人工智能方法在气象领域的应用综述

A review of the application of artificial intelligence methods in the field of meteorology

摘要:

气象预测是人工智能技术应用的重要领域。人工智能方法在捕捉和分析气象系统复杂动态模式方面展现出巨大潜力，为气象预测业务带来新的契机和挑战。首先介绍了传统的机器学习算法如随机森林、XGBoost和支持向量机，虽然在某些方面比传统方法性能要佳，但也存在一定局限性。其次深度学习模型凭借其强大的特征提取和模式识别能力，在分析和预测气象变量方面表现出色，尤其是神经网络、卷积神经网络和循环神经网络等。最后介绍了大模型如盘古、伏羲和GraphCAST在提高预测准确性方面的巨大潜力。同时，文中也指出了未来需要关注的研究方向，包括模型优化、算法改进、提高数据质量和多样性，以及跨学科融合等，以期为气象预测的持续发展提供理论支撑和技术保证。

Abstract:

Artificial intelligence (AI) has become an indispensable tool in the field of meteorological forecasting, showcasing its prowess in deciphering the complex dynamics of weather systems. This article delves into the application of AI in meteorological prediction, outlining both its current successes and the challenges that lie ahead. Firstly, traditional machine learning algorithms such as random forests, XGBoost, and support vector machines are introduced, which, despite performing better than conventional methods in certain aspects, exhibit inherent limitations. Secondly, deep learning models, leveraging their powerful feature extraction and pattern recognition capabilities, have excelled in analyzing and predicting meteorological variables, particularly neural networks, convolutional neural networks, and recurrent neural networks. Furthermore, the text underscores the significant potential of large-scale models like Pangu, FuXi, and GraphCAST in bolstering the accuracy of weather predictions. In conclusion, the study proposes key areas for future research, emphasizing the need for model optimization, algorithmic enhancement, data quality and diversity improvements, and interdisciplinary collaboration. These efforts are crucial for laying the groundwork and providing the technological support necessary to propel meteorological forecasting into a new era of sophistication and reliability.