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深度学习技术在智能网格天气预报中的应用进展与挑战

杨绚 代刊 朱跃建

杨绚,代刊,朱跃建. 2022. 深度学习技术在智能网格天气预报中的应用进展与挑战. 气象学报,80(5):649-667 doi: 10.11676/qxxb2022.051
引用本文: 杨绚,代刊,朱跃建. 2022. 深度学习技术在智能网格天气预报中的应用进展与挑战. 气象学报,80(5):649-667 doi: 10.11676/qxxb2022.051
Yang Xuan, Dai Kan, Zhu Yuejian. 2022. Progress and challenges of deep learning techniques in intelligent grid weather forecasting. Acta Meteorologica Sinica, 80(5):649-667 doi: 10.11676/qxxb2022.051
Citation: Yang Xuan, Dai Kan, Zhu Yuejian. 2022. Progress and challenges of deep learning techniques in intelligent grid weather forecasting. Acta Meteorologica Sinica, 80(5):649-667 doi: 10.11676/qxxb2022.051

深度学习技术在智能网格天气预报中的应用进展与挑战

doi: 10.11676/qxxb2022.051
基金项目: 国家重点研发计划项目(2021YFC3000905和2017YFC1502004)、中国气象局重点创新团队(CMA2022ZD04)、中国工程院咨询研究项目(FWC2014)
详细信息
    作者简介:

    杨绚,主要从事数值预报统计后处理研究。E-mail:yx_221@126.com

    通讯作者:

    代刊,主要从事集合预报、定量降水预报研究。E-mail:daikan1998@163.com

  • 中图分类号: P456.8

Progress and challenges of deep learning techniques in intelligent grid weather forecasting

  • 摘要: 中国智能网格天气预报已初步建立0—30 d涵盖基本气象要素的无缝隙气象预报业务体系。近年深度学习技术兴起,给不同领域带来前所未有的变革。同样,深度学习的非线性映射能力、海量信息提取能力、时空建模能力等优势为进一步提升智能网格预报的准确性和精细化水平提供了新的思路和方法。越来越多的研究将深度学习技术应用于智能网格预报的各个方面,包括数值预报订正和解释应用、集合天气预报、相似集合、统计降尺度、纯数据驱动的预报模型和极端天气预报等,并展示出良好的应用潜力。然而,目前深度学习技术在天气预报领域的应用仍处于起步阶段,将其引入智能网格预报业务体系还面临诸多挑战,主要包括算法的选择、算法的数据基础、多源数据融合以及模型的可解释性、可信度、可用性和工程化等。通过回顾近年来深度学习技术在智能网格预报中的应用进展和前景,同时对面临的挑战与应对进行探讨,将有利于促进深度学习技术在天气客观预报领域更好、更稳定的发展。

     

  • 图 1  基于CNN算法的数值模式预报订正和解释应用的常用结构和流程

    Figure 1.  Flowchart of statistical postprocessing for numerical weather prediction using CNN

    图 2  基于深度学习模型的集合预报后处理的常用方法示意

    Figure 2.  Illustration of current statistical postprocessing methods for ensemble forecasts using deep learning models

    表  1  目前中国无缝隙智能网格预报技术体系

    Table  1.   Current technologies of seamless intelligent grid weather forecasting in China

    预报时效技术方法
    临近预报
    (0—4 h)
    多尺度变幅光流临近预报技术
    循环网络和时空记忆的深度学习
    基于迎风格式的平流外推
    残差衰减滚动订正
    短时预报
    (4—24 h)
    基于GRAPES-3 km 实时频率匹配订正技术
    快速分析和预报系统(RAFS,Rapid Analysis and Forecast System)短时动态频率拟合
    动态双因子滚动预报模型
    格点化输出统计快速更新系统(GMOSRR,Gridded Model Output Statistics Rapid Refresh)逐时滚动系统
    全球、中尺度匹配融合
    中短期预报
    (1—10 d)
    基于最优背景场生成技术的定量降水预报
    自适应全球及中尺度集成技术
    集合最优百分位预报技术
    频率匹配订正预报技术
    基于逻辑回归(LR,Logistic Regression)配料法降水预报技术
    基于最优路径的台风暴雨预报技术
    降水相态客观预报技术
    基于背景分析的全球模式输出统计(GMOS,Global Model Output Statistics)技术
    基于区域建模的非连续变量技术
    超前空间实况信息融入技术
    海洋风、能见度客观预报技术
    延伸期预报
    (10—30 d)
    气温最优分位组合方法
    降水分级位相组合方法
    基于集合的逻辑回归方法
    基于集合的卡尔曼滤波方法
    下载: 导出CSV

    表  2  深度学习技术在智能网格预报应用的主要算法和模型列表

    Table  2.   List of main algorithms and models of deep learning in intelligent grid weather forecasting

      应用模块  算法/模型  应用效果、优势  主要参考文献
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    Salazar,et al,2021
    卷积神经网络(CNN)基于卷积等模块计算,相较于ANN,有更好的空间特征提取能力门晓磊等,2019
    陈锦鹏等,2021
    基于CNN算法的U-Net结构U-Net作为语义分割模型应用于预报误差订正,优势是保留原始场的空间信息Dupuy,et al,2021
    Han,et al,2021
    Grönquist,et al,2021
    长短期记忆(LSTM)考虑了时间序列特征在预报解释应用上的影响Zhang C J,et al,2020
    集合预报参数化统计后处理人工神经网络(ANN)利用神经网络自动学习预报变量和分布参数之间的非线性关系Rasp,et al,2018
    Ghazvinian,et al,2021
    非参数化统计后处理基于神经网络的分位数回归方法利用神经网络得到分位数函数的近似值,从而拟合预报变量的概率分布Cannon,2018
    Bremnes,2020
    基于神经网络的直方图法基于神经网络预测预报要素在指定分仓值(bins)的概率来估计概率密度分布Veldkamp,et al,2021
    Scheuerer,et al,2020
    TransformerTransformer算法能够处理全部集合成员,从集合成员之间提取额外信息来订正集合预报Finn,2021
    量化不确
    定性
    卷积神经网络(CNN)只需少量集合成员就可以提供比原始集合成员更有效的预报不确定性信息Grönquist,et al,2021
    生成集合
    预报
    卷积神经网络(CNN)替代数值模式,利用深度学习直接生成集合预报Scher,et al,2018
    相似集合(AnEn)人工神经网络(ANN)利用ANN生成确定性预报,作为附加预报因子组成相似集合Cervone,et al,2017
    条件变分自编码器(CVAE)利用CVAE生成预报要素的概率分布,对概率预报进行订正Fanfarillo,et al,2021
    长短期记忆(LSTM)优化了AnEn计算流程,基于深度学习算法避免了原始AnEn方法在特征选择和权重优化上的缺陷Hu,et al,2021
    统计降尺度长短期记忆递归神经网络
    (RNN-LSTM)
    能够反映局地气象要素的时空相关性Misra,et al,2018
    基于CNN的超分辨率模型SRCNN和DeepSD已成为深度学习在统计降尺度中的基准模型Chao,et al,2014
    Vandal,et al,2017
    Kumar,et al,2021
    基于生成式对抗网络(GAN)的超分辨率模型基于GAN产生更精确的生成样本,代表模型有ESRGANWang,et al,2018
    Singh,et al,2019
    Harris,et al,2022
    数据驱动的预报模型卷积长短时记忆模型 (ConvLSTM)基于时序特征的临近降水预报模型,是该领域里程碑式的工作Shi,et al,2015
    基于注意力机制的MetNet实现了1 km空间分辨率和2 min时间分辨率美国范围内提前7—8 h的降水预报Kaae Sønderby,et al,2020
    预测循环神经网络(PredRNN)、
    运动循环神经网络(MotionRNN)
    显著提升了捕捉和预测雷达回波运动的能力Wang,et al,2017
    Wu,et al,2021
    条件生成对抗网络(CGAN)基于GAN从雷达数据场的条件分布中生成临近降水概率预报Ravuri,et al,2021
    极端天气预报堆叠的CNN模型以堆叠的方式构建深度模型,输出降水等级的概率分布,预报临近极端降水发生的概率Franch,et al,2020
    胶囊神经网络 (CapsNets)能够提取数据特征的属性关系,提高极端气温的预报准确率Chattopadhyay,et al,2020
    下载: 导出CSV
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  • 收稿日期:  2021-12-19
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