A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

Madonna, Erica; Ginsbourger, David; Martius, Olivia (2018). A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables. Atmospheric research, 203, pp. 261-274. Elsevier 10.1016/j.atmosres.2017.11.024

	Text 1-s2.0-S0169809517308232-main.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (5MB)
Preview	Text Poisson_regression.pdf - Accepted Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (3MB) | Preview

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April–September) in the period 2002–2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-toyear variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its interannual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Interest & Impact

Downloads

205 since deposited on 19 Mar 2018
20 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item