| 人工智能技术气候预测应用简介 |
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| 引用本文： | 杨淑贤,零丰华,应武杉,杨松,罗京佳.人工智能技术气候预测应用简介[J].大气科学学报,2022,45(5):641-659. |
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| 作者姓名： | 杨淑贤 零丰华 应武杉 杨松 罗京佳 |
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| 作者单位： | 南京信息工程大学 气候与应用前沿研究院/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044 |
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| 基金项目： | 国家重点研发计划项目（2020YFA0608004）；国家自然科学基金资助项目（42030605） |
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| 摘 要： | 近年来，随着人工智能技术在多个领域大数据分析中的应用，许多研究工作者尝试将地学研究与人工智能跨学科结合，取得了很多新的进展，推动了地球科学的发展。其中气候预测与人类生活以及防灾减灾等息息相关，准确的气候预测至关重要。本文简要总结了人工智能技术在气候预测应用方面的研究进展，包括资料同化、模式参数化、求解偏微分方程、构建统计预测模型、改进数值模式产品释用等领域。这些研究证明了利用人工智能提高气候预测技巧的可能性和适用性，可以极大地节省计算成本和时间。然而人工智能应用也存在诸多挑战，例如数据集的构建、模型的适用性和物理可解释性等问题，对这些难点问题的研究和攻克，可以让人工智能在大数据时代中更好地补充传统地球科学方法，产生更多有益的效应，极大地改进气候预测水平。
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| 关 键 词： | 人工智能 数值产品释用 气候预测 |
| 收稿时间： | 2021/6/23 0:00:00 |
| 修稿时间： | 2021/12/28 0:00:00 |
A brief overview of the application of artificial intelligence to climate prediction |
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| YANG Shuxian,LING Fenghu,YING Wushan,YANG song,LUO Jingjia.A brief overview of the application of artificial intelligence to climate prediction[J].大气科学学报,2022,45(5):641-659. |
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| Authors: | YANG Shuxian LING Fenghu YING Wushan YANG song LUO Jingjia |
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| Institution: | Institute for Climate and Application Research (ICAR)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | In recent years, artificial intelligence (AI) has made great achievements in big data analysis in many fields.Consequently, many researchers have attempted to combine geoscience studies with AI, which has made new progress and can promote the development of Earth science.Climate prediction is closely related to human life and disaster prevention and mitigation, thus its prediction accuracy is highly important.This study briefly summarizes the recent progresses on the application of AI to climate prediction, including data assimilation, model parameterization, partial differential equation solution, prediction models, and numerical model output improvement.The results of this study demonstrate the possibility and applicability of using AI to improve climate prediction, which can significantly reduce computational costs and time.However, there are also many challenges involved in the application of AI, such as the construction of input data sets, the applicability of AI models, and their physical interpretability.Exploring and solving these difficult problems can help geoscience that involves many multi-source data to better utilize AI and thus improve climate prediction. |
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| Keywords: | artificial intelligence application to numerical model products climate prediction |
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