DOI: 10.1016/j.atmosres.2017.11.024
Scopus记录号: 2-s2.0-85044287291
论文题名: A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables
作者: Madonna E. ; Ginsbourger D. ; Martius O.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 203 起始页码: 261
结束页码: 274
语种: 英语
英文关键词: Hail
; Insurance data
; Inter-annual variability
; Regression models
Scopus关键词: Potential energy
; Precipitation (meteorology)
; Convective available potential energies
; Cross validation
; Environmental variables
; Interannual variability
; Intra-annual variability
; Long-term variability
; Poisson regression
; Regression model
; Regression analysis
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 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-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 trend is weaker in the other data sets. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108960
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
There are no files associated with this item.
作者单位: Oeschger Centre for Climate Change Research, Institute of Geography, University of Bern, Switzerland; Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Norway; Idiap Research Institute, Martigny, Switzerland; IMSV, Department of Mathematics and Statistics, University of Bern, Switzerland
Recommended Citation:
Madonna E.,Ginsbourger D.,Martius O.. A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables[J]. Atmospheric Research,2018-01-01,203