Articles | Volume 18, issue 6
https://doi.org/10.5194/nhess-18-1617-2018
https://doi.org/10.5194/nhess-18-1617-2018
Research article
 | 
13 Jun 2018
Research article |  | 13 Jun 2018

A statistical model to estimate the local vulnerability to severe weather

Tobias Pardowitz

Related authors

An analysis of uncertainties and skill in forecasts of winter storm losses
Tobias Pardowitz, Robert Osinski, Tim Kruschke, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 16, 2391–2402, https://doi.org/10.5194/nhess-16-2391-2016,https://doi.org/10.5194/nhess-16-2391-2016, 2016
Short summary

Related subject area

Atmospheric, Meteorological and Climatological Hazards
Global estimates of 100-year return values of daily precipitation from ensemble weather prediction data
Florian Ruff and Stephan Pfahl
Nat. Hazards Earth Syst. Sci., 24, 2939–2952, https://doi.org/10.5194/nhess-24-2939-2024,https://doi.org/10.5194/nhess-24-2939-2024, 2024
Short summary
Exploring the sensitivity of extreme event attribution of two recent extreme weather events in Sweden using long-running meteorological observations
Erik Holmgren and Erik Kjellström
Nat. Hazards Earth Syst. Sci., 24, 2875–2893, https://doi.org/10.5194/nhess-24-2875-2024,https://doi.org/10.5194/nhess-24-2875-2024, 2024
Short summary
Probabilistic short-range forecasts of high-precipitation events: optimal decision thresholds and predictability limits
François Bouttier and Hugo Marchal
Nat. Hazards Earth Syst. Sci., 24, 2793–2816, https://doi.org/10.5194/nhess-24-2793-2024,https://doi.org/10.5194/nhess-24-2793-2024, 2024
Short summary
Surprise floods: the role of our imagination in preparing for disasters
Joy Ommer, Jessica Neumann, Milan Kalas, Sophie Blackburn, and Hannah L. Cloke
Nat. Hazards Earth Syst. Sci., 24, 2633–2646, https://doi.org/10.5194/nhess-24-2633-2024,https://doi.org/10.5194/nhess-24-2633-2024, 2024
Short summary
Modelling crop hail damage footprints with single-polarization radar: the roles of spatial resolution, hail intensity, and cropland density
Raphael Portmann, Timo Schmid, Leonie Villiger, David N. Bresch, and Pierluigi Calanca
Nat. Hazards Earth Syst. Sci., 24, 2541–2558, https://doi.org/10.5194/nhess-24-2541-2024,https://doi.org/10.5194/nhess-24-2541-2024, 2024
Short summary

Cited articles

Akaike, H.: Prediction and entropy, in: A Celebration of Statistics, edited by: Atkinson, A. C. and Fienberg, S. E., Springer, 1–24, 1985.
Aller, D. and Kozlowski, E.: Unwetter und ihre Relevanz für die Versicherungswirtschaft (Thunderstorms and their implications for the insurance industry) Promet, 34, 10–20, 2008.
Apel, H., Aronica, G. T., Kreibich, H., and Thieken, A. H.: Flood risk analyses – how detailed do we need to be?, Nat. Hazards, 49, 79–98, 2009.
Bassil, K. L., Cole, D. C., Moineddin, R., Lou, W., Craig, A. M., Schwartz, B., and Rea, E.: The relationship between temperature and ambulance response calls for heat-related illness in Toronto, Ontario, 2005, J. Epidemiol. Commun. H., 65, 829–831, 2011.
Belsley, D. A., Kuh, E., and Welsch, R. E.: Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, New York, Wiley, ISBN:0-471-05856-4, 1980.
Download
Short summary
The paper presents a statistical analysis of socioeconomic factors influencing vulnerability and exposure to severe weather. By means of statistical modelling, the risks of weather impacts can be predicted at very high spatial resolutions. Such models can serve as a basis for a broad range of tools or applications in emergency management and planning and thus might help to enhance resilience to severe weather.
Altmetrics
Final-revised paper
Preprint