Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/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
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
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 |
引用统计 | |
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 |
推荐引用方式 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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