## 2 Data  
This study harnesses a diverse array of satellite data and in-situ observations from several authoritative sources to construct and calibrate a high-precision ocean temperature prediction model. Central to this research are sea level anomaly (SLA) and sea surface temperature (SST) data, which are sourced from the Copernicus Marine Environment Monitoring Service (CMEMS). The SLA dataset, characterized by a spatial resolution of 0.25\({}^{\circ}\), spans from January 1993 to June 2023, providing over thirty years of critical data necessary for comprehensive sea level trend analysis. This extensive temporal coverage allows for the identification of both short-term fluctuations and long-term trends, which are essential for understanding the dynamic processes governing sea level changes. In parallel, the SST data, with its finer spatial resolution of 0.05\({}^{\circ}\), offers a detailed depiction of sea surface temperature variations, which is crucial for the study of ocean thermal processes, including heat distribution and exchange with the atmosphere. Moreover, the wind field data, which include the u and v components of wind, are derived from the cross-calibrated multi-platform (CCMP) Level 4 dataset. This sophisticated dataset integrates satellite observations with reanalysis data to generate comprehensive global ocean vector wind fields. The multi-source data fusion employed in the CCMP ensures that the wind field data maintain both extensive spatial coverage and high precision. This makes it a vital resource for simulating wind-driven ocean processes, such as upwelling, circulation patterns, and the transportation of heat and nutrients across vast oceanic expanses. The high spatial and temporal resolution of the CCMP dataset is particularly advantageous for capturing the nuanced variations in wind fields, which can have significant impacts on ocean dynamics and subsequently on marine ecosystems and climate. To enhance the accuracy of our model's temperature predictions, we incorporated in-situ observations from Argo floats, which were provided by the French Research Institute for Exploitation of the Sea (IFREMER). These observations include global ocean temperature and salinity profiles, characterized by high spatial and temporal resolution, thereby offering a robust foundation for model calibration. The widespread distribution and continuous observational capabilities of the Argo float network provide invaluable data that are essential for capturing the complex vertical and horizontal structures of the ocean. These data not only inform the model of current oceanic conditions but also enable the accurate simulation of temperature variations and heat content across different ocean layers, from the surface down to the deep ocean. Typhoon data were obtained from the China Meteorological Administration's Tropical Cyclone Data Center, specifically the Satellite Analyzed Tropical Cyclone Dataset (version 3.0). This dataset provides six-hourly updates on the position, intensity, and scale of typhoons in the Northwest Pacific, derived entirely from satellite observations. In this study, data from a total of 135 typhoon events that occurred in China and the Northwest Pacific region from 2011 to 2020 were utilized. Typhoons, as intense meteorological phenomena, have profound impacts on the marine environment, influencing factors such as oceanic heat content, salinity, and even the distribution of marine species. The dataset offers critical insights into the behavior of typhoons, including their formation, trajectory, and the resultant effects on oceanic processes. Understanding these impacts is essential for developing predictive models that can simulate the response of ocean conditions to typhoon events, thereby improving the accuracy of forecasts during extreme weather conditions. Additionally, this study references news report data from the \"China National Knowledge Infrastructure (CNKI)-China Important Newspapers Full-text Database\", covering a period from 1 January 2011 to 31 December 2020.