Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Article

Zhu, Yifan, Zhang, Sifan, Li, Yinan, Lu, Hao, Shi, Kaize and Niu, Zhendong. 2020. "Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace." Geoscience Data Journal. 7 (1), pp. 61-79. https://doi.org/10.1002/gdj3.85
Article Title

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

ERA Journal ID212611
Article CategoryArticle
AuthorsZhu, Yifan, Zhang, Sifan, Li, Yinan, Lu, Hao, Shi, Kaize and Niu, Zhendong
Journal TitleGeoscience Data Journal
Journal Citation7 (1), pp. 61-79
Number of Pages19
Year2020
PublisherJohn Wiley & Sons
Place of PublicationUnited Kingdom
ISSN2049-6060
Digital Object Identifier (DOI)https://doi.org/10.1002/gdj3.85
Web Address (URL)https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/gdj3.85
Abstract

Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary. Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological service. It was also evident that current researches are contributing towards a systemic and intelligent knowledge service to establish a better bridge among academic, industrial and individual community.

Keywordscrowdsourcing; data-driven; knowledge services; meteorological services; social space
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460806. Human-computer interaction
Byline AffiliationsBeijing Institute of Technology, China
Chinese Academy of Sciences, China
University of Pittsburgh, United States
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