Reconstructing hail days in Switzerland with statistical models (1959–2022)

Abstract

Abstract. Hail is one of the costliest natural hazards in Switzerland and causes extensive damage to agriculture, cars, and infrastructure each year. In a warming climate, hail frequency and its patterns of occurrence are expected to change, which is why understanding the long-term variability and its drivers is essential. This study presents new multidecadal daily hail time series for northern and southern Switzerland from 1959 to 2022. Daily radar hail proxies and environmental predictor variables from ERA5 reanalysis are used to build an ensemble statistical model for predicting past hail occurrence. Hail days are identified from operational radar-derived probability of hail (POH) data for two study domains, the north and south of the Swiss Alps. We use data from 2002 to 2022 during the convective season from April to September. A day is defined as a hail day when POH surpasses 80 % for a minimum footprint area of the two domains. Separate logistic regression and logistic generalized additive models (GAMs) are built for each domain and combined in an ensemble prediction to reconstruct the final time series. Overall, the models are able to describe the observed time series well. Historical hail reports are used for comparing years with the most and least hail days. For the northern and southern domains, the time series both show a significant positive trend in yearly aggregated hail days from 1959 to 2022. The trend is still positive and significant when considering only the period of 1979–2022. In all models, the trends are driven by moisture and instability predictors. The last 2 decades show a considerable increase in hail days, which is the strongest in May and June. The seasonal cycle has not shifted systematically across decades. This time series allows us to study the local and remote drivers of the interannual variability and seasonality of Swiss hail occurrence.