Spatio-temporal variability in observed and simulated rainfall fields

观测和模拟降雨场的时空变化

• 批准号: EP/D062241/1
• 负责人: Valerie Isham
• 金额: $ 1.28万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD062241%2F1
• 关键词: Spatio; temporal; variability; observed; simulated

How can we ensure that everybody has enough safe water to drink? What can be done to protect our towns and cities from flooding? Ultimately, the answers to such questions depend on how much it rains (or not!). Rainfall affects many other aspects of modern life in less obvious ways - for example, it affects radio communications, so that communications engineers need to consider rainfall when designing new technology.In planning for the future it is unwise to rely solely on past observations, because the weather is never exactly the same from day to day: the future is guaranteed to be different from the past (even without accounting for changes in the earth's climate). It is therefore useful to be able to use a computer to simulate pseudo-rainfall data that allows for random variation. Hydrologists can use these simulated data to test their plans and designs.One way of simulating rainfall data is by treating it as a random process in space and time, and building a probabilistic model for this process. Such a model should produce simulated rainfall sequences that have the right kind of structure. However, it is not always clear what is meant by this, or how to measure it. This is because, typically, the rainfall process is organised in a complex way in both time and space; some features of this organisation will be very important in controlling the effect of a particular rainfall event, while others might be less so. Within this context, the proposed research aims to develop ways of summarising the complex structure in observed rainfall fields, and to apply these summaries to rainfall models that are currently being developed for national application in the UK. The summaries considered are based on scaling ideas, whereby the structure of the rainfall process at one space or time scale can be related to that at another. Two particular class of summary measures will be considered:1. Spectral multiscaling of spatially averaged rain rate (SARR) processes. This is a means of linking the properties of rainfall sequences observed over different spatial areas. For example, an hourly sequence of average rainfall over an area of 100 square kilometres will typically look very different from an hourly sequence for an area of 10,000 square kilometres; spectral multiscaling describes the relationships between the properties of the two sequences. 2. Scaling properties of wet and dry periods at different spatial scales. For example, dry periods are likely to be shorter at large spatial scales than over small areas, since it is more likely to be raining somewhere in a large region than in a small area.3. Relationships between SARR and the fraction of area (in the region of aggregation) where rain rate exceeds a given threshold. A lot of previous work has shown that remarkably stable relationships exist, across a range of spatial scales, for rainfall observed in the tropics. One of the aims of the proposed research is to determine, through analyses of data from weather radar stations and rain gauges in the UK, whether these relationships hold more generally. If so, this would open up exciting possibilities - for example, the results could be used to recreate rainfall data at a fine space or time scale from observations at a much coarser scale.A further aspect of the proposed work is to determine whether these relationships can be reproduced by rainfall models that are currently being developed by the investigators for national application in the UK. Currently, the performance of such models is usually assessed using conventional statistical summaries. The proposed research allows the possibility of carrying out much more detailed performance assessment, using measures that are arguably more relevant than those currently in use.

我们如何确保每个人都有足够的安全饮用水？我们可以做些什么来保护我们的城镇免受洪水的侵袭？归根结底，这些问题的答案取决于雨下了多少(或不下！)。降雨以不太明显的方式影响现代生活的许多其他方面--例如，它影响无线电通信，因此通信工程师在设计新技术时需要考虑降雨。在规划未来时，仅依靠过去的观测是不明智的，因为天气从来不会一天比一天完全相同：未来肯定会与过去不同(即使不考虑地球气候的变化)。因此，能够使用计算机模拟允许随机变化的伪降雨数据是有用的。水文学家可以利用这些模拟数据来验证他们的计划和设计。模拟降雨量数据的方法之一是将其视为一个在空间和时间上的随机过程，并为这个过程建立一个概率模型。这样的模型应该产生具有正确结构的模拟降雨序列。然而，人们并不总是清楚这是什么意思，或者如何衡量它。这是因为，通常情况下，降雨过程在时间和空间上都是以一种复杂的方式组织的；这种组织的一些特征在控制特定降雨事件的影响方面将非常重要，而其他特征可能不那么重要。在这一背景下，这项拟议的研究旨在开发总结观测雨量场中复杂结构的方法，并将这些总结应用于目前正在为英国国家应用而开发的降雨模型。所考虑的总结是基于尺度思想，即降雨过程在一个空间或时间尺度上的结构可以与另一个空间或时间尺度上的结构相关联。将考虑两类特殊的汇总度量：1.空间平均雨率(SARR)过程的光谱多尺度。这是一种将在不同空间区域观测到的降雨序列的特性联系起来的方法。例如，100平方公里面积上的每小时平均降雨量序列与10,000平方公里面积上的每小时平均降雨量序列通常看起来非常不同；光谱多尺度描述了这两个序列属性之间的关系。2.不同空间尺度上干湿周期的尺度特征。例如，大空间尺度的旱期可能比小区域的短，因为大区域的某个地方比小区域更有可能下雨。降雨率超过某一阈值的面积(聚集区)所占面积比例与SARR之间的关系。许多以前的工作已经表明，热带地区观测到的降雨量在一系列空间尺度上存在着非常稳定的关系。这项拟议研究的目的之一是，通过对英国天气雷达站和雨量计数据的分析，确定这些关系是否更普遍地适用。如果是这样的话，这将带来令人兴奋的可能性--例如，结果可以用来从粗略得多的观测中重建精细空间或时间尺度的降雨数据。拟议工作的另一个方面是确定这些关系是否可以通过研究人员目前正在开发的降雨模型来再现，该模型目前正在英国全国应用。目前，这类模型的性能通常是使用传统的统计摘要来评估的。拟议的研究使进行更详细的绩效评估成为可能，使用的衡量标准可以说比目前使用的衡量标准更相关。