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基于深度学习的京津冀地区精细尺度降水临近预报研究

曹伟华 南刚强 陈明轩 程丛兰 杨璐 吴剑坤 宋林烨 刘瑞婷

曹伟华,南刚强,陈明轩,程丛兰,杨璐,吴剑坤,宋林烨,刘瑞婷. 2022. 基于深度学习的京津冀地区精细尺度降水临近预报研究. 气象学报,80(4):546-564 doi: 10.11676/qxxb2022.027
引用本文: 曹伟华,南刚强,陈明轩,程丛兰,杨璐,吴剑坤,宋林烨,刘瑞婷. 2022. 基于深度学习的京津冀地区精细尺度降水临近预报研究. 气象学报,80(4):546-564 doi: 10.11676/qxxb2022.027
Cao Weihua, Nan Gangqiang, Chen Mingxuan, Cheng Conglan, Yang Lu, Wu Jiankun, Song Linye, Liu Ruiting. 2022. A study on fine scale precipitation nowcasting in Beijing-Tianjin-Hebei region based on deep learning. Acta Meteorologica Sinica, 80(4):546-564 doi: 10.11676/qxxb2022.027
Citation: Cao Weihua, Nan Gangqiang, Chen Mingxuan, Cheng Conglan, Yang Lu, Wu Jiankun, Song Linye, Liu Ruiting. 2022. A study on fine scale precipitation nowcasting in Beijing-Tianjin-Hebei region based on deep learning. Acta Meteorologica Sinica, 80(4):546-564 doi: 10.11676/qxxb2022.027

基于深度学习的京津冀地区精细尺度降水临近预报研究

doi: 10.11676/qxxb2022.027
基金项目: 北京市自然基金项目(8192016、8204060)、国家自然科学基金项目(41801022)、国家重点研发计划项目(2017YFC1502104、2018YFC1507504)
详细信息
    作者简介:

    曹伟华,主要从事临近预报与灾害风险研究。E-mail:whcao@ium.cn

  • 中图分类号: P456.9

A study on fine scale precipitation nowcasting in Beijing-Tianjin-Hebei region based on deep learning

  • 摘要: 精细尺度降水的临近预报对于提升现代城市内涝和山洪地质灾害预警能力具有重要意义。深度学习作为一种新兴方法,在挖掘数据内部特征及物理规律方面更具优势,近年来在天气雷达图像领域的应用已初见成效。为进一步提升精细尺度降水的临近预报能力,基于深度学习网络模型RainNet,研究建立了两种滚动预报方式,开展了京津冀地区1 km分辨率精细尺度降水滚动式临近预报试验和对比分析。 试验结果表明:与传统基于交叉相关的外推预报相比,深度学习网络模型RainNet总体可以明显改进降水1 h临近预报的绝对误差和相关系数;两个RainNet相结合的滚动预报方式对1.04 mm/(10 min)及以下阈值降水,在10—50 min预报性能一致优于传统的交叉相关外推预报。深度学习模型对降水消亡过程的时、空演变趋势刻画更好,尤其更适用于降水消亡过程的临近预报。采用两个RainNet模型相结合的滚动式预报方式优于单一模型滚动预报方式。

     

  • 图 1  京津冀地区地形海拔高度 (单位:m) 及8部天气雷达位置 (“+”表示)

    Figure 1.  Surface elevation (unit:m) around the study area in contiguous North China and the eight radar sites of the operational China Next Generation Weather Radar network are displayed (+ symbols)

    图 2  研究区域范围 (虚线矩形框为原始降水分析场的覆盖范围581 km×511 km,实线矩形为裁剪后研究范围576 km×496 km)

    Figure 2.  Study domain (the dotted rectangle is the domain of 581 km×511 km for original precipitation analysis,the rectangle within the solid line is the final study domain of 576 km×496 km)

    图 3  深度学习网络模型的结构

    Figure 3.  Illustration of the RainNet architecture

    图 4  基于深度学习网络模型的滚动式临近预报原理示意

    Figure 4.  Schematic diagram of rolling nowcast based on deep learning network model

    图 5  交叉相关方法示意 (图a、b分别代表tt+Δt时刻降水场;黑色矩形框代表求交叉相关的像素点区域,r代表搜索半径,黑色矢量为TREC矢量,所指向终点代表所匹配的具有最大相关系数的矩形中心)

    Figure 5.  Schematic diagram showing the computation of the TREC (Fig. 5a and Fig. 5b represent precipitation analysis at time t and tt respectively,r is search radius,the black vector is the TREC vector,the end point of which is the centre of the array at time tt with maximum correlation coefficient)

    图 6  2020年6—9月的绝对误差空间分布 (单位:mm/(10 min);a. 基于交叉相关的外推预报 (TREC) 绝对误差,b. 两个深度学习模型相结合的滚动预报 (RainNet) 绝对误差;下标1—6分别表示预报时效为10、20、30、40、50、60 min)

    Figure 6.  Spatial distributions of mean absolute error (MAE) during June to Septemper 2020 (unit:mm/(10 min); a. TREC predictions,b. two rolling RainNet predictions;1—6 represents the forecast time in 10,20,30,40,50 and 60 min,respectively)

    Continued

    图 7  2020年6—9月京津冀地区的相关系数空间分布 (a. 基于交叉相关的外推预报 (TREC) 相关系数,b. 两个深度学习模型相结合滚动预报 (RainNet) 的相关系数;下标1—6分别表示预报时效为10、20、30、40、50、60 min)

    Figure 7.  Spatial distributions of correlation coefficient during June to September 2020 (a. TREC predictions,b. RainNet predictions;1—6 represents the forecast time in 10,20,30,40,50 and 60 min,respectively)

    Continued

    图 8  绝对误差的时间序列箱线图 (黑色方框的上、下边界为75%、25%分位数,上须和下须分别是90% 和10%分位数,黑盒子中的白线和白三角分别是中位数和均值;a—f. 分别是预报时效为10、20、30、40、50、60 min的结果)

    Figure 8.  Box plots of mean absolute error (the upper and lower of the black box are the quantiles of 75% and 25%,the upper and lower whiskers are 90% and 10% quantiles,the white line and triangle in the black box show the median and mean values,respectively;a—f. the forecast time is 10,20,30,40,50 and 60 min,respectively)

    图 9  相关系数的时间序列箱线图 (黑色方框的上、下边界为75%、25%分位数,上须和下须分别是90% 和10%分位数,黑盒子中的白线和白三角分别是中位数和均值;a—f. 分别是预报时效为10、20、30、40、50、60 min的结果)

    Figure 9.  Box plots of correlation coefficient (the upper and lower of the black box are the quantiles of 75% and 25%,the upper and lower whiskers are 90% and 10% quantiles,the white line and triangle in the black box show the median and mean values,respectively;a—f. the forecast time is 10,20,30,40,50 and 60 min,respectively)

    图 10  2020年6—9月降水临近预报的相关系数 (a) 和绝对误差 (b) 曲线

    Figure 10.  Mean absolute errors (a) and correlation coefficients (b) during June to September 2020

    图 11  2020年6—9月不同降水阈值 (a—d) 的CSI评分曲线

    Figure 11.  Critical success indexes (CSI) for four different intensity thresholds (a—d) during June to September 2020

    图 12  2020年7月31日11时20分起报10—60 min (a1—a6、b1—b6) 降水临近预报结果及降水实况(c1—c6) (a. 交叉相关外推预报,b. 深度学习RainNet预报,c. 相应时间的降水实况)

    Figure 12.  Predictions of TREC (a1—a6) and RainNet (b1—b6) as well as corresponding observations (c1—c6) at 11:20 UTC 31 July 2020 (1—6 represents the forecast time in 10,20,30,40,50 and 60 min,respectively)

    Continued

    图 13  2020年8月1日13时50分起报10—60 min (a1—a6、b1—b6) 降水临近预报结果及降水实况 (c1—c6)(a. 交叉相关外推预报,b. 深度学习RainNet预报,c. 相应时间的降水实况)

    Figure 13.  Predictions of TREC (a1—a6) and RainNet (b1—b6) as well as corresponding observations (c1—c6) at 13:50 UTC 1 August 2020 (1—6 represents the forecast time in 10,20,30,40,50 and 60 min,respectively)

    Continued

    图 14  2020年8月15日03时00分起报10—60 min (a1—a6、b1—b6) 降水临近预报结果及降水实况 (c1—c6)(a. 交叉相关外推预报,b. 深度学习RainNet预报,c. 相应时间的降水实况)

    Figure 14.  Predictions of TREC (a1—a6) and RainNet (b1—b6) as well as corresponding observations (c1—c6) at 03:00 UTC 15 August 2020 (1—6 represents the forecast time in 10,20,30,40,50 and 60 min,respectively)

    Continued

    表  1  模型数据集信息

    Table  1.   Main parameters of the datasets

    年份训练集测试集
    2017201820192020
    样本数2852319929573266
    总计90083266
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-02
  • 录用日期:  2022-06-15
  • 修回日期:  2022-03-07
  • 网络出版日期:  2022-03-09

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