GSTDTAP  > 地球科学
DOI10.1016/j.atmosres.2017.11.024
A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables
Madonna, Erica1,2,3; Ginsbourger, David4,5; Martius, Olivia1
2018-05-01
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
EISSN1873-2895
出版年2018
卷号203页码:261-274
文章类型Article
语种英语
国家Switzerland; Norway
英文摘要

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 re-analysis. 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-to-year 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 inter annual 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 tend is weaker in the other data sets.


英文关键词Hail Regression models Insurance data Inter-annual variability
领域地球科学
收录类别SCI-E ; SSCI
WOS记录号WOS:000426226400023
WOS关键词SIMULATED CONVECTIVE STORMS ; SEVERE THUNDERSTORM ; THERMODYNAMIC CONDITIONS ; SPATIAL-DISTRIBUTION ; SOUTHWESTERN FRANCE ; DEEP CONVECTION ; COUNT DATA ; PARAMETERS ; HAILSTORMS ; EUROPE
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/38373
专题地球科学
作者单位1.Univ Bern, Inst Geog, Oeschger Ctr Climate Change Res, Bern, Switzerland;
2.Univ Bergen, Geophys Inst, Bergen, Norway;
3.Bjerknes Ctr Climate Res, Bergen, Norway;
4.Idiap Res Inst, Martigny, Switzerland;
5.Univ Bern, Dept Math & Stat, IMSV, Bern, Switzerland
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GB/T 7714
Madonna, Erica,Ginsbourger, David,Martius, Olivia. A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables[J]. ATMOSPHERIC RESEARCH,2018,203:261-274.
APA 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,203,261-274.
MLA Madonna, Erica,et al."A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables".ATMOSPHERIC RESEARCH 203(2018):261-274.
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