Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting

利用新数据源、数据同化和集合技术进行风暴和洪水预报

• 批准号: NE/E002064/1
• 负责人: Anthony Illingworth
• 金额: $ 32.47万
• 依托单位: NERC CEH (Up to 30.11.2019)
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FE002064%2F1
• 关键词: Exploitation; new; data; sources; assimilation

Floods in the UK are often caused by extreme rainfall events. At present, weather forecasts can give an indication of a threat of severe storms which might cause flash floods, but are unable to say precisely when and where the downpours will occur, due to the complex range of processes and space-time scales involved. The first stage is to predict the air motions leading to convergence and ascent at a certain location where the precipitation will be initiated, then the development of the precipitation needs to be forecast, and hydrological models used to produce accurate, quantitative, probabilistic flood predictions. Data assimilation is a sophisticated mathematical technique that combines observations with model predictions to give an analysis of the current state of the atmosphere. This analysis may be used to initialise a weather forecast. Although precipitation is well observed by weather radar, attempts to assimilate radar data have had little success; by the time the rain develops the forecast model state is too far from the truth and the air motions are inconsistent with the position of the first radar precipitation echo. We propose to overcome this problem by assimilating new types of data from weather radars. These provide information on the evolving humidity fields and air motions in the lower atmosphere so that the model can accurately track the developing storm before precipitation appears. The model used will be a new Met Office model that can be run with a resolution (i.e., grid-spacing) of order 1-4km. This enables storm-cloud motions to be explicitly calculated, rather than treated as a sub-grid-scale effect. Furthermore, current operational forecast models are only updated with observations every few hours; in the new approach the model will be updated much more frequently. This should yield weather forecasts with improved locations (in space-time) for rainfall events. Initialisation errors are not the only cause of inaccuracies in storm-scale weather forecasts. Models are often run only for a small region of the world, and the data on the boundaries of this area provided from a larger-scale model. These data are known as lateral boundary conditions. Errors in these lateral boundary conditions and modelling errors also contribute to the errors in the forecast. Even if these errors were reduced, the nonlinear nature of the storm dynamics ensures that there is a limit, beyond which the value of deterministic forecasts becomes questionable. After that point it becomes important to determine the uncertainties in the forecast precipitation, so an ensemble approach is required. (An ensemble is a collection of perturbed forecasts that may be considered as a statistical sample of the forecast probability distribution.) The appropriate construction of a storm-scale ensemble is an open question. We propose a structured approach where perturbations will be designed on the basis of physical insight into convective forcing mechanisms. The resulting probabilistic rainfall forecasts can be interfaced to hydrological models used for flood forecasting. For the first time, this project will allow different scales of application of these methods to be supported: ranging from localised flash flooding of small catchments, through to indicative first-alert forecasting with UK-coverage and forecasting of river discharges to the sea. The project will also assess the impacts of improvements in numerical weather prediction on flood forecast performance. In this project we anticipate fruitful interactions between the different disciplines of observations and measurement, meteorology and hydrology. Radar assimilation software development and ensemble forecasts will take place using Met Office models, so improvements can be implemented operationally very easily. The use of operational radars makes this project well placed to take advantage of data from any extreme events occurring during the period of the study.

英国的洪水通常是由极端降雨事件引起的。目前，天气预报可以提示可能引发山洪的强风暴的威胁，但由于过程和时间尺度的复杂性，无法准确预测暴雨会在何时何地发生。第一阶段是预测降水开始的某一地点导致辐合和上升的空气运动，然后需要对降水的发展进行预测，并使用水文模型进行准确，定量，概率的洪水预测。数据同化是一种复杂的数学技术，它将观测与模式预测结合起来，对大气的当前状态进行分析。这种分析可以用来初始化天气预报。虽然气象雷达能很好地观测到降水，但同化雷达数据的努力收效甚微；当降雨发生时，预报模式状态与实际情况相差甚远，空气运动与第一雷达降水回波位置不一致。我们建议通过吸收来自气象雷达的新型数据来克服这个问题。这些数据提供了低层大气湿度场和空气运动的演变信息，使模式能够在降水出现之前准确地跟踪风暴的发展。所使用的模型将是一个新的气象局模型，可以以1-4公里的分辨率（即网格间距）运行。这使得风暴云的运动能够被明确地计算出来，而不是被当作亚网格尺度的效应来处理。此外，目前的业务预报模式每隔几小时才根据观测资料更新一次；在新的方法中，模型将更频繁地更新。这将产生具有降雨事件改进位置（时空）的天气预报。初始化错误并不是风暴尺度天气预报不准确的唯一原因。模型通常只针对世界上的一个小区域运行，该区域边界上的数据来自一个更大尺度的模型。这些数据被称为横向边界条件。这些侧向边界条件的误差和模拟误差也会导致预报的误差。即使这些误差减少了，风暴动力学的非线性特性也会有一个限制，超过这个限制，确定性预报的价值就会受到质疑。在此之后，确定预报降水的不确定性就变得很重要了，因此需要采用集合方法。（集合是扰动预测的集合，可视为预测概率分布的统计样本。）风暴尺度集合的适当构建是一个悬而未决的问题。我们提出了一种结构化的方法，其中将根据对对流强迫机制的物理见解设计扰动。由此产生的概率降雨预报可以与用于洪水预报的水文模型相结合。该项目将首次支持这些方法的不同规模的应用：从小型集水区的局部山洪暴发，到英国范围内的指示性预警预报和河流入海流量预报。该计划亦会评估数值天气预报的改进对洪水预报性能的影响。在这个项目中，我们期望在观测和测量、气象学和水文学的不同学科之间进行富有成效的互动。雷达同化软件的开发和集合预报将使用气象局的模型进行，因此改进可以很容易地在操作上实施。作战雷达的使用使该项目能够很好地利用研究期间发生的任何极端事件的数据。