# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 1 Introduction
Nevertheless, in traditional physical oceanography, the inapplicability of geostrophic  
Figure 1: Seasonal mean zonal velocity distributions of the Wyrtki Jet in the equatorial Indian Ocean (5\({}^{\circ}\) S–5\({}^{\circ}\) N, 50\({}^{\circ}\) E-90\({}^{\circ}\) E) from the Ocean Surface Current Analysis Real-time (OSCAR) dataset (1993–2022): (**a**) Spring, (**b**) Summer, (**c**) Autumn, (**d**) Winter. balance relationships near the equatorial regions renders this approach inadequate for accurately assessing sub-mesoscale ocean phenomena, with substantial associated errors. As for the third source, reanalysis model data rely on the traditional dynamic model framework and faces the issue of lag in data updating. Over recent decades, the rapid advancements in ocean satellite remote sensing technology and machine learning (ML) have established a transformative paradigm for marine scientific research. The integration of multi-source satellite observational data with ML algorithms has effectively bridged the spatiotemporal continuity gaps inherent in traditional observational approaches, emerging as a cornerstone technology in contemporary marine dynamics studies. In addition, meteorological data have also laid a foundation for the development of marine artificial intelligence. Due to its comprehensive variables and high resolution, CDMet can be used as input data for hydrological, agricultural, and ecological models [18]. [PERSON] et al. [19] adapted the Berkeley High Resolution product (BEHR-HK) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO\({}_{2}\) in southern China. [PERSON] et al. [20] outlined the process of the Copernicus Climate Change Service's Global Land and Marine Observations Database service in securing data sources. These datasets can be used to evaluate the effect of the pressure changes on marine circulation, residual current, and oceanographic variables [21]. Significant progress has been achieved in regional-scale ocean current inversion studies for specific sea areas. For instance, in the Pacific region, [PERSON] et al. [22] employed the method of the local phase gradient to estimate the spatial currents in bay areas of northern Taiwan. Through comparison with actual measurement data, they confirmed that the local phase gradient method was more suitable than the traditional method for estimating sea currents within bay areas. Additionally, [PERSON] et al. [23] used Himawari-8 data received from the Joint Receiving Station for Satellite Remote Sensing of Xiamen University to retrieve coastal currents in Hangzhou Bay. The results showed that Himawari-8 data can effectively estimate coastal currents in Hangzhou Bay. In the Atlantic region, [PERSON] et al. [24] presented a novel deep learning framework, rooted in a variational data assimilation paradigm, that unlocks new avenues for leveraging the synergistic relationships between satellite-derived sea surface observations, namely sea surface height and sea surface temperature. Concurrently, [PERSON] et al. [25] proposed a novel approach for retrieving sea surface currents (SSCs) through the synergistic merging of current information derived from synthetic aperture radar (SAR) and ocean color imagery, where the current vectors were computed using the maximum cross-correlation (MCC) method. Furthermore, [PERSON] et al. [26] proposed a method to extract prior probability distributions from historical current fields. This approach estimates ocean surface currents using the Ocean Surface Current Analysis Real-time (OSCAR) model in the Gulf Stream region, with validation experiments conducted through simulated DopScat data. [PERSON] et al. [27] implemented a joint inversion scheme combining the normalized radar cross-section (NRCS) and Doppler centroid anomaly (DCA) through a Bayesian method to retrieve sea surface wind and current information. [PERSON] et al. [28] proposed a remote sensing retrieval method for sea surface currents in the Mediterranean Sea.