NeurOST-SSH Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE

Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'. Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main These maps were made using only L3 SSH (not including SST). NeurOST citations: Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059 Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589本数据集包含利用NeurOST生成的全球海面高度（Sea Surface Height, SSH）与地转表层海流地图。NeurOST是一种基于星下点卫星测高数据与海面温度（Sea Surface Temperature, SST）生成海面高度格网的深度学习方法，其生成服务于2023a_SSH_mapping_OSE海洋数据挑战赛所设定的观测系统试验。 海洋数据挑战赛相关页面：https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main 本次生成的海面高度地图仅采用了L3级海面高度（L3 SSH）数据，未纳入海面温度数据。 NeurOST相关引用文献： Martin, S. A., Manucharyan, G. E., 及 Klein, P. (2024). 深度学习改进全球海洋涡动动力学卫星观测. 《地球物理研究快报》, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059 Martin, S. A., Manucharyan, G. E., 及 Klein, P. (2023). 利用深度学习合成海面温度与卫星测高观测数据，提升格网海面高度距平的精度与分辨率. 《地球系统建模进展期刊》, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589