Preprints
https://doi.org/10.5194/nhess-2020-413
https://doi.org/10.5194/nhess-2020-413

  05 Jan 2021

05 Jan 2021

Review status: this preprint is currently under review for the journal NHESS.

Social sensing of high-impact rainfall events worldwide: A benchmark comparison against manually curated impact observations

Michelle D. Spruce1, Rudy Arthur1, Joanne Robbins2, and Hywel T. P. Williams1 Michelle D. Spruce et al.
  • 1College of Engineering, Maths and Physical Sciences, University of Exeter, Exeter, EX4 4SB, UK
  • 2Met Office, Exeter, EX1 3PB, UK

Abstract. Impact-based weather forecasting and warnings create the need for reliable sources of impact data to generate and evaluate models and forecasts. Here we compare outputs from social sensing – analysis of unsolicited social media data, in this case from Twitter – against a manually curated impact database created by the UK Met Office. The study focuses on high-impact rainfall events across the globe between January–June 2017.

Social sensing successfully identifies most high-impact rainfall events present in the manually curated database, with an overall accuracy of 95 %. Performance varies by location, with some areas of the world achieving 100 % accuracy. Performance is best for severe events and events in English-speaking countries, but good performance is also seen for less severe events and in countries speaking other languages. Social sensing detects a number of additional high-impact rainfall events that are not recorded in the Met Office database, suggesting that social sensing can usefully extend current impact data collection methods and offer more complete coverage.

This work provides a novel methodology for the curation of impact data that can be used to support the evaluation of impact-based weather forecasts.

Michelle D. Spruce et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Michelle D. Spruce et al.

Michelle D. Spruce et al.

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