人工智能技术在数值天气预报中的应用

当前，人工智能迎来第3次发展浪潮并在多个领域大数据分析中取得巨大成功，这为人工智能技术与数值天气预报结合提供了契机。已有大量研究尝试将人工智能技术用于数值天气预报的初值生成、预报和产品应用过程中，涉及观测资料预处理、资料同化、模式积分、后处理以及高性能计算，通过误差估计、参数估计和局部代理等手段使预报结果，得到改进且计算速度大幅提升，展示出良好的应用前景，一些神经网络模型也表现出纯数据驱动预报的可能性，在短时强对流天气、降水以及气候预测中已有较为理想的应用实例。然而，人工智能技术在数值天气预报中的应用与发展仍面临一些挑战，主要包括深度学习的弱解释性、不确定性分析以及两者的耦合等，除了应对这些挑战，未来两者的深度结合还需要在理论指导下的人工智能模型设计、高时空分辨率人工智能预报模型设计以及使用更多新型人工智能技术等方面深入探索。

Application of Artificial Intelligence Technology to Numerical Weather Prediction

Numerical weather prediction technology plays an increasingly important role in improving accuracy and service level of modern weather forecast.With the development of observation system and higher resolution and complexity of the numerical weather prediction model,the products of numerical weather forecast have been greatly improved in quantity and quality,and can offer rich information at high spatial-temporal frequency.However,such a large amount of prediction data are not fully explored.Artificial intelligence has achieved great success in many fields,such as pattern recognition and natural language processing,which provides an opportunity for further improving numerical weather prediction.It's also employed in initialization,numerical model and production of weather forecast service,involving observation system,data assimilation,model integration,ensemble forecast and high-performance computing methods.Both the accuracy of forecast results and computational efficiency have been improved by using error correction,parameter estimation,local surrogate model and so on.In addition,some end-to-end neural network models also show the potential of pure data-driven weather forecast.These models use spatial-temporal observation data as input and directly output the prediction results in terms of deterministic results or probabilities.Some of them perform well in short-term severe convective weather,precipitation,and long-term climate forecast.Existing works employ various artificial intelligence technology methods,mainly including large-scale calculation of neural network,feature analysis,interpretability,and customized loss function.However,there are still some challenges,the potential of artificial intelligence needs to be further explored.Some issues should be carefully considered,including weak interpretability,uncertainty analysis and the coupling with conventional numerical models,and how to use physical knowledge to guide the design of artificial intelligence model is also worth addressing.To deal with these challenges,some promising suggestions are proposed.Bayesian network and causal network will help to establish more comprehensive and profound feature engineering.Using Bayesian inference to generate distribution characteristics of current meteorological states may be an alternative to efficient and effective uncertainty quantification.The development of some standard workflow and framework will contribute to the coupling of conventional numerical model and artificial intelligence module.Successful artificial intelligence applications in weather forecast require deep cooperation between meteorological experts and computer experts who focus on artificial intelligence and high-performance computing.