Nowcasting weather with coupled fluid dynamics and machine learning

“临近预报”提供天气预报，主要是在短于6小时的时间尺度上发布高影响天气的警报和警告。在这些时间尺度上，数值天气预报（NWP）模式由于运行缓慢而不能使用，因此预报员通常依靠直接解释观测结果来预测天气状况的演变。通过利用机器学习的最新进展，这项挑战提供了一个机会，可以直接从数据中学习天气的近时间演变，并通过这种方式获得对风暴形成的大气过程的新见解。准确的临近预报工具在提供即将发生的山洪事件的警告方面非常有用，可能提供几个小时的通知，以便采取行动防止生命或财产的风险。该项目的目的是利用实时遥感观测（地面雷达和卫星）提供降水临近预报，以预测未来数小时的降水。卫星观测具有接近全球覆盖的优势，在非洲等世界上数据稀疏的地区尤其有价值，而地面雷达提供更高的空间和时间分辨率观测。该学生将通过将深度学习模型和简单的二维流体动力学模型相结合，开发一种新的临近预测方法，将研究置于新兴的物理信息神经网络领域。降水临近预报系统必须预测风暴的运动和演变。当前的机器学习方法倾向于使用神经网络作为黑盒，并同时学习两个过程，但这通常会导致不切实际的解决方案。在实践中，我们已经对风暴的运动有了相当大的物理洞察力，而基于物理的神经网络提供了一种令人兴奋的新方法，可以整合这些先验知识来改进预测。通过利用二维流体动力学模型来处理风暴的水平平流，神经网络的任务仅是预测风暴的形成和演变。基于现有的研究，假设利用一个单独的平流模型将减轻当代仅使用神经网络的临近预报方法中出现的数值扩散（涂抹）。传输和进化的解耦也将允许在神经网络解决方案中引入基于物理的约束，例如将水平风场约束为近似无散度。

"Nowcasting" provides weather forecasting central to issuing alerts and warnings of high-impact weather on timescales typically shorter than 6 hours. On these timescales numerical weather prediction (NWP) models cannot be utilised due to their slow runtime, and therefore forecasters generally rely on directly interpreting observations to make predictions about the evolution of weather conditions. By utilising recent advances in machine learning this challenge presents an opportunity to learn directly from data the near-time evolution of weather and through this gain new insights into atmospheric processes of storm formation. Accurate nowcasting tools are extremely useful in providing warning of imminent flash flood events, potentially providing a few hours' notice in which to take action to prevent risk to life or property.The aim of the project is to provide precipitation nowcasting using real-time remote sensing observations (ground-based radar and satellite) to predict precipitation for the next hours. Satellite observations have the advantage of near global coverage, particularly valuable in data sparse parts of the world such as Africa, while ground based radar offer higher spatial and temporal resolution observations. The student will develop a novel approach to nowcasting by coupling a deep learning model and a simple 2D fluid dynamics model, placing the research within the realms of emerging field of physics-informed neural networks. A precipitation nowcasting system must predict both the movement and evolution of a storm. Current machine learning approaches to nowcasting tend to use neural network as a black box and learn both the processes together, but this can often lead to solutions which are unrealistically smeared out. In practice we already have considerable physical insight into the motion of storms and physics-informed neural networks provide an exciting new approach to integrating this prior knowledge to improve predictions. By utilising a 2D fluid dynamics model to take care of the horizontal advection of storms, the neural network is tasked with only predicting the formation and evolution of storms. Based on existing research it is hypothesised that utilising a separate advection model will alleviate the numerical diffusion (smearing) seen in contemporary neural network-only approaches to nowcasting. The decoupling of transport and evolution will also allow for the introduction of physics-based constraints on the neural network solution, for example constraining the horizontal wind field to be approximately divergence free.