Articles | Volume 20, issue 4
https://doi.org/10.5194/os-20-1035-2024
https://doi.org/10.5194/os-20-1035-2024
Research article
 | 
28 Aug 2024
Research article |  | 28 Aug 2024

Deep learning for the super resolution of Mediterranean sea surface temperature fields

Claudia Fanelli, Daniele Ciani, Andrea Pisano, and Bruno Buongiorno Nardelli

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Cited articles

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Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faugère, Y., Delepoulle, A., Chelton, D., Dibarboure, G., and Picot, N.: On the resolutions of ocean altimetry maps, Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019, 2019. a
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Bowen, M. M., Emery, W. J., Wilkin, J. L., Tildesley, P. C., Barton, I. J., and Knewtson, R.: Extracting multiyear surface currents from sequential thermal imagery using the maximum cross-correlation technique, J. Atmos. Ocean. Tech., 19, 1665–1676, 2002. a
Bretherton, F. P., Davis, R. E., and Fandry, C.: A technique for objective analysis and design of oceanographic experiments applied to MODE-73, Deep Sea Res., 23, 559–582, 1976. a
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Short summary
Sea surface temperature (SST) is an essential variable to understanding the Earth's climate system, and its accurate monitoring from space is essential. Since satellite measurements are hindered by cloudy/rainy conditions, data gaps are present even in merged multi-sensor products. Since optimal interpolation techniques tend to smooth out small-scale features, we developed a deep learning model to enhance the effective resolution of gap-free SST images over the Mediterranean Sea to address this.