Abstract:

The groundbreaking advancements in deep learning technology establish a new paradigm for interdisciplinary research in “AI + meteorology”， and severe convective weather prediction emerges as a cutting-edge research focus due to its complex dynamical characteristics and significant socioeconomic impacts. Therefore， the theoretical advancements and methodological innovations of Deep Neural Networks （DNN） in severe convective weather prediction were reviewed systemically， and their specific applications were explored deeply. Firstly， based on the spatio-temporal sequence prediction paradigm， the mechanisms of high-frequency feature extraction by Recurrent Neural Networks （RNNs） and non-RNNs in meteorological sequence modeling were dissected. Secondly， from a generative modeling perspective， the advantages of Generative Adversarial Network （GAN） and diffusion model in probabilistic prediction of extreme weather events were demonstrated. Thirdly， the theoretical breakthroughs of meteorological large-scale models realizing multimodal data fusion and cross-scale feature learning via pre-training and fine-tuning paradigms were revealed， along with their generalization enhancement mechanisms in global numerical weather prediction. Fourthly， aiming at model evaluation systems， the limitations of traditional statistical metrics in extreme weather prediction were analyzed， and pathways for constructing novel evaluation frameworks such as physical consistency constraints were discussed. Finally， the key scientific challenges faced currently and future research directions were distilled， aiming to provide theoretical support and methodological references for constructing the next-generation intelligent system for severe convective weather prediction.

Key words: deep learning, severe convective weather prediction, Generative Adversarial Network (GAN), diffusion model, meteorological large-scale model

摘要：

深度学习技术的突破性进展为“AI+气象”交叉学科的研究开辟了新的范式，而强对流天气预测因为它的复杂动力学特征和重大社会经济影响成为前沿研究热点。因此，本文系统梳理深度神经网络（DNN）在强对流天气预测领域的理论进展和方法创新，并深入探讨它们的具体应用。首先，基于时空序列预测范式剖析循环神经网络（RNN）与非RNN在气象时序建模中的高频特征捕获机理；其次，从生成式建模的视角，论证生成对抗网络（GAN）和扩散模型在极端天气事件概率预测中的建模优势；再次，揭示气象大模型通过预训练-微调范式实现多模态数据融合与跨尺度特征学习的理论突破，并阐释它们在全球数值天气预报中的泛化能力提升机制；继次，针对模型评估体系，分析传统统计指标在极端天气预测中的局限性，并探讨物理一致性约束等新型评估框架的构建路径；最后，凝练出当前面临的关键科学挑战及未来研究方向，旨在为构建新一代强对流天气预测智能系统提供理论支撑与方法论参考。

关键词: 深度学习, 强对流天气预测, 生成对抗网络, 扩散模型, 气象大模型