Consequence forecasting: A rational framework for predicting the consequences of approaching storms

Gregow, 2017, Increasing large scale windstorm damage in Western, Central and Northern European forests, 1951–2010, Scientific Reports, 7, 46397, 10.1038/srep46397 Feser, 2015, Storminess over the North Atlantic and northwestern Europe—A review, Quarterly Journal of the Royal Meteorological Society, 141, 350, 10.1002/qj.2364 Donat, 2011, Reanalysis suggests long-term upward trends in European storminess since 1871, Geophysical Research Letters, 38, n/a, 10.1029/2011GL047995 Wang, 2006, Climatology and Changes of Extratropical Cyclone Activity: Comparison of ERA-40 with NCEP–NCAR Reanalysis for 1958–2001, Journal of Climate, 19, 3145, 10.1175/JCLI3781.1 Dawson, R.J.T., D.; Johns, D.; Gosling, S.; Chapman, L.; Darch, G.; Watson, G.; Powrie, W.; Bell, S.; Paulson, K.; Hughes, P.; and Wood, R. , UK Climate Change Risk Assessment Evidence Report: Chapter 4, Infrastructure, A.S.-C.o.t.C.o.C. Change, Editor. 2016: London. Atkins G, W.C., Hogarth R, Davies N and Norris E What’s Wrong with Infrastructure Decision Making? Conclusions from six UK case studies. 2017. DEFRA, Climate Resilient Infrastructure: Preparing for a Changing Climate. 2011. Serinaldi, 2015, Stationarity is undead: Uncertainty dominates the distribution of extremes, Advances in Water Resources, 77, 17, 10.1016/j.advwatres.2014.12.013 Shapland, M., Reducing the gap between cost estimates and outturns for major infrastructure projects and programmes. 2019, Institution of Civil Engineers: 1 Great George Street, Westminster, London, SW1P 3AA, UK. Aitsi-Selmi, 2016, Ensuring science is useful, usable and used in global disaster risk reduction and sustainable development: a view through the Sendai framework lens, Palgrave Communications, 2, 16016, 10.1057/palcomms.2016.16 Mechler, 2014, Managing unnatural disaster risk from climate extremes, Nature Climate Change, 4, 235, 10.1038/nclimate2137 Stern, 2013, Managing risk with climate vulnerability science, Nature Climate Change, 3, 607, 10.1038/nclimate1929 UNDRR, Sendai, Framework for Disaster Risk Reduction 2015–2030. 2015. Röösli, 2021, Towards operational impact forecasting of building damage from winter windstorms in Switzerland, Meteorological Applications, 28, 10.1002/met.2035 Welker, 2021, Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland. Nat. Hazards Earth Syst. Sci., 21, 279, 10.5194/nhess-21-279-2021 2005 Clark, 2002, The Use of Computer Modeling in Estimating and Managing Future Catastrophe Losses. The Geneva Papers on Risk and Insurance, Issues and Practice, 27, 181 Golnaraghi, M., et al., Managing Physical Climate Risk: Leveraging Innovations in Catastrophe Risk Modelling, T.G. Association, Editor. 2018, The Geneva Association: Zurich. Aznar-Siguan, 2019, CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085, 10.5194/gmd-12-3085-2019 Rinaldi, 2001, Identifying, understanding, and analyzing critical infrastructure interdependencies, IEEE Control Systems Magazine, 21, 11, 10.1109/37.969131 Dueñas-Osorio, 2009, Cascading failures in complex infrastructure systems, Structural Safety, 31, 157, 10.1016/j.strusafe.2008.06.007 O’ROURKE, 2007, Critical Infrastructure, Interdependencies, and Resilience, the bridge, 37 Bruneau, 2003, A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities, Earthquake Spectra, 19, 733, 10.1193/1.1623497 Clark, 2016, Convection-permitting models: a step-change in rainfall forecasting, Meteorological Applications, 23, 165, 10.1002/met.1538 Office, M. Numerical weather prediction models. 2010 [cited 2020 05 March 2020]; Available from: https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting. Tang, 2013, The benefits of the Met Office variable resolution NWP model for forecasting convection, Meteorological Applications, 20, 417, 10.1002/met.1300 Dunn, 2018, Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database, Natural Hazards Review, 19, 04017019, 10.1061/(ASCE)NH.1527-6996.0000267 Jägemann, 2013, Decarbonizing Europe's power sector by 2050 — Analyzing the economic implications of alternative decarbonization pathways, Energy Economics, 40, 622, 10.1016/j.eneco.2013.08.019 Sachs, 2016, Pathways to zero emissions, Nature Geoscience, 9, 799, 10.1038/ngeo2826 McColl, 2012, Assessing the potential impact of climate change on the UK’s electricity network, Climatic Change, 115, 821, 10.1007/s10584-012-0469-6 Panteli, 2017, Power System Resilience to Extreme Weather: Fragility Modeling, Probabilistic Impact Assessment, and Adaptation Measures, IEEE Transactions on Power Systems, 32, 3747, 10.1109/TPWRS.2016.2641463 Billinton, 1968, Transmission System Reliability Evaluation Using Markov Processes, IEEE Transactions on Power Apparatus and Systems, PAS-87, 538, 10.1109/TPAS.1968.292051 Bhuiyan, 1994, Inclusion of weather effects in composite system reliability evaluation using sequential simulation, IEE Proceedings – Generation, Transmission and Distribution, 141, 575, 10.1049/ip-gtd:19941573 Alvehag, 2011, A Reliability Model for Distribution Systems Incorporating Seasonal Variations in Severe Weather, IEEE Transactions on Power Delivery, 26, 910, 10.1109/TPWRD.2010.2090363 Zhang, 2014, Risk analysis for distribution systems in the Northeast U.S. under wind storms Fu, 2018, Integrated Approach to Assess the Resilience of Future Electricity Infrastructure Networks to Climate Hazards, IEEE Systems Journal, 12, 3169, 10.1109/JSYST.2017.2700791 Dunn, 2017, Hazard tolerance of spatially distributed complex networks, Reliability Engineering & System Safety, 157, 1, 10.1016/j.ress.2016.08.010 Association, E.N. NaFIRS. Available from: https://www.energynetworks.org/electricity/engineering/nafirs.html. HAZUS. Hazus — MH 2.1, hurricane model, technical manual. FEMA, Editor. 2015. FEMA: Washington, DC. porter, k. A Beginner’s Guide to Fragility, Vulnerability, and Risk. U.o.C. Boulder, Editor. 2020. Shafieezadeh, 2014, Age-Dependent Fragility Models of Utility Wood Poles in Power Distribution Networks Against Extreme Wind Hazards, IEEE Transactions on Power Delivery, 29, 131, 10.1109/TPWRD.2013.2281265 Efron, 1981, The Jackknife Estimate of Variance, The Annals of Statistics, 9, 586, 10.1214/aos/1176345462 Elizabeth Kendon, G.F., James Murphy, Steven Chan, Robin Clark, Glen Harris, Adrian Lock, Jason Lowe, Gill Martin, Jenny Pirret, Nigel Roberts, Mike Sanderson, Simon Tucker, UKCP Convection-permitting model projections: Science report. 2019, Met Office: Reading.