Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–5 December 2018

Atmospheric Science Letters

Bent Sass¹  Kasper S. Hintz¹  Henrik Vedel¹  Xiaohua Yang¹  Ulrik S. Korsholm¹  Callie McNicholas²  Alexander Cress³  Ivar Ansper⁴  Valeria Siirand⁴  Mohamed Dahoui⁵  Lars Isaksen⁵  Juhana Hyrkkanen⁶  Katharine O'Boyle⁷  Matthew Clark⁷  Rónán Darcy⁸  Emilie Mallet⁹  Saja Al‐Ali⁹  Marion Lavanant⁹  Gwenaelle Le Bloa⁹  Jeanette Onvlee‐Hooimeijer^1,10  Dick Blaauboer^1,10  Eigil Kaas^1,11  Joanne A. Waller^1,12  Sarah L. Dance^1,12 

[1] Danish Meteorological Institute Copenhagen Denmark;Department of Atmospheric Sciences University of Washington Seattle Washington;Deutsche Wetterdienst Offenbach Germany;Estonian Environment Agency Tallinn Estonia;European Centre for Medium‐Range Weather Forecasts Reading UK;Finnish Meteorological Institute Helsinki Finland;Met Office Exeter UK;Met Èireann Dublin Ireland;Météo‐France Paris France;Royal Netherlands Meteorological Institute De Bilt the Netherlands;University of Copenhagen Niels Bohr Institute Copenhagen Denmark;University of Reading Reading UK;

关键词: citizen science;    crowdsourcing;    data collection;    opportunistic data;    quality control;    third‐party data;   

DOI  :  10.1002/asl.921

来源: DOAJ

【 摘 要 】

Abstract In December 2018, the Danish Meteorological Institute organised an international meeting on the subject of crowdsourced data in numerical weather prediction (NWP) and weather forecasting. The meeting, spanning 2 days, gathered experts on crowdsourced data from both meteorological institutes and universities from Europe and the United States. Scientific presentations highlighted a vast array of possibilities and progress being made globally. Subjects include data from vehicles, smartphones, and private weather stations. Two groups were created to discuss open questions regarding the collection and use of crowdsourced data from different observing platforms. Common challenges were identified and potential solutions were discussed. While most of the work presented was preliminary, the results shared suggested that crowdsourced observations have the potential to enhance NWP. A common platform for sharing expertise, data, and results would help crowdsourced data realise this potential.

【 授权许可】

Unknown