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 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.

中文翻译：

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全球高影响降雨事件的社会感知：与人工策划的影响​​观测的基准比较

基于影响的天气预报和预警需要可靠的影响数据来源来生成和评估模型和预测。在这里，我们将社会感知的输出——分析未经请求的社交媒体数据，在这种情况下来自 Twitter——与由气象局创建的手动策划的影响​​数据库进行比较。该研究侧重于 2017 年 1 月至 6 月期间全球范围内的高影响降雨事件。社会感知成功识别了手动策划的数据库中存在的大多数高影响降雨事件，总体准确度为 95%。性能因位置而异，世界上某些地区的准确度为 100%。在严重事件和英语国家的事件中表现最好，但在不太严重的事件和讲其他语言的国家中也可以看到良好的表现。