NeurOST-SSH-SST 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 L3 SSH + L4 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