Leveraging crowd-sourced impact data with ML to improve severe weather warnings at MeteoSwiss

MeteoSwiss disseminates its extreme weather warnings through various channels, one of the most important being the MeteoSwiss smart phone app. This app is the most widely used platform for informing the Swiss population, with over 4.8 million installations and a daily user base ranging from 700,000 to 1.8 million people. It provides weather forecasts and displays any active severe weather warnings. When enabled, the app also sends push notifications to alert users about such warnings.

In addition, the MeteoSwiss app allows users to submit their own weather observations, known as "Meteo reports." These crowd-sourced reports can include general weather conditions as well as specific impacts such as strong winds, hail, or heatwaves. Users can contribute their observations in three ways: (i) by uploading situational photos, (ii) by selecting predefined keywords to describe the intensity of the event, and (iii) by submitting free-text descriptions.

As MeteoSwiss advances toward impact-based warnings, these user-generated reports play a key role in improving the understanding of how different weather events affect people and infrastructure. By analyzing the relationship between past warnings and reported impacts, MeteoSwiss can refine its algorithms to better predict the consequences of future events based on physical warning parameters. A data driven model essentially predicts a selection of most likely outcomes in terms of user report based on properties of the issued warning. These include keywords describing the impact and draws from submitted photos deemed representative. This improved understanding supports the development of more accurate extreme weather warnings, ultimately enhancing preparedness and response planning.