Modelling the frequency of extreme event forecasts for the management of funds for humanitarian action

对极端事件预测的频率进行建模，以管理人道主义行动资金

• 批准号: 2605786
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605786
• 关键词: Modelling; frequency; extreme; event; forecasts

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 expecteddrawdown (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 assumptionoverlooks 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 eventTherefore, this research work aims to explore how often the forecasts will trigger a payout, and how muchuncertainty 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.

基于预测的融资（FbF）是一项人道主义倡议，它在灾害发生前根据预测发放资金，以支持人道主义行动，而不是依赖于灾害应对。该倡议发展迅速，现在由若干系统的筹资机制组成，例如红十字会与红新月会国际联合会的救灾紧急基金。最近捐助者对固定资产基金的投资有所增加，因此有必要探讨管理这类基金破产风险的各种办法（例如保险产品或巨灾债券），其中一种风险与预测触发因素得到满足的频率有关。成功管理这一FbF融资机制和类似的资金需要了解预期的缩减（即预测触发因素中规定的条件得到满足和资金释放的频率）。目前，预期的减少是基于（例如）每5年平均出现一次10年1次回报期事件的50%概率的假设。这个假设忽略了以下问题：-有一个有限的样本1在10年重现期的定义,所以它可能比预期的或多或少地经常发生,事件将会集中在空间和时间,导致出人意料的低或高从基金中提取-概率预测并不完美,overpredict或低估了一个事件发生的可能性,以及任何偏见或噪音也将随预测变量,eventTherefore交货时间和严重程度,这项研究工作旨在探索预测触发支付的频率，以及支付频率的不确定性有多大。该研究项目将利用欧洲中期天气预报中心（European Centre of mid - range Weather forecasting）制作的预报档案，例如重新预报，重点放在具体的预报产品上，包括全球洪水预警系统（www.globalfloods.eu），以探索目前存在的一些预报触发因素（例如乌干达和孟加拉国的洪水事件），以及极端预报指数。作为该项目的一部分，该研究生将有机会在欧洲中期天气预报中心进行为期3个月的实习，以开发用于识别和表征事件预报频率偏差的指标。通过跟随ECMWF工作人员进行日常报告的操作，学生将深入了解事件预测频率偏差可能产生问题的情况。他们将与预测部门的主要研究人员合作，制定诊断此类偏差原因的策略，以支持模型开发，并将此方法应用于选定的案例研究。该学生将与IFRC和红十字与红新月气候中心合作，提供有关触发频率的信息，这些信息将被纳入DREF“FbA早期行动协议”的制定中，从而产生宝贵的现实影响。