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

英国的洪水通常是由极端降雨事件引起的。目前，天气预报可以给出可能导致山洪暴发的严重风暴威胁的迹象，但由于涉及的过程和时空尺度复杂，无法准确说明暴雨将在何时何地发生。第一阶段是预报在某一降水发源地导致辐合和上升的空气运动，然后需要预报降水的发展，并使用水文模型来产生准确的、定量的、概率的洪水预报。数据同化是一种复杂的数学技术，它将观测与模型预测相结合，以分析大气的当前状态。这一分析可以用来初始化天气预报。虽然天气雷达能很好地观测降水，但同化雷达数据的尝试收效甚微；到降雨发展时，预报模式状态与事实相差太远，空气运动与第一个雷达降水回波的位置不一致。我们建议通过吸收来自天气雷达的新类型的数据来克服这个问题。这些信息提供了有关低层大气中湿度场和空气运动演变的信息，因此该模式可以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种新的Met Office模型，它可以以1-4千米的分辨率(即网格间距)运行。这使得可以显式计算风暴云的运动，而不是将其视为亚网格尺度的效果。此外，目前的业务预报模型每隔几个小时才根据观测值进行更新；在新的方法中，模型将更频繁地更新。这应该会产生天气预报，改善降雨事件的(空间和时间)位置。初始化错误并不是暴风雨天气预报不准确的唯一原因。模型通常只针对世界上的一小部分区域运行，而该区域边界的数据是由较大比例的模型提供的。这些数据称为横向边界条件。这些横向边界条件的误差和建模误差也是造成预测误差的原因。即使减少了这些误差，风暴动力学的非线性本质也确保了存在一个极限，超过这个极限，确定性预报的价值就会受到质疑。在这一点之后，确定预报降水中的不确定性变得很重要，因此需要一种集合方法。(集合是可被视为预测概率分布的统计样本的扰动预测的集合。)一个风暴规模的剧团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，其中扰动的设计将基于对对流强迫机制的物理洞察。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连接。该项目将首次支持这些方法的不同规模的应用：从小流域的局部山洪暴发，到英国的指示性第一警报预报-覆盖范围和河流排放到海洋的预报。该项目还将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，我们预计观测和测量、气象学和水文学之间的不同学科之间将进行卓有成效的互动。雷达同化软件开发和集合预报将使用气象局模式进行，因此改进可以非常容易地在业务上实施。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。

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.