气象多模态数据融合方法研究综述OA

Advancements in multi-source observation technologies have ushered in the era of multimodal meteorological data.However,single-modal data is inherently limited in its ability to characterize the complex and dynamic atmospheric system,failing to meet the demand for more precise predictions.Consequently,integrating multimodal data to leverage complementary information and enhance performance has become a key research frontier in meteorology.This paper addresses the core challenge of effectively integrating meteorological data from different modalities by providing a systematic review of multimodal data fusion methods.First,this paper traces the evolution of multimodal fusion techniques,with a particular focus on deep learning-based strategies.It elaborates on the core concepts,architectural features,and advantages of mainstream models,such as encoder-decoder architectures,attention mechanisms,graph neural networks,and generative adversarial networks in the context of multimodal data fusion.Then,this paper comprehensively analyzes the current applications of this technology.Drawing on a systematic compilation of publicly available multimodal meteorological data-sets,this paper reviews the technology's application in key tasks,including precipitation nowcasting and predicting the paths and intensities of typhoons and tropical cyclones.It summarizes the research progress and effectiveness of different fusion methods in these scenarios.Furthermore,this paper analyzes the key challenges currently facing multimodal fusion in meteo-rology.Finally,based on the identified challenges,it outlines future research directions for the field.