Interdisciplinary Assessment of Tanzanian Severe Weather Forecasts: Linking Verification and User Evaluation

Abstract

While there is substantial research on weather forecast verification and on user evaluation of forecasts, these approaches are often treated separately. Linking them can enhance forecast credibility, increase trust, and support more effective decision‐making. This study applies a mixed‐methods approach to evaluate severe weather forecasts produced by the Tanzania Meteorological Authority (TMA), from forecast generation to end‐user application. Quantitative verification used satellite‐derived rainfall estimates from the Global Precipitation Measurement (GPM) mission to assess forecast skill across lead times and rainfall thresholds. Results show forecast accuracy above 0.80, with probability of detection (POD) of 0.6–0.9 across thresholds, but also a high false alarm ratio (0.4–0.7) and evidence of overforecasting, particularly at longer lead times. Reliance on a single national 50 mm/24 h threshold was a key driver of false alarms and may not reflect area‐specific risks. Qualitative evaluation, based on interviews and participatory observations with disaster risk reduction stakeholders, highlighted that severe weather forecasts reached institutional actors such as the Dar es Salaam Emergency Response Team and the Tanzania Red Cross Society in a timely manner. However, challenges remain in ensuring last mile delivery to communities. Interviewees emphasized the importance of SMS, local radio, trusted intermediaries, and simpler language to improve understanding and uptake. The study recommends revising rainfall thresholds by regions, integrating disaster impact data into verification, and institutionalizing user feedback forums for severe weather forecasts. Limitations include reliance on 1 year of warnings, the absence of geo‐referenced impact datasets, and constraints in polygon vectorization. By combining forecast verification with user evaluation, this study provides a comprehensive assessment of forecast performance and its application in disaster risk reduction, with lessons relevant beyond Tanzania.