Progress and challenges of deep learning techniques in intelligent grid weather forecasting
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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。Abstract: 0—30 d seamless fine gridded weather forecasts have been initially established to cover fundamental forecast elements in China. In recent years, the advances and applications of deep learning have brought unprecedented changes to different fields. The capabilities of nonlinear mapping, massive information extraction, spatial-temporal modeling and other advantages of deep learning provide new concepts and methods for further improvement of forecast accuracy and refinement. The growing studies on deep learning techniques have been applied widely to weather forecasting, including statistical postprocessing, ensemble forecasting, analog ensemble, statistical downscaling, data-driven forecasting models and extreme weather forecasting. The deep learning techniques have demonstrated a great application potential. However, the application of deep learning in gridded weather forecasting is still at the initial stage. The challenges include algorithm selection, benchmark dataset, multi-source data blending, interpretability, reliability, availability and operational implementation, etc., when introducing it into current Intelligent Grid Forecast System. Review of the progress and challenges of the deep learning at fine gridded weather forecasting in recent years will be helpful for us to better understand deep learning techniques and their application in weather forecasting.
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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
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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 模式解释应用 人工神经网络（ANN） 构建数据间非线性关系，允许加入更多预报因子等数据 McGovern，et al，2017
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，2020Transformer Transformer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ESRGAN Wang，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
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