Project highlights

  • Opportunity to drive science forward for cyclonic windstorms, e.g. tropical and extra-tropical cyclones, one of the biggest and most disruptive hazards to vulnerable regions in tropics, sub-tropics, and mid-latitudes.
  • Learn data science skills and use cutting-edge seasonal forecasts (e.g., SEAS5; Artificial Intelligence (AI)).
  • Engagement with (re)insurance sector, thus results with real world impact and enhanced post-PhD job prospects. A placement/internship is envisaged.


Severe tropical cyclones (TCs) are a major threat to societies and cause significant loss all over the tropical and sub-tropical regions. For example, China is affected by on average seven tropical cyclones (with Typhoon strength) making landfall each year, resulting in annual damages of about US$5.6 billion. Severe damages are especially documented on the local scale, impacting on local property damages of up to 50% and reductions in the local economy by about 20% for the respective year. Total net economic losses are estimated to be in the range of US$28 billion for the recent climate period (1992-2010, Elliott et al., 2015). One fundamental question is the potential predictability of these rare severe events in different time scales. Latest research showed significant and partly usable skill of TC frequencies on the seasonal time scale (Vitart and Stockdale, 2001; on specific regional scales: Vecchi et al., 2014); on landfall predictions (Camp et al., 2015), but varying between ocean basins. Specific large-scale factors (e.g. like ENSO) are related to TC occurrence variability and do also show seasonal prediction skill.

The project will address this topic by investigating the following research questions:

  • Which meteorological factors (e.g., ENSO, West Pacific High) cause predictability of estimates of loss in the upcoming season?
  • When should these measures have attention paid to them? This will develop a growing understanding of conditions in which skilful predictions are possible.
  • For which geographical regions are skilful prediction most likely?
  • How is predictability explained in terms of physical processes? This underpinning understanding is essential to give comfort to decision makers, and it gives the basis for insights into another key consideration i.e. what is the uncertainty in the predictions?

These will feed into practical considerations:

  • In which years, additional information about the season ahead would have made most difference to reinsurance decisions?
  • Should (re)insurers align their annual renewals to immediately prior to a season (e.g. Oct-Mar) such that the information available to them is maximized?



University of Birmingham


  • Climate and Environmental Sustainability


Project investigator

  •  Prof GC Leckebusch, University of Birmingham



  •  Dr JK Hillier, Loughborough University
  • Dickie Whitaker, Lighthill Risk Network Ltd (CEO)

How to apply


A core of the work is low risk, but scope exists for a student to innovate and excel. Elements of the proposed analysis are:

  • Literature review on factors steering TC frequency variability and forecast skill
  • Use of historical SEAS5 seasonal forecast data (ECMWF hindcasts).
  • Hypothesis driven investigations of observable quantities (e.g. storm counts), and the physical processes driving predictability. Methods (e.g. storm track detection) will be selected as appropriate.
  • Application and analysis of novel Artificial Intelligence (AI) tools in climate science (e.g., PCMCI; Runge et al., 2019) especially to multi-member ensemble hindcasts for the detection of causal links for predictability
  • Review, incl. qualitative, of decision making in last 10-15yrs within one or several (re)insurers to understand related processes. To include a focus on any differences if the seasonal forecasts now available, would have been used in the past.
  • Construction and application of a ‘toy’ illustrative decision-making tool to assess qualitatively and quantitatively any impact (e.g. financial) of additional information about the season ahead.


Training and skills

Specifically for this project, the PhD student will gain state-of-the-art data science skills applicable to both meteorological data and financial losses; such large datasets are sometimes referred to as ‘Big Data’. Training will be provided in meteorological data analysis techniques, approaches used in atmospheric science, and suitable statistical methods (e.g., in R). To gain on-the-ground experience of (re)insurance, i.e. the student will have the opportunity to undertake placement(s) in markets such as London and attend industry conferences (e.g., Impact Forecasting).  Training will also be available in catastrophe model design and use, with a focus on flooding and wind models.

Partners and collaboration

RE-STORM will investigate which weather/climatic processes cause predictability that matters in (re)insurance decisions regarding cyclonic windstorms.

This project is co-designed with and supported by the Lighthill Risk Network, a Level-1 (i.e. top tier) CENTA partner, who will supply both (i) expert knowledge of the (re)insurance sector and (ii) supervisory input into the project. Via this partner, we will secure further interest and input from additional interested relevant partners (e.g., Lloyd’s of London, Aon) from the insurance industry. This will be further discussed and clarified once the project has started, depending on the skill set of the applicant.

Further details

For information about this project, please contact Prof GC Leckebusch (

Applications need to be submitted via the University of Birmingham postgraduate portal,, by midnight 11.01.2021. Please first check whether the primary supervisor is within Geography, Earth and Environmental Sciences, or in Biosciences, and click on the corresponding PhD program on the application page.

This application should include

  • a brief cover letter, CV, and the contact details for at least two referees
  • a CENTA application form
  • the supervisor and title of the project you are applying for under the Research Information section of the application form.

Referee’s will be invited to submit their references once you submit your application, but we strongly encourage applicants to ensure referees are aware of your submission and expecting a reference request from us. Students are also encouraged to visit and explore the additional information available on the CENTA website.


Possible timeline

Year 1

The student will familiarise themselves with state-of-the-art seasonal forecasting, including e.g., SEAS5 or ECMWF hindcasts and their numerical interrogation, with the aim of identifying candidate metrics and physical processes with promise to be skilful predictors for further investigation.  In parallel a review, perhaps qualitative, of decision making in last 10-15 ETC seasons within one or several (re)insurers will be undertaken.

Year 2

Hypothesis driven investigations of observable quantities (e.g. storm counts), and the physical processes driving any predictability. Application of AI tools in multi-member ensembles.

Year 3

Whilst continuing to research atmospheric science, the main addition this year will be to construct and running of a ‘toy’ illustrative decision-making tool so that the impact (e.g. financial) of additional information about the season ahead can be quantitatively commented upon.

Depending upon progress, the scope of the project may expand to include (i) correlations between flooding and wind damage or (ii) the decline in European storminess since the year 2000, and could this have been noticed coming with the hindcasts.  The (re)insurance industry is keen to understand the answers to both of these.


Further reading

Befort, D.J., Wild, S., Knight, J.R., Lockwood, J.F., Thornton, H.E., Hermanson, L., Bett, P.E., Weisheimer, A., Leckebusch, G.C., 2018. Seasonal Forecast Skill for Extra-tropical Cyclones and Windstorms. Quart J R. Meteorol Soc., 145, 92–104.

Befort, D.J., T. Kruschke, and G.C. Leckebusch, 2020: Objective Identification of Potentially Damaging Tropical Cyclone over the Western North Pacific. Environ. Res. Commun., 2(3), 031005.

Camp, J., Roberts, M., MacLachlan, C., Wallace, E., Hermanson, L., Brookshaw, A., Arribas, A. and Scaife, A.A. (2015) Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Quarterly Journal of the Royal Meteorological Society, 141, 2206–2219.

Donat, M.G., Leckebusch, G.C., Wild, S., Ulbrich, U., 2011. Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations. Nat Hazards Earth Syst Sci., 11, 1351–1370.

Hillier, J.K., 2017. The Perils in Brief, in: Natural Catastrophe Risk Management and Modelling: A Practitioner’s Guide. Wiley-Blackwell, Oxford, UK, p. pp 536.

Lavers, D.A., Allan, R.P., Wood, E.F., Villarini, G., Brayshaw, D.J., Wade, A.J., 2011. Winter floods in Britain are connected to atmospheric rivers. Geophys Res Lett 38, L23803.

Leckebusch, G.C., D. Renggli, and U. Ulbrich, 2008: Development and Application of an Objective Storm Severity Measure for the Northeast Atlantic Region. Meteorol. Z., Vol. 17, No. 5, 575-587.

Renggli, D., Leckebusch, G.C., Ulbrich, U., Gliexner, S.N., 2011. The Skill of Seasonal Ensemble Prediction Systems to Forecast Wintertime Windstorm Frequency over the North Atlantic and Europe. Mon. Weather Rev. 139, 3052–3068.

Runge, J., et al., 2019: Inferring causation from time series in Earth system sciences, Nature Com.,

Scaife, A.A., Arribas, A., Blockley, E., 2014. Skilful long-range predictions of European and North American winters. Geophys Res Lett 41, 2514–2519.

Scaife, AA, et al., 2019: Does increased atmospheric resolution improve seasonal climate predictions? Atm Sci Let, DOI: 10.1002/asl.922

Vecchi, G.A., Delworth, T., Gudgel, R., Kapnick, S., Rosati, A., Wittenberg, A.T., Zeng, F., Anderson, W., Balaji, V., Dixon, K., Jia, L., Kim, H., Krishnamurthy, L., Msadek, R., Stern, W.F., Underwood, S.D., Villarini, G., Yang, X. and Zhang, S. (2014) On the seasonal forecasting of regional tropical cyclone activity. Journal of Climate, 27, 7994–8016.

Vitart, F. and Stockdale, T.N. (2001) Seasonal forecasting of tropical storms using coupled GCM integrations. Monthly Weather Review, 129, 2521–2537.

Walz, M.A., Donat, M.G., Leckebusch, G.C., 2018. Large-Scale Drivers and Seasonal Predictability of Extreme Wind Speeds Over the North Atlantic and Europe. J. Geophys. Res. Atmospheres 123, 11518–11535.

Walz, M.A., and G.C. Leckebusch, 2019: Loss potentials based on an ensemble forecast: How likely are winter windstorm losses similar to 1990? Atmospheric Science Letters, 20, 4, UNSP e891.


The COVID-19 pandemic’s potential impact on the project is minimal as the work is completely computer- & desk-based. All necessary datasets are readily available and the scientific work can also be done remotely. Thus, a physically presence, e.g. at a desk in the university, is not necessary. This work can even be done successfully during a potential nation-wide lockdown. The only facet of the project perhaps being affected is a potential placement at an industry partner. Nevertheless, any non in-person delivery of this, would not hinder the successful generation of suitable scientific results of the project.