Big Data Analytics in Weather Forecasting: A Systematic Review

• Published in: Archives of computational methods in engineering Vol. 29; no. 2; pp. 1247 - 1275
• Main Authors: Fathi, Marzieh, Haghi Kashani, Mostafa, Jameii, Seyed Mahdi, Mahdipour, Ebrahim
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-03-2022; Springer Nature B.V
• Subjects: Algorithms; Atmospheric models; Big Data; Data analysis; Data management; Engineering; Environment models; Literature reviews; Mathematical and Computational Engineering; Meteorological data; Review Article; Taxonomy; Weather forecasting
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-021-09616-4

Weather forecasting, as an important and indispensable procedure in people’s daily lives, evaluates the alteration happening in the current condition of the atmosphere. Big data analytics is the process of analyzing big data to extract the concealed patterns and applicable information that can yield better results. Nowadays, several parts of society are interested in big data, and the meteorological institute is not excluded. Therefore, big data analytics will give better results in weather forecasting and will help forecasters to forecast weather more accurately. In order to achieve this goal and to recommend favorable solutions, several big data techniques and technologies have been suggested to manage and analyze the huge volume of weather data from different resources. By employing big data analytics in weather forecasting, the challenges related to traditional data management techniques and technology can be solved. This paper tenders a systematic literature review method for big data analytic approaches in weather forecasting (published between 2014 and August 2020). A feasible taxonomy of the current reviewed papers is proposed as technique-based, technology-based, and hybrid approaches. Moreover, this paper presents a comparison of the aforementioned categories regarding accuracy, scalability, execution time, and other Quality of Service factors. The types of algorithms, measurement environments, modeling tools, and the advantages and disadvantages per paper are extracted. In addition, open issues and future trends are debated.