Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC)

短暂的强降雨可能导致山洪暴发，可能造成数百万英镑的财产损失，并威胁到生命安全。准确的洪水预警甚至可以提前几个小时做好准备，将损失降到最低。为了改善对这些事件的预测，需要更准确的暴雨预报，然后可以将其用于洪水预测和预警系统。英国气象局正在开发一种新的数值天气预报系统，目的是改善恶劣天气预报。这是一个计算机模型，可以解决表示大气运动和其他物理过程（如云的形成）的数学方程，其水平网格间距为1.5公里。这样可以更准确地表示精细尺度特征和明确表示风暴，但结果仍然依赖于初始条件的准确性或描述大气变量（如风、压力、温度和湿度）当前状态的初始数据。初始条件通常使用一种称为数据同化的复杂数学技术来估计，该技术将观测值与模型信息混合在一起，同时考虑到数据中的不确定性。在这个项目中，我们提出了减少初始条件误差的基础研究。这项工作将在雷丁大学、萨里大学和英国气象局的合作下进行。我们计划研究从气象雷达观测大气低层降水和湿度中提取最大信息的方法。虽然天气雷达通常能很好地观测到降雨，但严重的降水会使雷达波束失去能量，因此较弱的返回信号可能会被误解，从而得到比实际更低的降雨率。我们将开发算法来纠正这个问题和其他由强降雨引起的问题。最近，我们还开发了推断低层大气湿度信息的技术，我们计划优化方法并研究观测误差特征，为这些数据被英国气象局吸收做准备。我们的目标之一是利用观测来提供小尺度的信息，而不降低大尺度的天气模式，大尺度的天气模式本身可能是准确的。然而，目前许多小尺度观测信息由于忽略观测误差之间的相关性而丢失。我们将开发一种通用的方法来处理一系列观测类型的观测相关性。我们将研究既能捕获观测中包含的最大量信息，又能在有限时间内进行实际计算的数学方法。另一个目标是开发创新的方法来处理潮湿过程，这在很大程度上是当今同化系统所没有的。我们计划设计和测试高效和有效的方法来吸收水分信息，尊重在大气中运作的复杂的动力学和物理关系。如果成功，这种新方法将比目前更好地利用云和雨的影响观测。由于对初始条件中的小误差的高度敏感性，即使有良好的数据同化和模型，一些事件本身也是不可预测的，这使得预测对流雨变得更加困难。将进行进一步的研究，利用从模型和观测中得到的诊断来研究此类事件的低可预测性的动力学原因。

Brief periods of intense rainfall can lead to flash flooding with the potential to cause millions of pounds of damage to property, and to threaten lives. Accurate flood warnings even just a few hours ahead can allow preparations to be made to minimize damage. In order to improve the prediction of these events, more accurate forecasts of heavy rainfall are needed, which can then be used to inform flood prediction and warning systems. The UK Met Office is developing a new numerical weather prediction system with the goal of improving severe weather forecasts. This is a computer model that solves mathematical equations representing atmospheric motions and other physical processes such as cloud formation, with a horizontal grid spacing of 1.5km. This allows a more accurate representation of fine-scale features and explicit representation of storms, but the results are still dependent on the accuracy of the starting conditions or initial data describing the current state of atmospheric variables such as winds, pressure, temperature and humidity. Initial conditions are usually estimated using a sophisticated mathematical technique known as data assimilation that blends observations with model information, taking account of the uncertainties in the data. In this project, we propose fundamental research to reduce initial condition errors. The work will be carried out in a partnership between the Universities of Reading, Surrey and the Met Office. We plan to investigate ways of extracting the maximum information from weather radar observations of precipitation and moisture in the lower parts of the atmosphere. Although rainfall is usually well observed by weather radar, severe precipitation can cause the radar beam to lose energy, and thus the weaker returned signal may be misinterpreted, giving a lower rain-rate than in reality. We will develop algorithms to correct for this and other problems caused by severe rainfall. Recently, we have also developed techniques to infer humidity information about the lower atmosphere, and we plan to optimize the method and investigate the observation error characteristics, to prepare for this data to be assimilated by the Met Office. One of our goals is to use observations to provide information on the small scales without degrading the large scale weather patterns, which are themselves likely to be accurate. However, currently much of the small scale observational information is being lost by ignoring correlations between observation errors. We will develop a generic approach for treating observation correlations for a range of observation types. We will investigate mathematical methods that both capture the maximum amount of information contained in the observations, while still being practical for operational computations, which have to take place within a limited time frame. Another goal is to develop innovative ways of treating moist processes that are largely absent from present-day assimilation systems. We plan to design and test efficient and effective ways of assimilating moisture information that respect the intricate dynamical and physical relationships that operate in the atmosphere. If successful, such new approaches will allow better use of cloud and rain affected observations than at present.Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Further studies will be made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations.