### Reanalysis Data  
Reanalysis data combines observational data with numerical model outputs using advanced data assimilation techniques to produce gridded ocean datasets [55; 56]. By regularly correcting models with newly ingested measurements, reanalysis data minimizes discrepancies between simulations and reality, ensuring more accurate and reliable results. These data products are essential resources for studying large-scale ocean dynamics, climate trends, and long-term variability. Several widely used reanalysis datasets include:  
* ERA5: The fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), known as ERA5, integrates satellite observations, in situ measurements, and numerical model analyses to produce high-quality datasets [57]. ERA5 offers data at varying spatial and temporal resolutions, such as sea surface wind speed at 0.25\({}^{\circ}\) resolution and wave height at 0.5\({}^{\circ}\) resolution, with an hourly time step [58; 59]. This comprehensive dataset supports a wide range of applications in meteorology, oceanography, and climate research. * SODA: The Simple Ocean Data Assimilation (SODA) dataset, developed by the University of Maryland, combines historical satellite observations, buoy data, ship-based measurements, and other observations with physical ocean models, providing high-resolution, three-dimensional representations of ocean states. This dataset is widely used for studying long-term oceanographic changes and validating global climate models [60]. * GODAS: Produced by the U.S. National Centers for Environmental Prediction, Global Ocean Data Assimilation System (GODAS) integrates observational data with numerical models to generate high-resolution gridded datasets of oceanic variables, such as temperature, salinity, and current [61]. This reanalysis data focus on seasonal forecasting, especially the prediction of El Nino-Southern Oscillation (ENSO), making it a crucial tool for climate research and weather prediction [62]. * OISST: The Optimum Interpolation Sea Surface Temperature (OISST) dataset, provided by the U.S. National Centers for Environmental Information (NCEI), integrates in situ observations with satellite measurements and applies an interpolation algorithm to create consistent, gap-filled SST fields. These datasets are essential for analyzing SST trends, monitoring marine heatwaves (MHWs), and supporting climate predictions [63; 64]. * GLORYS: Developed by the European Copernicus Marine Environment Monitoring Service (CMEMS), Global Ocean Reanalysis and Simulation (GLORYS) reanalysis products provide global datasets with a high spatial resolution of 1/12\({}^{\circ}\) (approximately 8 km). These datasets include variables for currents, sea level, temperature, salinity, mixed layer depth, and ice parameters, making them valuable for oceanographic and climate studies [65]. Different types of oceanographic datasets, such as satellite remote sensing, in situ observations, and reanalysis products, are suitable for different DL tasks. For example, satellite remote sensing data are ideal for tasks that require broad spatial coverage and frequent observations, such as SST prediction and sea ice classification. In situ observationsare well suited for ocean surface pH estimation, and reanalysis data are commonly used in global-scale studies and long-term forecasting tasks, such as global sea surface wind speed and wave height estimation. Different data sources also bring distinct advantages and limitations that affect the accuracies and generalizations of DL models. For example, satellite data offer broad spatial coverage, which enables DL models to capture large-scale oceanographic patterns. However, sensors like MODIS suffer from missing values due to cloud cover or atmospheric interference, which can degrade model performance, especially for fine-resolution classification or detection.