深度学习技术在智能网格天气预报中的应用进展与挑战

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

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。

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.