Articles | Volume 20, issue 2
https://doi.org/10.5194/os-20-417-2024
© 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-20-417-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2024
Research article |
| 22 Mar 2024
| 22 Mar 2024
Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Giuliano Galimberti
Department of Statistical Sciences, University of Bologna, Bologna, Italy
Simona Masina
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Ronan McAdam
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Emanuela Clementi
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Viewed
Total article views: 4,120 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Aug 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,035 | 993 | 92 | 4,120 | 147 | 82 | 96 |
- HTML: 3,035
- PDF: 993
- XML: 92
- Total: 4,120
- Supplement: 147
- BibTeX: 82
- EndNote: 96
Total article views: 3,254 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Mar 2024)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,572 | 625 | 57 | 3,254 | 87 | 63 | 72 |
- HTML: 2,572
- PDF: 625
- XML: 57
- Total: 3,254
- Supplement: 87
- BibTeX: 63
- EndNote: 72
Total article views: 866 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Aug 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 463 | 368 | 35 | 866 | 60 | 19 | 24 |
- HTML: 463
- PDF: 368
- XML: 35
- Total: 866
- Supplement: 60
- BibTeX: 19
- EndNote: 24
Viewed (geographical distribution)
Total article views: 4,120 (including HTML, PDF, and XML)
Thereof 3,960 with geography defined
and 160 with unknown origin.
Total article views: 3,254 (including HTML, PDF, and XML)
Thereof 3,126 with geography defined
and 128 with unknown origin.
Total article views: 866 (including HTML, PDF, and XML)
Thereof 834 with geography defined
and 32 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- What makes a marine heatwave forecast useable, useful and used? C. Spillman et al. 10.1016/j.pocean.2025.103464
- Multi-Dilated Convolutional LSTM With U-Net for Global Sea Surface Temperature Forecasting M. Janmaijaya et al. 10.1109/ACCESS.2024.3486914
- Marine heatwaves in the Mediterranean Sea: a convolutional neural network study for extreme event prediction A. Parasyris et al. 10.5194/os-21-897-2025
- Promoting best practices in ocean forecasting through an Operational Readiness Level E. Alvarez Fanjul et al. 10.3389/fmars.2024.1443284
- A Machine Learning-Based Bias Correction Scheme for the All-Sky Assimilation of AGRI Infrared Radiances in a Regional OSSE Framework X. Zhang et al. 10.1109/TGRS.2024.3427434
- Factors Influencing Endangered Marine Species in the Mediterranean Sea: An Analysis Based on IUCN Red List Criteria Using Statistical and Soft Computing Methodologies D. Klaoudatos et al. 10.3390/environments11070151
- Decentralized control strategies with predictive disturbance rejection for OC-OTEC plant in Lakshadweep using deep learning S. Sutha et al. 10.1016/j.jwpe.2024.105539
- Estimating the importance of environmental factors influencing the urban heat island for urban areas in Greece. A machine learning approach I. Petrou & P. Kassomenos 10.1016/j.jenvman.2024.122255
- Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study E. Azmi et al. 10.1016/j.ecoinf.2025.103306
- An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling M. Minaei et al. 10.1007/s11356-024-33790-0
- Trends and variability of marine heatwaves in Portuguese coastal waters M. Monteiro et al. 10.1016/j.scitotenv.2025.179161
- An Informer-based prediction model for extensive spatiotemporal prediction of sea surface temperature and marine heatwave in Bohai Sea J. He et al. 10.1016/j.jmarsys.2024.104037
- TL-iTransformer: Revolutionizing sea surface temperature prediction through iTransformer and transfer learning W. Jia et al. 10.1007/s12145-024-01436-x
- Extreme marine heatwave linked to mass fish kill in the Red Sea M. Tietbohl et al. 10.1016/j.scitotenv.2025.179073
- Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea G. Bonino et al. 10.5194/os-20-417-2024
14 citations as recorded by crossref.
- What makes a marine heatwave forecast useable, useful and used? C. Spillman et al. 10.1016/j.pocean.2025.103464
- Multi-Dilated Convolutional LSTM With U-Net for Global Sea Surface Temperature Forecasting M. Janmaijaya et al. 10.1109/ACCESS.2024.3486914
- Marine heatwaves in the Mediterranean Sea: a convolutional neural network study for extreme event prediction A. Parasyris et al. 10.5194/os-21-897-2025
- Promoting best practices in ocean forecasting through an Operational Readiness Level E. Alvarez Fanjul et al. 10.3389/fmars.2024.1443284
- A Machine Learning-Based Bias Correction Scheme for the All-Sky Assimilation of AGRI Infrared Radiances in a Regional OSSE Framework X. Zhang et al. 10.1109/TGRS.2024.3427434
- Factors Influencing Endangered Marine Species in the Mediterranean Sea: An Analysis Based on IUCN Red List Criteria Using Statistical and Soft Computing Methodologies D. Klaoudatos et al. 10.3390/environments11070151
- Decentralized control strategies with predictive disturbance rejection for OC-OTEC plant in Lakshadweep using deep learning S. Sutha et al. 10.1016/j.jwpe.2024.105539
- Estimating the importance of environmental factors influencing the urban heat island for urban areas in Greece. A machine learning approach I. Petrou & P. Kassomenos 10.1016/j.jenvman.2024.122255
- Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study E. Azmi et al. 10.1016/j.ecoinf.2025.103306
- An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling M. Minaei et al. 10.1007/s11356-024-33790-0
- Trends and variability of marine heatwaves in Portuguese coastal waters M. Monteiro et al. 10.1016/j.scitotenv.2025.179161
- An Informer-based prediction model for extensive spatiotemporal prediction of sea surface temperature and marine heatwave in Bohai Sea J. He et al. 10.1016/j.jmarsys.2024.104037
- TL-iTransformer: Revolutionizing sea surface temperature prediction through iTransformer and transfer learning W. Jia et al. 10.1007/s12145-024-01436-x
- Extreme marine heatwave linked to mass fish kill in the Red Sea M. Tietbohl et al. 10.1016/j.scitotenv.2025.179073
Latest update: 08 Aug 2025
Short summary
This study employs machine learning to predict marine heatwaves (MHWs) in the Mediterranean Sea. MHWs have far-reaching impacts on society and ecosystems. Using data from ESA and ECMWF, the research develops accurate prediction models for sea surface temperature (SST) and MHWs across the region. Notably, machine learning methods outperform existing forecasting systems, showing promise in early MHW predictions. The study also highlights the importance of solar radiation as a predictor of SST.
This study employs machine learning to predict marine heatwaves (MHWs) in the Mediterranean Sea....
