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，天气预报可以显示可能导致山洪暴发的严重风暴的威胁，但由于所涉及的过程和时空尺度的复杂范围，无法准确地说暴雨将在何时何地发生。第一阶段是预测空气运动导致的收敛和上升，在一定的位置，降水将开始，然后需要预测降水的发展，水文模型用于产生准确的，定量的，概率洪水预报。数据同化是一种复杂的数学技术，它将观测与模式预测相结合，以分析大气的当前状态。该分析可用于初始化天气预报。虽然天气雷达可以很好地观测到降水，但同化雷达数据的尝试收效甚微;到降雨形成时，预报模式的状态与事实相差太远，空气运动与第一次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。这些数据提供了低层大气中不断变化的湿度场和空气运动的信息，使模式能够在降水出现之前准确地跟踪正在发展的风暴。使用的模型将是一个新的气象局模型，可以通过解决方案运行（即，网格间距）为1-4km。这使得风暴云的运动能够被明确地计算出来，而不是被视为亚网格尺度的效应。此外，目前的业务预报模型每隔几个小时才用观测数据更新一次;在新方法中，模型的更新频率将高得多。这将产生天气预报，改进降雨事件的位置（时空）。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，并且该区域边界的数据来自较大规模的模型。这些数据被称为横向边界条件。这些横向边界条件的误差和模型误差也会导致预测误差。即使这些误差减少了，风暴动力学的非线性性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成了问题。在这一点之后，重要的是确定预测降水的不确定性，因此需要一个集合方法。(An集合是扰动预报的集合，其可以被认为是预报概率分布的统计样本。风暴尺度集合的适当构造是一个悬而未决的问题。我们提出了一个结构化的方法，扰动将设计的基础上的物理洞察对流强迫机制。由此产生的概率降雨预报可以连接到用于洪水预报的水文模型。该项目将首次允许支持这些方法的不同规模应用：从小流域的局部山洪暴发，到具有英国覆盖范围的指示性预警预报和河流入海量预报。该项目还将评估改进数值天气预报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，气象学和水文学的不同学科之间的富有成效的互动。雷达同化软件开发和集合预报将使用气象局模型进行，因此可以很容易地在操作上实现改进。使用业务雷达使该项目能够充分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.