Insights from the Objective Verification of Weather Warnings

Severe weather can cause considerable damage to nature and infrastructure and may endanger people. Timely and accurate warnings are crucial to protect the population. Considerable efforts are being taken at MeteoSwiss to improve weather warnings. The evaluation of the quality of weather warnings forms an important part of the ongoing developments. 

Currently, weather warnings at MeteoSwiss are verified manually by a team of forecasters. This method effectively leverages expert knowledge and has proven successful over the years. However, manual verification also comes with a number of limitations: it is time-consuming, subjective, and may lead to inconsistencies. Additionally, due to resource constraints the granularity of the results and the ability to produce long-term statistics is restricted. All of these limitations hamper the identification of systematic biases and opportunities for improvement. 

To overcome these limitations, we developed an objective and automated verification system aimed at enhancing the efficiency, consistency, and detail of warning evaluations. This approach provides a formalized framework for verification, reduces human bias, incorporates a broader range of observational data, and supports the generation of a comprehensive set of verification metrics. Moreover, automated verification facilitates retrospective analyses, making it easier to uncover long-term trends, recurring patterns, and potential weaknesses in the warning system.  

In this presentation, we share results from our ongoing work, including case study verifications and comparisons with traditional manual assessments. We show that the performance of weather warnings at MeteoSwiss has significantly improved over the past decade. This positive trend is in line with advances in weather forecasting capabilities. Furthermore, we illustrate how objective verification with detailed diagnostics can be used to further improve those warnings and minimize adverse effects of severe weather.