EGU General Assembly 2020
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the Creative Commons Attribution 4.0 License.

Improving our understanding of Bangladesh tropical cyclone risk: decision making insights using kilometre scale numerical modelling and Bayesian data analysis

Hamish Steptoe1, Theo Economou1,2, and Bernd Becker1
Hamish Steptoe et al.
  • 1Met Office, Exeter, UK
  • 2College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK

We present results from state-of-the-art kilometre scale numerical models of tropical cyclones over Bangladesh.  We demonstrate how the latest generation of numerical models are filling the data gap in regions of the world with sparse observational networks, and compare our results to the latest generation global reanalyses.  We show how an ensemble of simulations expands our understanding of plausible events beyond our limited observations record.  Utilising this ensemble information in a Bayesian data analysis framework, we can robustly estimate prediction intervals for various parameters, such as peak wind speed or extreme rainfall, which when combined with Decision Theory and a loss function offer a coherent data-to-decision framework supporting disaster risk assessment and management strategies. We show how this decision making could be integrated into current global weather and climate forecast ensembles to provide forecasting of hazards and impacts up to 5 days ahead of an event, and in a future climate context.  We end with some thoughts on the ways this could influence the future of risk management and insurance underwriting and the challenges of working with big numerical model datasets.

How to cite: Steptoe, H., Economou, T., and Becker, B.: Improving our understanding of Bangladesh tropical cyclone risk: decision making insights using kilometre scale numerical modelling and Bayesian data analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7721,, 2020

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Display material version 1 – uploaded on 06 May 2020
  • CC1: Comment on EGU2020-7721, Md Jamal Uddin Khan, 06 May 2020

    Thanks for this very interesting work. Any comment on the performance of model to reproduce the precipitation field? 

    I will be eagerly waiting for the published paper. 

    • AC1: Reply to CC1, Hamish Steptoe, 07 May 2020

      This is an interesting question, but not something we have looked into.  I would expect the comparison to be similar to wind, in that the extremes will be more intense, but it would be interesting to see how the spatial distribution compares.

      Precipitation data from our model runs is already freely availble via Oasis Hub:

      • CC4: Reply to AC1, Md Jamal Uddin Khan, 08 May 2020

        Thanks. I will surely check the data out. One other small query - what is the source of most improvement in your model? Or to put it another way, other than the resolution, how the model differes from the ERA5? Any specific physics improvement?

        Thanks again.

        • AC3: Reply to CC4, Hamish Steptoe, 13 May 2020

          The differences are primarily down to increased resolution, which means we can explicitly resolve convective processes in our regional model. I'm fairly certain (though find it hard to track down a reference) that given the resolution, convection in ERA5 is still parametrized.

  • CC2: Comment on EGU2020-7721, Tracy Irvine, 07 May 2020

    Hi Hamish,

    Would you mind if we send a link out to your presentation on Oasis Hub twitter?




    • AC2: Reply to CC2, Hamish Steptoe, 07 May 2020

      No, that's fine. Can you tweet @metoffice_sci and I'll make sure we retweet.

      • CC3: Reply to AC2, Tracy Irvine, 07 May 2020