surface temperature,2 m-relative humidity,10-m U wind speed,and 10-m V wind speed for a lead time of 4—12 hours.The root-mean-square error and mean absolute error are employed to evaluate the accuracy of the model in this study.By comparing with the original prediction results of the RISE system,it is proven that the deep learning-based model,Rise-Unet,can effectively improve the accuracy of high-resolution gridded prediction results.The method proposed in this study can be applied as the post-processing module of the RISE system,which has important scientific significance and application value for improving the grided prediction level of the RISE system as well as other high-resolution numerical weather forecasting systems.

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张延彪,宋林烨,陈明轩,韩雷,杨璐,2022.基于卷积神经网络的京津冀地区高分辨率格点预报偏差订正试验[J].大气科学学报,45(6):850-862. ZHANG Yanbiao, SONG Linye, CHEN Mingxuan, HAN Lei, YANG Lu,2022. A study of error correction for high-resolution gridded forecast based on a convolutional neural network in the Beijing-Tianjin-Hebei Region[J]. Trans Atmos Sci,45(6):850-862.

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  • 收稿日期:2022-06-15
  • 最后修改日期:2022-09-29
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  • 在线发布日期: 2022-12-15
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