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

TL;DR

Ocean-E2E is a novel hybrid physics-based and data-driven framework that enables accurate, end-to-end 40-day forecasts of extreme marine heatwaves globally, outperforming existing models by explicitly modeling oceanic and atmospheric interactions.

Contribution

The paper introduces Ocean-E2E, a hybrid neural network framework that combines physical modeling with data-driven approaches for end-to-end marine heatwave forecasting.

Findings

Significantly improves MHW forecast accuracy over existing models.

Capable of regional high-resolution predictions independently of numerical models.

Performs well on global-to-regional scales and short-to-long-term forecasts.

Abstract

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 in forecasting general patterns and extreme events. 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 MHWs by explicitly modeling the effect of oceanic mesoscale advection and air-sea interaction based on a dynamic kernel. Furthermore, Ocean-E2E is capable of end-to-end MHWs forecast and regional high-resolution prediction, allowing our framework to operate completely independently of numerical models while outperforming the current state-of-the-art ocean numerical/AI 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.