Modelling the frequency of extreme event forecasts for the management of funds for humanitarian action
Lead Research Organisation:
University of Reading
Department Name: Meteorology
Abstract
Forecast-based Financing (FbF) is a humanitarian initiative which releases funds on the basis of a forecast to support humanitarian actions before a disaster strikes, rather than relying on disaster response. The initiative has grown rapidly and now consists of several systematic financing mechanisms, such as the International Federation of Red Cross and Red Crescent Societies' (IFRC) Disaster Relief Emergency Fund (DREF). The recent expansion in donor-investments in FbF has necessitated the exploration of options for managing the risk of insolvency of such funds (e.g. insurance products or catastrophe bonds), and one such risk relates to how often the forecast triggers are met.
Successful management of this FbF financing mechanism and similar funds requires knowledge of the expected
drawdown (i.e. how frequently the conditions specified in the forecast triggers will be met and funding released).
Currently, the expected drawdown is based on the assumption that (for example) the condition of a 50%
probability of a 1 in 10 year return period event would be met on average once in every 5 years. This assumption
overlooks the following issues:
- There is a limited sample from which the 1 in 10 year return period is defined, and so it could occur more or less frequently than expected
- Events will be clustered in both space and time, leading to unexpectedly low or high drawdowns from the fund
- Probabilistic forecasts are not perfect and will overpredict or underpredict the likelihood of an event occurring, and any bias or noise will also vary by forecast variable, lead time and severity of event
Therefore, this research work aims to explore how often the forecasts will trigger a payout, and how much
uncertainty there is in the frequency of payouts. The research project will use archives of forecasts produced by the European Centre of Medium-Range Weather Forecasts such as re-forecasts, focusing on specific forecast products including the Global Flood Awareness System (www.globalfloods.eu) to explore some of the forecast triggers currently in place (such as for flood events in Uganda and Bangladesh), and also the Extreme Forecast Index.
As a part of this project, the research student will have the opportunity to undertake a 3-month placement at the European Centre for Medium Range Weather Forecasting - to develop metrics for identifying and characterizing biases in event forecast frequency. By shadowing ECMWF staff on operational 'daily report' duty the student will gain a deep understanding of the circumstances where biases in event forecast frequency might be problematic. They will work together with leading researchers in the Forecast Department to develop a strategy for diagnosing the causes of such biases to support model development, applying this approach to selected case studies. Working with the IFRC and the Red Cross Red Crescent Climate Centre, the student will offer information about the frequency of triggers, which will be incorporated into the development of Early Action Protocols for the 'FbA by the DREF', thereby having a valuable real-world impact.
Successful management of this FbF financing mechanism and similar funds requires knowledge of the expected
drawdown (i.e. how frequently the conditions specified in the forecast triggers will be met and funding released).
Currently, the expected drawdown is based on the assumption that (for example) the condition of a 50%
probability of a 1 in 10 year return period event would be met on average once in every 5 years. This assumption
overlooks the following issues:
- There is a limited sample from which the 1 in 10 year return period is defined, and so it could occur more or less frequently than expected
- Events will be clustered in both space and time, leading to unexpectedly low or high drawdowns from the fund
- Probabilistic forecasts are not perfect and will overpredict or underpredict the likelihood of an event occurring, and any bias or noise will also vary by forecast variable, lead time and severity of event
Therefore, this research work aims to explore how often the forecasts will trigger a payout, and how much
uncertainty there is in the frequency of payouts. The research project will use archives of forecasts produced by the European Centre of Medium-Range Weather Forecasts such as re-forecasts, focusing on specific forecast products including the Global Flood Awareness System (www.globalfloods.eu) to explore some of the forecast triggers currently in place (such as for flood events in Uganda and Bangladesh), and also the Extreme Forecast Index.
As a part of this project, the research student will have the opportunity to undertake a 3-month placement at the European Centre for Medium Range Weather Forecasting - to develop metrics for identifying and characterizing biases in event forecast frequency. By shadowing ECMWF staff on operational 'daily report' duty the student will gain a deep understanding of the circumstances where biases in event forecast frequency might be problematic. They will work together with leading researchers in the Forecast Department to develop a strategy for diagnosing the causes of such biases to support model development, applying this approach to selected case studies. Working with the IFRC and the Red Cross Red Crescent Climate Centre, the student will offer information about the frequency of triggers, which will be incorporated into the development of Early Action Protocols for the 'FbA by the DREF', thereby having a valuable real-world impact.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/S007261/1 | 01/10/2019 | 30/09/2027 | |||
| 2605786 | Studentship | NE/S007261/1 | 01/10/2021 | 19/03/2026 | Harshita Gupta |