[1]范娇,曾小团,黄荣成,等.深度学习在降水预报中的研究和应用进展[J].气象研究与应用,2024,45(03):1-11.[doi:10.19849/j.cnki.CN45-1356/P.2024.3.01]
　FAN Jiao,ZENG Xiaotuan,HUANG Rongcheng,et al.Research and application progress of deep learning in precipitation forecasting[J].Journal of Meteorological Research and Application,2024,45(03):1-11.[doi:10.19849/j.cnki.CN45-1356/P.2024.3.01]
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深度学习在降水预报中的研究和应用进展()
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气象研究与应用[ISSN:1673-8411/CN:45-1356/P]

卷:
第45卷
期数:
2024年03期
页码:
1-11
栏目:
综述
出版日期:
2024-09-15

文章信息/Info

Title:
Research and application progress of deep learning in precipitation forecasting
作者:
范娇1; 2;  曾小团1;  黄荣成1;  黄增俊2
1. 广西壮族自治区气象台, 南宁 530022;
2. 南宁市气象局, 南宁 530029
Author(s):
FAN Jiao1; 2;  ZENG Xiaotuan1;  HUANG Rongcheng1;  HUANG Zengjun2
1. Guangxi Meteorological Observatory, Nanning 530022, China;
2. Nanning Meteorological Bureau, Nanning 530029, China
关键词:
深度学习; 降水预报; 数值天气预报; 模式耦合
Keywords:
deep learning; precipitation forecast; numerical weather forecasting; modes coupling
分类号:
P456
DOI:
10.19849/j.cnki.CN45-1356/P.2024.3.01
文献标志码:
10.19849/j.cnki.CN45-1356/P.2024.3.01
摘要:
主要总结气象领域常用深度学习算法特征、纯数据驱动的深度学习降水预报技术和深度学习与数值天气预报耦合技术在临近、短时、中短期、气候降水预报中的探索成果，同时对气象业务预报中深度学习的应用进行简单回顾。传统深度学习算法的优缺点不同，在实际应用中，需要根据具体的气象数据特性和业务需求来选择合适的模型和方法。临近预报方面，常使用深度学习算法建立降水预报模型从而进行强降水云团空间特征提取和时间演变分析。长时间序列降水预报，则主要通过初始场资料同化、模式改进、模式预报后处理等方面优化数值模式降水预报效果。深度学习在解决降水预报技术方面有巨大潜力，部分成果已经实现业务应用，尤其在临近降水方面，对于更长时效的降水预测应用还有待进一步研究。
Abstract:
This article summarizes the characteristics of commonly used deep learning algorithms in the meteorological field，as well as the exploration results of pure data-driven deep learning precipitation forecasting technology and deep learning coupled with numerical weather forecasting technology in near， medium-short-term，and extended period precipitation forecasting. At the same time，a brief review is made on the application of deep learning in meteorological operational forecasting. The advantages and disadvantages of traditional deep learning algorithms are different. In practical applications，it is necessary to choose appropriate models and methods based on specific meteorological data characteristics and business needs. In terms of proximity prediction，deep learning algorithms are often used to establish precipitation prediction models for extracting spatial features and analyzing temporal evolution of heavy precipitation cloud clusters; Long time series（medium to short term，extended period）precipitation forecasting mainly optimizes the numerical model precipitation forecasting effect through initial field data assimilation， model improvement，and model prediction post-processing. At present，meteorological departments in various regions have applied deep learning algorithms in business forecasting. In the future，specific problems still need to be addressed，a large amount of related work needs to be carried out to further promote the development of precipitation forecasting.

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备注/Memo

备注/Memo:
收稿日期:2024-2-15。
基金项目:广西自然科学基金项目（2022GXNSFAA035482）、广西气象科研计划项目（桂气科2022QN05、桂气科2020QN03）
作者简介:范娇(1993-),工程师,主要从事数值天气预报研究。E-mail:742895059@qq.com
通讯作者:曾小团,正高级工程师,主要从事天气预报技术研究和系统开发工作。E-mail:158083890@qq.com
更新日期/Last Update: 2024-09-15