Extracting likely scenarios from high resolution ensemble forecasts in real-time

从高分辨率集合预报中实时提取可能的场景

• 批准号: 2109529
• 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: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2109529
• 关键词: Extracting; likely; scenarios; resolution; ensemble

National weather forecast centres are moving to a new generation of ensemble forecast systems that run multipleconvection-permitting model forecasts (grid spacing ~2km). They are needed because they can partially resolve the dynamics of small-scale high impact weather phenomena such as intense precipitation. This approach provides a large number of detailed forecasts of local weather for any given forecast time and location, and hence a wealth of new information about the predictability of high-impact events that affect society such as destructive winds, flash flooding, snow and fog. Since the Met Office MOGREPS-UK forecast was one of the first of such systems to go operational, there is now a unique 5-year forecast dataset. One barrier to the full and effective use of these new forecasts is that it is considerably more difficult for a human to fully process such a large amount of information in time to communicate early warnings. Therefore, there is a pressing need to develop a capability to synthesise these data into a manageable number of plausible scenarios or storylines that capture the phenomena of concern and provide emergency responders a clear understanding of the possible outcomes they may face together with an estimate of risk.The project aims to develop new techniques for extracting clusters (groups of similar forecasts) from the ensemble which can be used to provide forecasters a small set of possible scenarios that can be readily understood by end users. Three approaches will be explored in generating scenarios from ensembles: a top-down approach from clustering global and regional forecast ensembles together, a bottom-up approach from statistical clustering of weather variables at high resolution and an approach using physical insight to partition an ensemble before statistical matching. Case studies will be performed that include situations in which the ensemble is perceived to have produced insufficient variability in outcomes (such as a case in winter 2017 in which all MOGREPS-UK ensemble members produced too much snow over southern England). The goal is to improve early warning services and risk-based decision making.Training opportunities:Through collaboration with the Met Office, you will have the opportunity to work with researchers in high resolution modelling and forecast evaluation, operational forecasters and the multi-disciplinary team with expertise in hazards and communication with emergency responders.While on placement in the Met Office headquarters (Exeter) in an operational research environment the student will experience the challenges of real-time forecasting, the process of upgrading the operational numerical models and production of user-facing forecast products. They will gain an appreciation of user-needs and the degree to which added value to forecasts can be created by understanding the user problems and the decisions they need to make. At Reading, you will be part of the Dynamical Processes Research Group in the Department of Meteorology which brings together about 40 researchers working on the dynamics of weather systems and climate in weekly group meetings. Together with the Mesoscale Dynamics and Data Assimilation Research Groups, this forms a world-renowned critical mass of atmospheric dynamics and predictability research and would enable you to develop your research with help from the other researchers in the group. Student profile:This project would be suitable for students with a degree in physics, mathematics or a closely related environmental or physical science. Experience of computational statistics and some prior knowledge of programming in python, matlab or similar would be desirable. Empathy with users of forecasts and understanding of the needs of professional forecasters is an important aspect of the project.Funding particulars:This project has CASE sponsorship from the Met Office in addition to the NERC studentship funding.

国家天气预报中心正在转向新一代综合预报系统，该系统运行允许多重对流的模式预报（网格间距约2公里）。它们是必要的，因为它们可以部分地解决强降水等小尺度高影响天气现象的动力学问题。这种方法为任何给定的预报时间和地点提供了大量详细的当地天气预报，因此提供了大量关于影响社会的高影响事件（如破坏性大风、山洪暴发、雪和雾）可预测性的新信息。由于英国气象局的MOGREPS-UK预报是此类系统中最早投入使用的系统之一，现在有了一个独特的5年预报数据集。充分和有效利用这些新预报的一个障碍是，人类要及时充分处理如此大量的信息以传达早期预警要困难得多。因此，迫切需要发展一种能力，将这些数据综合为可管理的数量合理的情景或故事情节，这些情景或故事情节捕捉到令人关切的现象，并使应急人员清楚地了解他们可能面临的可能结果以及对风险的估计。该项目旨在开发从集合中提取聚类（类似预测的组）的新技术，这些聚类可用于向预报员提供最终用户易于理解的一小部分可能情景。将探索从集合中生成情景的三种方法：自上而下的方法，将全球和区域预报集合聚类在一起，自下而上的方法，从高分辨率天气变量的统计聚类，以及在统计匹配之前使用物理洞察力来划分集合的方法。将进行案例研究，其中包括认为整体在结果上产生的变异性不足的情况（例如2017年冬季所有MOGREPS-UK整体成员在英格兰南部产生过多降雪的情况）。目标是改善预警服务和基于风险的决策。培训机会：通过与英国气象局的合作，您将有机会与高分辨率建模和预测评估方面的研究人员、业务预报员以及具有灾害和与应急响应人员沟通专业知识的多学科团队一起工作。在英国气象局总部（埃克塞特）的运营研究环境中，学生将体验实时预测的挑战，升级运营数值模型的过程以及面向用户的预测产品的生产。他们将了解用户的需求，以及通过了解用户问题和他们需要做出的决定来创造预测附加值的程度。在雷丁大学，你将成为气象学系动力过程研究小组的一员，该小组在每周的小组会议上汇集了大约40名研究天气系统和气候动力学的研究人员。与中尺度动力学和数据同化研究小组一起，形成了世界知名的大气动力学和可预测性研究的临界质量，并将使您能够在小组其他研究人员的帮助下发展您的研究。学生简介：本项目适合具有物理，数学或相关环境或物理科学学位的学生。有计算统计学经验，有python、matlab或类似语言编程经验者优先考虑。对预测使用者的同理心和对专业预报员需求的理解是该项目的一个重要方面。资助细节：除了NERC学生资助外，该项目还获得了英国气象局的CASE赞助。