Abstract
With the development of the meteorological IoT (Internet of Things) and meteorological sensing network, the collected multi-source meteorological data have the characteristics of large amount of information, multidimensional and high accuracy. Cloud computing technology has been applied to the storage and service of meteorological big data. Although the constant evolution of big data storage technology is improving the storage and access of meteorological data, storage and service efficiency is still far from meeting multi-source big data requirements. Traditional methods have been used for the storage and service of meteorological data, and a number of problems still persist, such as a lack of unified storage structure, poor scalability, and poor service performance. In this study, an efficient storage and service method for multidimensional meteorological data is designed based on NoSQL big data storage technology and the multidimensional characteristics of meteorological data. In the process of data storage, multidimensional block compression technology and data structures are applied to store and transmit meteorological data. In service, heterogeneous NoSQL common components are designed to improve the heterogeneity of the NoSQL database. The results show that the proposed method has good storage transmission efficiency and versatility, and can effectively improve the efficiency of meteorological data storage and service in meteorological applications.
Highlights
The application and service of meteorology become increasingly common in human daily lives, and meteorological information has become crucial in various aspects of life [1]
4.3.1 Evaluation of the storage performance Storage performance of meteorological data is a key factor to determine the performance of meteorological services, we evaluate on this value to discuss the storage performance achieved by Radar Echo Product (REP), QPF, and FY4A Sate Product (FSP) with different datasets
4.3.2 Evaluation of the service performance In order to validate the proposed meteorological data service method, the query time of data API on the meteorological cloud application using Numerical Forecast Products (NFP), Meteorological Grid Product (MGP), and FSP using three different types of datasets are listed in Table 4, including any latitude and longitude grid time sequences data, hourly automatic station precipitation grid data, and satellite raster data
Summary
The application and service of meteorology become increasingly common in human daily lives, and meteorological information has become crucial in various aspects of life [1]. Meteorological data collected mainly by meteorological IoT (Internet of Things) and meteorological sensor networks, including temperature, precipitation, pressure, and radar echo, are the basic data of meteorological application service. Meteorological data are mainly composed of a variety of data structures, including structured, semi-structured, and unstructured data [4]. Traditional relational database management systems (RDBMS) have been used for the analysis, processing, and storage requirements of structured data, such as automatic weather station data and station forecasting. The semi-structured and unstructured data are mainly stored in file management systems in various formats, such as NetCDF (Network Common Data Form) [5], GRIB (General Regularly-distributed Information in Binary form), BUFR
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