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

Kasper S Hintz,Mohamed Dahoui,Lars Isaksen,Valeria Siirand,Gwenaelle Le Bloa,Henrik Vedel,Marion Lavanant,Emilie Mallet,Katharine O'Boyle,Dick Blaauboer,Sarah L Dance,Callie Mcnicholas,Ivar Ansper,Xiaohua Yang,Alexander Cress,Eigil Kaas,Bent Hansen Sass ,Jeanette Onvlee-Hooimeijer ,Juhana Hyrkkänen ,Saja Al-Ali ,Ulrik Smith Korsholm ,R J D'arcy ,John C Waller ,Matthew R Clark

doi:10.1002/asl.921

Abstract

AbstractIn 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.

Highlights

Within the atmospheric sciences, “crowdsourced” data is a relatively new term
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
While the term crowdsourcing was initially defined by Howe (2006) as outsourcing an act to the general public, this definition is no longer restricted to traditional tasks being outsourced

Summary

Introduction

Within the atmospheric sciences, “crowdsourced” data is a relatively new term. While the term crowdsourcing was initially defined by Howe (2006) as outsourcing an act to the general public, this definition is no longer restricted to traditional tasks being outsourced. Today, crowdsourcing is more than outsourcing data collection to the general public. Instead, crowdsourcing embraces new data sources, data storage, quality control and utilisation, which requires standard methods and a common terminology. Direct and indirect observations from non-conventional sources are being investigated for use in the atmospheric sciences. Examples of data sources include Personal Weather Stations (PWSs) (Bell et al, 2013, 2015; Clark et al, 2018), smartphones (Kim et al, 2015; McNicholas and Mass, 2018; Price et al, 2018; Hintz et al, 2019), vehicles (Anderson et al, 2012; Mahoney and O'Sullivan, 2013) and communication networks (Zinevich et al, 2009)

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