Articles | Volume 20, issue 4
https://doi.org/10.5194/os-20-1035-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-20-1035-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Aug 2024
Research article |
| 28 Aug 2024
| 28 Aug 2024
Deep learning for the super resolution of Mediterranean sea surface temperature fields
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Calata Porta di Massa, 80133 Naples, Italy
Daniele Ciani
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Andrea Pisano
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Bruno Buongiorno Nardelli
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Calata Porta di Massa, 80133 Naples, Italy
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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.
Sea surface temperature (SST) is an essential variable to understanding the Earth's climate...
