Social sensing of high-impact rainfall events worldwide: a benchmark comparison against manually curated impact observations
Social sensing of high-impact rainfall events worldwide: a benchmark comparison against manually curated impact observations
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DOI：
10.5194/nhess-21-2407-2021
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发表时间：
2021-08
影响因子：
4.6
通讯作者：
M. Spruce;Rudy Arthur;J. Robbins;Hywel T. P. Williams
中科院分区：
文献类型：
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作者：
M. Spruce;Rudy Arthur;J. Robbins;Hywel T. P. Williams
抽象的。基于影响的天气预报和警报需要可靠的影响数据来源，以生成和评估模型和预报。在这里，我们比较了社交传感的输出-分析未经请求的社交媒体数据，在这种情况下，从Twitter -与手动策划的影响数据库创建的气象局。该研究重点关注2017年1月至6月期间地球仪的高影响降雨事件。社会感知成功地识别了人工管理的数据库中存在的大多数高影响力的降雨事件，总体准确率为95%。性能因地点而异，世界上某些地区可以达到100%的准确率。对于严重事件和英语国家的事件，性能最好，但对于不太严重的事件和其他语言的国家，也可以看到良好的性能。社会感知检测到一些额外的高影响降雨事件，没有记录在气象局数据库中，这表明社会感知可以有效地扩展当前的影响数据收集方法，并提供更完整的覆盖范围。这项工作提供了一种新的方法来管理影响数据，可用于支持基于影响的天气预报的评估。
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 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.