Nowcasting with Artificial Intelligence for African Rainfall: NAIAR

利用人工智能预测非洲降雨量：NAIAR

• 批准号: NE/Y000420/1
• 负责人: Christopher Taylor
• 金额: $ 43.86万
• 依托单位: UK CENTRE FOR ECOLOGY & HYDROLOGY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY000420%2F1
• 关键词: Nowcasting; Artificial; Intelligence; African; Rainfall

This project aims to use new digital solutions to create 0 to 6 hour predictions - nowcasting - for tropical storms using satellite data. The methods will be developed and rolled-out for Africa, where people urgently need information about storm hazards, through our existing online platforms and smartphone apps. In this way the results of the research will be used to deliver information on storm hazards to users within minutes. The project very closely addresses the NERC Digital Strategy. Tropical storms are very unpredictable, changing very rapidly - explosively - over timescales of an hour or so. For this reason, predictions are naturally very uncertain. Very often, the most important information people need regarding a storm hazard is what is happening now, and some information about how the storm likely to move and develop in the next couple of hours. This process is called "nowcasting" and in the USA, nowcasting of tornados saves many lives every year. The lack of weather radars in most African countries means that nowcasting is almost completely absent, but we have recently shown that satellite methods can provide useful nowcasting of storms too. The new Meteosat Third Generation (MTG) satellite will provide even better data coverage, from about 2024, at higher frequency and finer spatial scale. There is a tremendous opportunity to innovate in the creation of new nowcasting methods and communicate them to weather services, organisations and the public across Africa.While existing satellite nowcasting methods have some skill, they also have major shortcomings. They work by extrapolating observed patterns forward in time, but are not constrained to obey the laws of physics, and unphysical predictions commonly occur. The most challenging problem in storm nowcasting is to predict the initiation and subsequent development of new storms in future: there is no accepted way to do this, and our considerable knowledge of the physics of initiation is not being exploited. It takes about 30 minutes to generate these nowcasts, and when their accuracy is degrading after an hour or two, their use becomes limited. We aim to create useful 6-hour nowcasts.Nowcasting is an obvious application where new data-science methods, in particular machine-learning (ML), have the potential to make a massive impact, and a number of groups have begun to propose practical solutions. We need fundamental research to understand and improve the performance of these data-driven solutions, on the basis of the underlying physics and fluid-dynamics of storms. For instance, existing methods can extrapolate an image of a storm forward in time using ML to predict its future movement or growth, but the result may grow and be distorted in shape in a way which is incompatible with the laws of physics. These unrealistic predictions are obvious to an experienced forecaster but ordinary users of the data will be vulnerable to the consequences of inaccurate nowcasts. When nowcasts are used to predict hazards such as floods, unphysical solutions could lead to bad decisions.In this project, we aim to combine machine-learning, theoretical fluid dynamics, operational prediction and meteorology, to create innovative approaches to nowcasting of tropical storms. We will develop ML methods which are fast, and which obey physical laws, like the weather prediction models. Our solutions will include statistical forecasts of rainfall probabilities, as well as ensembles of forecast realisations, and an automated evaluation system will be created. Recent advances in physical understanding and the new data offered by MTG, will be used to create statistical nowcasts of storm initiation and its subsequent evolution. We will apply these methods through our existing web-based and mobile-phone communication portals delivering information to Africa, and support colleagues in Africa to exploit the methods locally.

该项目旨在使用新的数字解决方案来创建使用卫星数据的热带风暴创建0到6小时预测的预测。这些方法将用于非洲，并通过我们现有的在线平台和智能手机应用程序迫切需要有关风暴危害的信息。通过这种方式，研究结果将用于在几分钟内向用户提供有关风暴危害的信息。该项目非常紧密地解决了NERC数字策略。热带风暴是非常不可预测的，在一个小时左右的时间里，变化很快 - 爆炸性地变化。因此，预测自然是非常不确定的。通常，人们对风暴危害的最重要信息是现在正在发生的事情，以及有关暴风雨在接下来的几个小时内如何移动和发展的一些信息。这个过程称为“现状”，在美国，龙卷风的现象每年挽救许多生命。在大多数非洲国家，缺乏天气雷达意味着几乎完全不存在现状，但是我们最近表明，卫星方法也可以提供有用的风暴现象。新的MeteoSat第三代（MTG）卫星将以较高的频率和更细的空间尺度提供更好的数据覆盖范围。在创建新的现象方法并将其传达给非洲的天气服务，组织和公众时，有一个巨大的机会来创新。尽管现有的卫星现有方法具有一定的技能，但它们也有重大的缺点。它们通过推断观察到的模式在时间前进而起作用，但不受限制地遵守物理定律，并且通常会发生非物理预测。在风暴现象中，最具挑战性的问题是预测未来新风暴的启动和后续发展：没有公认的方法可以做到这一点，而且我们对启动物理学的大量了解并没有得到利用。生成这些现象大约需要30分钟，当它们的准确性在一两个小时后降解时，它们的使用就会受到限制。我们旨在创建有用的6小时Nowcast.Nowcasting是一个明显的应用程序，其中新的数据科学方法（尤其是机器学习（ML））有可能产生巨大影响，并且许多小组已开始提出实用解决方案。我们需要基本的研究来理解和改善这些数据驱动解决方案的性能，并基于风暴的基本物理和流体动力学。例如，现有方法可以使用ML推断暴风雨的图像，以预测其未来的运动或增长，但是结果可能会增长并以与物理定律不相容的方式增长和变形。这些不切实际的预测对于经验丰富的预报员来说是显而易见的，但是数据的普通用户将容易受到不准确的现象的后果。当现状用于预测洪水等危害时，非物理解决方案可能会导致错误的决定。在该项目中，我们旨在结合机器学习，理论流体动力学，操作预测和气象学，以创建创新的方法来启发热带风暴。我们将开发快速的ML方法，并且像天气预测模型一样遵守物理定律。我们的解决方案将包括降雨概率的统计预测以及预测实现的集合，并将创建自动化评估系统。物理理解的最新进展和MTG提供的新数据将用于创建风暴启动及其随后的演变的统计。我们将通过我们现有的基于Web的手机通信门户应用这些方法，向非洲提供信息，并支持非洲的同事在当地利用这些方法。