BIG data methods for improving windstorm FOOTprint prediction (BigFoot)

风暴会对财产和基础设施造成巨大破坏。风暴足迹（3天内最大阵风速度的地图）是对与保险业和基础设施提供商密切相关的危险的重要总结。传统上，风暴足迹是根据气象数据和数值天气模式分析估计的。然而，有几个有趣的结构化程度较低的数据源可能有助于估计风暴足迹，更重要的是将提高我们估计的空间分辨率。这一点很重要，因为有一些重要的小尺度气象现象，如刺痛射流，目前还没有很好地解决目前的方法。我们建议利用三个额外的数据源（在项目过程中可能还有其他数据源）。目前确定的三个来源是通过气象局天气观测网站（WOW）获得的业余观测，社交媒体上的评论以及社交媒体或CCTV上录制的视频。业余气象观测目前由气象局收集，但不用于产生足迹估计。我们将研究是否可以使用它们来估计风暴足迹;一个有用的副产品将是每个WOW站的不确定性估计。社交媒体，如推特或Instagram，经常包含对风暴的评论。这些内容包括对风有多大的评论，以及风暴造成的损失的报告。在某些情况下，消息的地理位置由设备提供，但在其他情况下，它必须被推断。每天在社交媒体上发布的信息数量非常多，应该可以使用这些信息来提供更详细的足迹模型。除了文字，社交媒体还记录图像和视频。还以闭路电视的形式广泛录制视频。比如说，树木在风中摇曳的视频记录包含了风暴强度的信息。我们会分析这些纪录，以提供有关风速和阵风速度的资料。汇集大量不同的数据是一个复杂的过程。我们将开发，测试和比较现代数据科学中的两种方法：统计过程建模和机器学习。这两种方法的目标都是将所有数据综合成风暴足迹的估计值（及其相关的不确定性）。前者将专注于根据最大阵风速度生成更像当前估计的地图，而后者基于数据的方法将更专注于绘制风暴造成的损失。一旦我们从社交媒体和建模中获得了风暴足迹的估计值，我们将把它们与标准产品进行比较，并与利益相关者协商，确定任何改进。

Wind storms can cause great damage to property and infrastructure. The windstorm footprint (a map of maximum wind gust speed over 3 days) is an important summary of the hazard of great relevance to the insurance industry and to infrastructure providers. Windstorm footprints are conventionally estimated from meteorological data and numerical weather model analyses. However there are several interesting less structured data sources that could contribute to the estimation of the wind storm footprints, and more importantly will raise the spatial resolution of our estimates. This is important as there are important small-scale meteorological phenomena, such as sting jets, that are currently not well resolved by the current methods. We propose to exploit three additional sources of data (and possibly others during the course of the project). The three sources so far identified identified are amateur observations available through the Met Office weather observations website (WOW), comments made on social media and video recorded on social media or CCTV. Amateur meteorological observations are currently collected by the Met Office but not used in producing the footprint estimates. We will investigate whether we can use them in the estimation of the storm footprint; a useful by-product will be estimates of the uncertainty for each WOW station. Social media, such as twitter or instagram, often contains comments on windstorms. These can range from comments on how windy it is, to reports of damage produced by storms. In some cases the geographical location of the message is provided by the device but in others it has to be inferred. There are very large numbers of messages posted on social media every day and it should be possible to used these to provide more detailed modelling of footprints. In addition to text, social media also records images and video. Video is also recorded extensively in the form of CCTV. Video recordings of trees, say, blowing in the wind include information on the strength of the windstorm. We will analyse such recordings to produce information on wind velocity and gust velocity. Bringing together large quantities of diverse data is a complex procedure. We will develop, test, and compare two approaches in modern data science: statistical process modelling and machine learning. Both methods will aim to synthesise all the data into an estimate of the windstorm footprint (and its associated uncertainty). The former will concentrate on producing a map more like the current estimates based on the maximum gust speed while the latter data based methods will concentrate more on mapping the damage caused by the storm. Once we have estimates of the windstorm footprint from both social media and the modelling we will compare these with the standard products and, in consultation with stakeholder, establish any improvements.