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/Y000331/1
• 负责人: Douglas Parker
• 金额: $ 71.89万
• 依托单位: University of Leeds
• 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%2FY000331%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.

该项目旨在利用新的数字解决方案，利用卫星数据对热带风暴进行 0 至 6 小时的预测（临近预报）。这些方法将为非洲开发和推广，那里的人们迫切需要通过我们现有的在线平台和智能手机应用程序获得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。该项目非常密切地关注 NERC 数字战略。热带风暴非常难以预测，在一个小时左右的时间尺度内变化非常迅速——爆炸性的。因此，预测自然是非常不确定的。通常，人们需要的有关风暴危害的最重要信息是现在正在发生的情况，以及有关风暴在未来几个小时内可能如何移动和发展的一些信息。这个过程称为“临近预报”，在美国，龙卷风临近预报每年可以挽救许多人的生命。大多数非洲国家缺乏气象雷达，意味着几乎完全不存在临近预报，但我们最近表明，卫星方法也可以提供有用的风暴临近预报。从 2024 年左右开始，新的气象卫星第三代 (MTG) 卫星将以更高的频率和更精细的空间尺度提供更好的数据覆盖范围。在创建新的临近预报方法并将其传达给整个非洲的气象服务机构、组织和公众方面存在着巨大的机会。虽然现有的卫星临近预报方法具有一定的技巧，但它们也存在重大缺陷。它们的工作原理是及时推断观察到的模式，但不受物理定律的约束，并且通常会发生非物理预测。风暴临近预报中最具挑战性的问题是预测未来新风暴的产生和随后的发展：没有公认的方法可以做到这一点，而且我们对引发物理学的大量知识也没有被利用。生成这些即时预报大约需要 30 分钟，当它们的准确性在一两个小时后下降时，它们的使用就会受到限制。我们的目标是创建有用的 6 小时即时预报。即时预报是一种明显的应用，其中新的数据科学方法，特别是机器学习 (ML)，有可能产生巨大影响，并且许多团体已经开始提出实用的建议解决方案。我们需要基础研究来理解和提高这些数据驱动解决方案的性能，以风暴的基本物理和流体动力学为基础。例如，现有方法可以使用机器学习及时推断风暴的图像，以预测其未来的运动或增长，但结果可能会增长并以与物理定律不相容的方式扭曲。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，但数据的普通用户将很容易受到不准确的临近预报的后果的影响。当使用临近预报来预测洪水等灾害时，非物理解决方案可能会导致错误的决策。在这个项目中，我们的目标是将机器学习、理论流体动力学、业务预测和气象学结合起来，创建热带风暴临近预报的创新方法。我们将开发快速且遵守物理定律的机器学习方法，例如天气预报模型。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，并将创建一个自动评估系统。物理理解的最新进展和 MTG 提供的新数据将用于创建风暴爆发及其后续演变的统计即时预报。我们将通过现有的网络和移动电话通信门户应用这些方法，向非洲提供信息，并支持非洲同事在当地利用这些方法。