With the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

The ever‐increasing volume of data and the higher demand for real‐time services in the meteorological industry have made the relevant departments face some problems such as how to process data efficiently and deliver services instantly. Hence, by intervening in cloud computing and building meteorological cloud platform, the data are effectively managed and processed which means reliable services could be satisfied. However, a large amount of complex heterogeneous data generated by meteorological stations remote from the cloud center is sent to the cloud for processing to impose bandwidth pressure on the cloud. It will bring substantial access delays in case of frequent access. Meanwhile, the data in the public cloud will encounter security problems for the sensitivity. Therefore, appropriate assistive techniques such as edge computing are urgent to be chosen to solve the aforementioned problems. The collaborative work of edge computing and cloud makes meteorological data sent to the edge nodes for processing, which effectively cuts down the bandwidth pressure, reduces the transmission delay, and advances data security of the cloud. In this paper, we first describe the architecture, classification, and definition of meteorological services in the cloud. Then, the resource management of meteorological services in edge computing and cloud computing is summarized respectively. Furthermore, meteorological applications in cloud computing is introduced. Besides, we explore the future work of meteorological service provisioning in collaborative edge computing and cloud.