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Big Data Analytics in Weather Forecasting: A Systematic Review
Archives of Computational Methods in Engineering ( IF 9.7 ) Pub Date : 2021-06-28 , DOI: 10.1007/s11831-021-09616-4
Marzieh Fathi , Mostafa Haghi Kashani , Seyed Mahdi Jameii , Ebrahim Mahdipour

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.



中文翻译:

天气预报中的大数据分析:系统回顾

天气预报作为人们日常生活中必不可少的重要程序,它评估当前大气状况发生的变化。大数据分析是分析大数据以提取可以产生更好结果的隐藏模式和适用信息的过程。如今,社会上很多人都对大数据感兴趣,也不排除气象所。因此,大数据分析将在天气预报中提供更好的结果,并将帮助预报员更准确地预测天气。为了实现这一目标并推荐有利的解决方案,已经提出了几种大数据技术和技术来管理和分析来自不同资源的大量天气数据。通过在天气预报中使用大数据分析,可以解决与传统数据管理技术和技术相关的挑战。本文为天气预报中的大数据分析方法(2014 年至 2020 年 8 月发布)提供了一种系统的文献综述方法。当前审查论文的可行分类被提出为基于技术、基于技术和混合的方法。此外,本文在准确性、可扩展性、执行时间和其他服务质量因素方面对上述类别进行了比较。提取了每篇论文的算法类型、测量环境、建模工具以及优缺点。此外,还讨论了未决问题和未来趋势。本文为天气预报中的大数据分析方法(2014 年至 2020 年 8 月发布)提供了一种系统的文献综述方法。当前审查论文的可行分类被提出为基于技术、基于技术和混合的方法。此外,本文在准确性、可扩展性、执行时间和其他服务质量因素方面对上述类别进行了比较。提取了每篇论文的算法类型、测量环境、建模工具以及优缺点。此外,还讨论了未决问题和未来趋势。本文为天气预报中的大数据分析方法(2014 年至 2020 年 8 月发布)提供了一种系统的文献综述方法。当前审查论文的可行分类被提出为基于技术、基于技术和混合的方法。此外,本文在准确性、可扩展性、执行时间和其他服务质量因素方面对上述类别进行了比较。提取了每篇论文的算法类型、测量环境、建模工具以及优缺点。此外,还讨论了未决问题和未来趋势。本文在准确性、可扩展性、执行时间和其他服务质量因素方面对上述类别进行了比较。提取了每篇论文的算法类型、测量环境、建模工具以及优缺点。此外,还讨论了未决问题和未来趋势。本文在准确性、可扩展性、执行时间和其他服务质量因素方面对上述类别进行了比较。提取了每篇论文的算法类型、测量环境、建模工具以及优缺点。此外,还讨论了未决问题和未来趋势。

更新日期:2021-06-29
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