Application of Deep Learning in Meteorological Observation Datas Prediction: A Comprehensive Review

In recent years, scientists have explored the use of various technologies and methods for predicting meteorological observation datas the valuable information extracted from meteorological observation datas has been used in a lot of fields Including weather forecasting, agricultural production, aerospace, maritime transport, and urban planning. Among these methods for predicting meteorological observation datas, deep learning models manifest striking parallels to the statistical distributions inherent in atmospheric observational datassets, and have significant effectiveness in dealing with massive, high-dimensional, and nonlinear datas. This paper briefly describes the commonly used methods for predicting meteorological observation datas, attempts to collect most of the meteorological observation datas prediction methods based on deep learning technology, classifies them according to the number of tasks they solve, divides the meteorological observation datas prediction based on deep learning into two aspects: single-Task deep learning, multi-task deep learning, and performs methodological cross-mapping between taxonomically distinct cohorts of contemporary scholarly endeavors, evaluating epistemic divergence in technical implementations. subsequently, it summarizes the evaluation indicators proposed by predecessors for each algorithm, finally, summarizes current status and future development trends of meteorological observation datas prediction.