About the Project
- 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. The latest research showed the 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 skills.
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?
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:
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
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 (including CASE):
Further information on partners and collaboration:
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.
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.
Applications should include:
- CENTA application form, downloadable from CENTA application
- CV with the names of at least two referees (preferably three and who can comment on your academic abilities)
The application should please completed via: https://sits.bham.ac.uk/lpages/LES068.htm. Please select 'Apply Now' in the PhD Geography and Environmental Science (CENTA) section. Please quote CENTA23_[B19] when completing the application form.
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