标题： 海洋-E2E：基于物理和数据驱动的极端海洋热浪端到端神经同化全球预测 显示英文标题

标题： Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation

摘要： 这项工作专注于全球极端海洋热浪（MHWs）的端到端预测，这些是异常温暖的海面温度事件，对海洋生态系统有深远的影响。 准确预测极端MHWs具有重要的科学和经济价值。 然而，现有的方法仍然存在一定的局限性，尤其是在最极端的MHWs方面。 在本研究中，为了解决这些问题，基于MHWs的物理特性，我们创建了一个新颖的混合数据驱动和数值MHWs预测框架Ocean-E2E，该框架能够通过端到端数据同化实现40天的准确MHW预测。 我们的框架通过基于可微分动态核显式建模海洋中尺度平流和海气相互作用的影响，显著提高了极端MHWs的预测能力。 此外， Ocean-E2E能够使用神经数据同化方法进行端到端的MHWs预测和区域高分辨率预测，使我们的框架完全独立于数值模型运行，同时表现出高同化稳定性和准确性，优于当前最先进的海洋数值预报-同化模型。 实验结果表明，所提出的框架在从全球到区域尺度以及从短期到长期预测中表现优异，尤其是在那些最极端的MHWs中。 总体而言，我们的模型为预测和理解MHWs及其他气候极端事件提供了一个框架。 我们的代码可在https://github.com/ChiyodaMomo01/Ocean-E2E获取。 显示英文摘要

摘要： This work focuses on the end-to-end forecast of global extreme marine heatwaves (MHWs), which are unusually warm sea surface temperature events with profound impacts on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel hybrid data-driven and numerical MHWs forecast framework Ocean-E2E, which is capable of 40-day accurate MHW forecasting with end-to-end data assimilation. Our framework significantly improves the forecast ability of extreme MHWs by explicitly modeling the effect of oceanic mesoscale advection and air-sea interaction based on a differentiable dynamic kernel. Furthermore, Ocean-E2E is capable of end-to-end MHWs forecast and regional high-resolution prediction using neural data assimilation approaches, allowing our framework to operate completely independently of numerical models while demonstrating high assimilation stability and accuracy, outperforming the current state-of-the-art ocean numerical forecasting-assimilation models. Experimental results show that the proposed framework performs excellently on global-to-regional scales and short-to-long-term forecasts, especially in those most extreme MHWs. Overall, our model provides a framework for forecasting and understanding MHWs and other climate extremes. Our codes are available at https://github.com/ChiyodaMomo01/Ocean-E2E.