Dynamical and microphysical evolution of convective storms (DYMECS)

英国一系列潮湿的夏季让我们想起了雷雨可能造成的毁灭性洪水，从2004年8月博斯卡斯尔洪水的标志性画面到2007年反复发生的洪水事件，导致格洛斯特郡城镇长达17天没有自来水供应，根据随后的皮特评论，造成了超过30亿英镑的损失。显然，迫切需要提高我们在未来几个小时到几天内预报这些风暴的能力。世界各地用于天气预报的大多数计算机模型将大气分成几十公里宽的盒子。由于对流阵雨云和雷暴的直径通常只有2至10公里，这些模型没有机会模拟单独的云；相反，它们必须尝试估计每个盒子内的云团对地表降雨的影响。这就是所谓的“对流参数化”，非常容易出错。然而，近年来计算机能力不断增强，英国气象局目前正在运行一种分辨率最高的国家天气预报模式，其模式的水平网格箱大小为1.5公里，覆盖整个英国。在这种分辨率下，它几乎能够模拟单个云中的空气流动，而不需要对流参数化。虽然有证据表明，这提高了预报的准确性，但很明显，在这一决议中模拟的对流阵雨云和雷暴的性质仍然存在非常重大的缺陷。我们需要的是对英国上空的大量阵雨云和雷暴进行非常详细的观测，以及每一朵云是如何增长和衰减的，以便测试和改进这些高分辨率模式。在这个项目中，我们将使用汉普郡的奇尔博顿天气雷达获取这些信息，该雷达能够以非常高的分辨率测量风暴的各种有用属性(3D结构、地面降雨率、冰雹的发生、云层上部的冰量、风暴内的气流和湍流水平)。该项目的一个特别创新的方面是，我们将开发软件来自动控制雷达，以便它可以在雷暴演变时跟踪个别雷暴。通过在18个月期间，包括2011年和2012年夏季，在40个合适的日子里运行雷达，将获得关于可能发生的数百场风暴的详细信息。这一独特的数据集将被用来评估预报模型中风暴的演变，其详细程度和一系列条件以前从未实现过。我们将用不同的模式配置重新运行模式(例如，以不同的方式描述云冰颗粒和液滴生长并相互作用最终形成雨的方式)，以确定是什么限制了该模式中阵雨云和雷暴的真实性，从而如何改进预报。这些发现将适用于世界各地的其他模型。我们将研究风暴在模型中和现实中演变的详细方式，特别是一个风暴中的气流如何共同引发另一场风暴，以及是什么导致风暴在一个地方持续降雨，因为通常正是这种行为导致洪水。该项目的另一个目标是检验对流参数化中所作的一些假设。尽管对流参数化不如明确模拟单个云那样准确，但对于做出100年气候预测来说，我们没有计算机能力来实现这一点，而这将是整个世界所需的1.5公里长的盒子。我们的详细数据集将帮助我们确保这些参数中关于云的大小和属性的假设是现实的，从而潜在地提高气候预测的准确性。

A series of wet summers in the UK have reminded us of the devastating floods that can be caused by thunderstorms, from the iconic images of the Boscastle flood in August 2004 through to the repeated flooding events in 2007 that led to towns in Gloucestershire without mains water for up to 17 days and according to the subsequent Pitt Review, caused in excess of 3 billion pounds worth of damage. There is clearly an urgent need to improve our ability to forecast these storms from a few hours to a few days ahead. Most computer models used for weather forecasting worldwide divide the atmosphere up into boxes several tens of kilometers across. Since convective shower clouds and thunderstorms are typically only between 2 and 10 km across, these models have no chance to simulate individual clouds; rather they must try to estimate the effect of an ensemble of clouds within each box on surface rainfall. This is known as a 'convection parameterization' and is very error prone. However, there has been a continued increase in computer power in recent years, and the Met Office currently runs one of the highest resolution national weather forecast model operationally, its model having a horizontal grid-box size of 1.5 km over the whole of the UK. At this resolution it is just about able to simulate the air flows in individual clouds, and a convection parameterization is not needed. While there is evidence that this has improved the accuracy of forecasts, it is clear that there are still very significant shortcomings in the nature of convective shower clouds and thunderstorms simulated at this resolution. What we need are very detailed observations of a large number of shower clouds and thunderstorms over the UK, and how each cloud grows and decays, with which to test and improve these high resolution models. In this project we will obtain this information using the Chilbolton weather radar in Hampshire, which is able to measure all kinds of useful properties of storms at very high resolution (3D structure, surface rain rate, the occurrence of hail, the amount of ice in the upper parts of the cloud, the airflows within the storm and the levels of turbulence). A particularly innovative aspect to the project is that we will develop software to control the radar automatically, so that it can track individual thunderstorms as they evolve. Detailed information on potentially hundreds of storms will be obtained by operating the radar on 40 suitable days over an 18-month period, including the summers of 2011 and 2012. This unique dataset will be used to evaluate the evolution of storms in the forecast model in a level of detail and a range of conditions that has never been achieved before. We will rerun the model with different model configurations (e.g. different ways to describe the ways that cloud ice particles and liquid droplets grow and interact to eventually form rain), in order to determine what it is that limits the realism of shower clouds and thunderstorms in this model, and hence how to improve forecasts. These findings will be applicable to other models worldwide. We will examine the detailed way that storms evolve in the model and in reality, particularly how the airflows in one storm conspire to initiate another storm, and what it is that causes storms to rain persistently in one place, since it is often this behaviour that is responsible for flooding. A further aim of this project will be to test a number of the assumptions that are made in convection parameterizations. Although convection parameterization is not as accurate as simulating individual clouds explicitly, for making 100-year climate forecasts we do not have the computer power to do this with the 1.5-km boxes that would be required over the entire world. Our detailed dataset will help us to ensure that the assumptions about the size and properties of clouds in these parameterizations are realistic, potentially improving the accuracy of climate forecasts.