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

国家天气预报中心正在转向新一代集合预报系统，该系统运行多种对流允许的模式预报（网格间距约2公里）。之所以需要它们，是因为它们可以部分解决小规模高影响天气现象（如强降水）的动力学问题。这种方法提供了大量的详细预报当地天气的任何给定的预测时间和位置，因此，丰富的新信息的可预测性的高影响力的事件，影响社会，如破坏性的风，山洪暴发，雪和雾。由于英国气象局MOGREPS-UK预测是首批投入使用的此类系统之一，因此现在有一个独特的5年预测数据集。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，我们认为，迫切需要发展一种能力，将这些数据综合成可管理数量的合理情景或故事情节，以捕捉令人关注的现象，并为应急响应者提供对他们可能面临的可能结果的清晰理解以及风险估计。该项目旨在开发提取集群的新技术（类似预报的群组），其可用于向预报员提供最终用户可容易理解的一小组可能的情景。将探讨从集合生成情景的三种方法：将全球和区域预报集合组合在一起的自上而下的方法，将高分辨率天气变量统计组合的自下而上的方法，以及在统计匹配之前利用物理洞察力划分集合的方法。将进行案例研究，其中包括集合被认为在结果中产生的变化不足的情况（例如2017年冬季的一个案例，其中所有MOGREPS-UK集合成员在英格兰南部产生了太多的雪）。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研究人员，业务预报员和具有灾害专业知识的多学科团队以及与应急响应人员的沟通合作。而在英国气象局总部（埃克塞特）的业务研究环境中，学生将体验实时预报的挑战，升级业务数值模型的过程以及面向用户的预报产品的生产。通过了解用户的问题和他们需要作出的决定，他们将了解用户的需要，并了解在何种程度上可以为预测创造附加值。在阅读，您将成为气象系动态过程研究小组的一员，该小组聚集了约40名研究人员，他们在每周的小组会议上研究天气系统和气候的动态。与中尺度动力学和数据同化研究小组一起，这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，并使您能够在该小组其他研究人员的帮助下开展研究。学生简介：本项目适合拥有物理学，数学或密切相关的环境或物理科学学位的学生。有计算统计学的经验和一些python，matlab或类似编程的知识将是可取的。与预报用户的共鸣和专业预报员的需求的理解是该项目的一个重要方面。资金细节：除了NERC的学生资助，该项目还得到了英国气象局的CASE赞助。

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.