气象学报2022，Vol.80Issue(5)：649-667,19.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
Progress and challenges of deep learning techniques in intelligent grid weather forecasting
摘要
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.关键词
智能网格预报/深度学习/统计后处理/统计降尺度/数据驱动预报模型Key words
Gridded weather forecasting/Deep learning/Statistical post-process/Statistical downscaling/Data-driven forecasting model分类
天文与地球科学引用本文复制引用
杨绚,代刊,朱跃建..深度学习技术在智能网格天气预报中的应用进展与挑战[J].气象学报,2022,80(5):649-667,19.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）。 （）
