  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 the
ocean-specific block for effectively capturing both local ocean information and
global teleconnection. Extensive experiments are conducted under satellite
observations, in situ observations, and the IV-TT Class 4 evaluation framework
of the world's leading operational GOFSs from January 2019 to December 2020.
The results demonstrate that XiHe achieves stronger forecast performance in all
testing variables than existing leading operational numerical GOFSs including
Mercator Ocean Physical SYstem (PSY4), Global Ice Ocean Prediction System
(GIOPS), BLUElinK OceanMAPS (BLK), and Forecast Ocean Assimilation Model
(FOAM). Particularly, the accuracy of ocean current forecasting of XiHe out to
60 days is even better than that of PSY4 in just 10 days. Additionally, XiHe is
able to forecast the large-scale circulation and the mesoscale eddies.
Furthermore, it can make a 10-day forecast in only 0.36 seconds, which
accelerates the forecast speed by thousands of times compared to the
traditional numerical GOFSs.
