XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting
Xiang Wang, Renzhi Wang, Ningzi Hu, Pinqiang Wang, Peng Huo, Guihua, Wang, Huizan Wang, Senzhang Wang, Junxing Zhu, Jianbo Xu, Jun Yin, Senliang, Bao, Ciqiang Luo, Ziqing Zu, Yi Han, Weimin Zhang, Kaijun Ren, Kefeng Deng,, Junqiang Song

TL;DR
XiHe is a novel data-driven global ocean forecasting model that uses transformer architecture to achieve higher accuracy and speed than traditional physics-based models, effectively capturing ocean dynamics at high resolution.
Contribution
The paper introduces XiHe, the first data-driven 1/12° resolution global ocean eddy-resolving forecast model, outperforming existing numerical models in accuracy and speed.
Findings
XiHe surpasses leading operational GOFSs in forecast accuracy.
XiHe forecasts ocean currents more accurately up to 60 days.
XiHe accelerates forecasting speed by thousands of times.
Abstract
The leading operational Global Ocean Forecasting Systems (GOFSs) use physics-driven numerical forecasting models that solve the partial differential equations with expensive computation. Recently, specifically in atmosphere weather forecasting, data-driven models have demonstrated significant potential for speeding up environmental forecasting by orders of magnitude, but there is still no data-driven GOFS that matches the forecasting accuracy of the numerical GOFSs. In this paper, we propose the first data-driven 1/12{\deg} resolution global ocean eddy-resolving forecasting model named XiHe, which is established from the 25-year France Mercator Ocean International's daily GLORYS12 reanalysis data. XiHe is a hierarchical transformer-based framework coupled with two special designs. One is the land-ocean mask mechanism for focusing exclusively on the global ocean circulation. The other is…
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Taxonomy
TopicsOceanographic and Atmospheric Processes · Reservoir Engineering and Simulation Methods
