Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5549-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5549-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Sep 2025
Model description paper |
| 04 Sep 2025
| 04 Sep 2025
CRITER 1.0: a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data
Matjaž Zupančič Muc
CORRESPONDING AUTHOR
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Vitjan Zavrtanik
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Alexander Barth
Department of Astrophysics, Geophysics and Oceanography, Geohydrodynamics and Environment Research, University of Liège, Liège, Belgium
Aida Alvera-Azcarate
Department of Astrophysics, Geophysics and Oceanography, Geohydrodynamics and Environment Research, University of Liège, Liège, Belgium
Matjaž Ličer
Slovenian Environment Agency, Office for Meteorology, Hydrology and Oceanography, Ljubljana, Slovenia
National Institute of Biology, Marine Biology Station, Piran, Slovenia
Matej Kristan
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
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Amirhossein Barzandeh, Matjaž Ličer, Marko Rus, Matej Kristan, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, and Rivo Uiboupin
Ocean Sci., 21, 1315–1327, https://doi.org/10.5194/os-21-1315-2025, https://doi.org/10.5194/os-21-1315-2025, 2025
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We evaluated a deep-learning model, HIDRA2, for predicting sea levels along the Estonian coast and compared it to traditional numerical models. HIDRA2 performed better overall, offering faster forecasts and valuable uncertainty estimates using ensemble predictions.
Marko Rus, Matjaž Ličer, and Matej Kristan
EGUsphere, https://doi.org/10.5194/egusphere-2025-3187, https://doi.org/10.5194/egusphere-2025-3187, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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This paper introduces HIDRA-D, a novel deep-learning model for dense, gridded sea level forecasting from sparse satellite altimetry and tide gauge data. By forecasting low-frequency spatial components, HIDRA-D offers a faster alternative to traditional numerical models. Evaluated in the Adriatic Sea, it outperforms the NEMO general circulation model, reducing the mean absolute error by 28.0 %. The model is robust but shows limitations in complex coastal areas.
Aida Alvera-Azcárate, Dimitry Van der Zande, Alexander Barth, Antoine Dille, Joppe Massant, and Jean-Marie Beckers
Ocean Sci., 21, 787–805, https://doi.org/10.5194/os-21-787-2025, https://doi.org/10.5194/os-21-787-2025, 2025
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This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.
Bayoumy Mohamed, Alexander Barth, Dimitry Van der Zande, and Aida Alvera-Azcárate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1578, https://doi.org/10.5194/egusphere-2025-1578, 2025
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We quantified the role of climate change and internal variability on marine heatwaves (MHWs) in the North Sea over more than four decades (1982–2024). A key finding is the 2013 climate shift, which was associated with increased warming and MHWs. Long-term warming accounted for 80 % of the observed trend in MHW frequency. The most intense MHW event in May 2024 was attributed to an anomalous anticyclonic atmospheric circulation. We also explored the impact of MHWs on chlorophyll concentrations.
Cécile Pujol, Alexander Barth, Iván Pérez-Santos, Pamela Muñoz-Linford, and Aida Alvera-Azcárate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1421, https://doi.org/10.5194/egusphere-2025-1421, 2025
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Marine heatwaves and cold spells are periods of extreme sea temperatures. This study focuses on Chilean Northern Patagonia, a fjord region vulnerable due to its aquaculture. It aims to understand these events' distribution and identify the most affected basins. Results show higher intensity in enclosed areas like Reloncaví Sound and Puyuhuapi Fjord. Marine heatwaves are becoming more frequent over time, while cold spells are decreasing.
Ehsan Mehdipour, Hongyan Xi, Alexander Barth, Aida Alvera-Azcárate, Adalbert Wilhelm, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-112, https://doi.org/10.5194/egusphere-2025-112, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
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HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers
Ocean Sci., 20, 1567–1584, https://doi.org/10.5194/os-20-1567-2024, https://doi.org/10.5194/os-20-1567-2024, 2024
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Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observations of the chlorophyll a concentration in the Black Sea. Rather than giving a single possible reconstructed field, the discussed method provides an ensemble of possible reconstructions using a generative neural network. The resulting ensemble is validated using techniques from numerical weather prediction and ocean modelling.
Manal Hamdeno, Aida Alvera-Azcárate, George Krokos, and Ibrahim Hoteit
Ocean Sci., 20, 1087–1107, https://doi.org/10.5194/os-20-1087-2024, https://doi.org/10.5194/os-20-1087-2024, 2024
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Our study focuses on the characteristics of MHWs in the Red Sea during the last 4 decades. Using satellite-derived sea surface temperatures (SSTs), we found a clear warming trend in the Red Sea since 1994, which has intensified significantly since 2016. This SST rise was associated with an increase in the frequency and days of MHWs. In addition, a correlation was found between the frequency of MHWs and some climate modes, which was more pronounced in some years of the study period.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
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We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Pamela Linford, Iván Pérez-Santos, Paulina Montero, Patricio A. Díaz, Claudia Aracena, Elías Pinilla, Facundo Barrera, Manuel Castillo, Aida Alvera-Azcárate, Mónica Alvarado, Gabriel Soto, Cécile Pujol, Camila Schwerter, Sara Arenas-Uribe, Pilar Navarro, Guido Mancilla-Gutiérrez, Robinson Altamirano, Javiera San Martín, and Camila Soto-Riquelme
Biogeosciences, 21, 1433–1459, https://doi.org/10.5194/bg-21-1433-2024, https://doi.org/10.5194/bg-21-1433-2024, 2024
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The Patagonian fjords comprise a world region where low-oxygen water and hypoxia conditions are observed. An in situ dataset was used to quantify the mechanism involved in the presence of these conditions in northern Patagonian fjords. Water mass analysis confirmed the contribution of Equatorial Subsurface Water in the advection of the low-oxygen water, and hypoxic conditions occurred when the community respiration rate exceeded the gross primary production.
Francesca Doglioni, Robert Ricker, Benjamin Rabe, Alexander Barth, Charles Troupin, and Torsten Kanzow
Earth Syst. Sci. Data, 15, 225–263, https://doi.org/10.5194/essd-15-225-2023, https://doi.org/10.5194/essd-15-225-2023, 2023
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This paper presents a new satellite-derived gridded dataset, including 10 years of sea surface height and geostrophic velocity at monthly resolution, over the Arctic ice-covered and ice-free regions, up to 88° N. We assess the dataset by comparison to independent satellite and mooring data. Results correlate well with independent satellite data at monthly timescales, and the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
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We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Nydia Catalina Reyes Suárez, Valentina Tirelli, Laura Ursella, Matjaž Ličer, Massimo Celio, and Vanessa Cardin
Ocean Sci., 18, 1321–1337, https://doi.org/10.5194/os-18-1321-2022, https://doi.org/10.5194/os-18-1321-2022, 2022
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Explaining the dynamics of jellyfish blooms is a challenge for scientists. Biological and meteo-oceanographic data were combined on different timescales to explain the exceptional bloom of the jellyfish Rhizostoma pulmo in the Gulf of Trieste (Adriatic Sea) in April 2021. The bloom was associated with anomalously warm seasonal sea conditions. Then, a strong bora wind event enhanced upwelling and mixing of the water column, causing jellyfish to rise to the surface and accumulate along the coast.
Begoña Pérez Gómez, Ivica Vilibić, Jadranka Šepić, Iva Međugorac, Matjaž Ličer, Laurent Testut, Claire Fraboul, Marta Marcos, Hassen Abdellaoui, Enrique Álvarez Fanjul, Darko Barbalić, Benjamín Casas, Antonio Castaño-Tierno, Srđan Čupić, Aldo Drago, María Angeles Fraile, Daniele A. Galliano, Adam Gauci, Branislav Gloginja, Víctor Martín Guijarro, Maja Jeromel, Marcos Larrad Revuelto, Ayah Lazar, Ibrahim Haktan Keskin, Igor Medvedev, Abdelkader Menassri, Mohamed Aïssa Meslem, Hrvoje Mihanović, Sara Morucci, Dragos Niculescu, José Manuel Quijano de Benito, Josep Pascual, Atanas Palazov, Marco Picone, Fabio Raicich, Mohamed Said, Jordi Salat, Erdinc Sezen, Mehmet Simav, Georgios Sylaios, Elena Tel, Joaquín Tintoré, Klodian Zaimi, and George Zodiatis
Ocean Sci., 18, 997–1053, https://doi.org/10.5194/os-18-997-2022, https://doi.org/10.5194/os-18-997-2022, 2022
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This description and mapping of coastal sea level monitoring networks in the Mediterranean and Black seas reveals the existence of 240 presently operational tide gauges. Information is provided about the type of sensor, time sampling, data availability, and ancillary measurements. An assessment of the fit-for-purpose status of the network is also included, along with recommendations to mitigate existing bottlenecks and improve the network, in a context of sea level rise and increasing extremes.
Emma Reyes, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Vanessa Cardin, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Maria J. Fernandes, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Pablo Lorente, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Adèle Révelard, Catalina Reyes-Suárez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Alejandro Orfila
Ocean Sci., 18, 797–837, https://doi.org/10.5194/os-18-797-2022, https://doi.org/10.5194/os-18-797-2022, 2022
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This work reviews the existing advanced and emerging scientific and societal applications using HFR data, developed to address the major challenges identified in Mediterranean coastal waters organized around three main topics: maritime safety, extreme hazards and environmental transport processes. It also includes a discussion and preliminary assessment of the capabilities of existing HFR applications, finally providing a set of recommendations towards setting out future prospects.
Pablo Lorente, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Alejandro Orfila, Adèle Révelard, Emma Reyes, Jorge Sánchez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Laura Ursella, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Vanessa Cardin
Ocean Sci., 18, 761–795, https://doi.org/10.5194/os-18-761-2022, https://doi.org/10.5194/os-18-761-2022, 2022
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High-frequency radar (HFR) is a land-based remote sensing technology that can provide maps of the surface circulation over broad coastal areas, along with wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network as well as present and future applications of this sensor for societal benefit such as search and rescue operations, safe vessel navigation, tracking of marine pollutants, and the monitoring of extreme events.
Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, https://doi.org/10.5194/gmd-15-2183-2022, 2022
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Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Malek Belgacem, Katrin Schroeder, Alexander Barth, Charles Troupin, Bruno Pavoni, Patrick Raimbault, Nicole Garcia, Mireno Borghini, and Jacopo Chiggiato
Earth Syst. Sci. Data, 13, 5915–5949, https://doi.org/10.5194/essd-13-5915-2021, https://doi.org/10.5194/essd-13-5915-2021, 2021
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The Mediterranean Sea exhibits an anti-estuarine circulation, responsible for its low productivity. Understanding this peculiar character is still a challenge since there is no exact quantification of nutrient sinks and sources. Because nutrient in situ observations are generally infrequent and scattered in space and time, climatological mapping is often applied to sparse data in order to understand the biogeochemical state of the ocean. The dataset presented here partly addresses these issues.
Estrella Olmedo, Verónica González-Gambau, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Marilaure Gregoire, Aida Álvera-Azcárate, Luminita Buga, and Marie-Hélène Rio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-364, https://doi.org/10.5194/essd-2021-364, 2021
Revised manuscript not accepted
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We present the first dedicated satellite salinity product in the Black Sea. We use the measurements provided by the European Soil Moisture and Ocean Salinity mission. We introduce enhanced algorithms for dealing with the contamination produced by the Radio Frequency Interferences that strongly affect this basin. We also provide a complete quality assessment of the new product and give an estimated accuracy of it.
Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 14, 2057–2074, https://doi.org/10.5194/gmd-14-2057-2021, https://doi.org/10.5194/gmd-14-2057-2021, 2021
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Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
Cited articles
Alvera-Azcárate, A., Barth, A., Rixen, M., and Beckers, J.: Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature, Ocean Model., 9, 325–346, https://doi.org/10.1016/j.ocemod.2004.08.001, 2005. a, b
Barth, A., Alvera-Azcárate, A., Troupin, C., and Beckers, J.-M.: DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations, Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, 2022. a, b, c, d, e, f, g, h, i
Barth, A., Brajard, J., Alvera-Azcárate, A., Mohamed, B., Troupin, C., and Beckers, J.-M.: Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea, Ocean Sci., 20, 1567–1584, https://doi.org/10.5194/os-20-1567-2024, 2024. a
Beauchamp, M., Febvre, Q., Georgenthum, H., and Fablet, R.: 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry, Geosci. Model Dev., 16, 2119–2147, https://doi.org/10.5194/gmd-16-2119-2023, 2023. a
Bishop, S. P., Small, R. J., Bryan, F. O., and Tomas, R. A.: Scale Dependence of Midlatitude Air–Sea Interaction, J. Climate, 30, 8207–8221, https://doi.org/10.1175/JCLI-D-17-0159.1, 2017. a
Buongiorno Nardelli, B., Cavaliere, D., Charles, E., and Ciani, D.: Super-resolving ocean dynamics from space with computer vision algorithms, Remote Sens., 14, 1159, https://doi.org/10.3390/rs14051159, 2022. a, b
Casey, K., Brandon, T., Cornillon, P., and Evans, R.: The Past, Present and Future of the AVHRR Pathfinder SST Program, in: Oceanography from Space: Revisited, edited by: Barale, V., Gower, J., and Alberotanza, L., Springer, https://doi.org/10.1007/978-90-481-8681-5_16, 2010. a
Chelton, D. B.: The Impact of SST Specification on ECMWF Surface Wind Stress Fields in the Eastern Tropical Pacific, J. Climate, 18, 530–550, https://doi.org/10.1175/JCLI-3275.1, 2005. a
Darmaraki, S., Somot, S., Sevault, F., and Nabat, P.: Past variability of Mediterranean Sea marine heatwaves, Geophys. Res. Lett., 46, 9813–9823, 2019. a
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.: An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale, in: International Conference on Learning Representations, https://openreview.net/forum?id=YicbFdNTTy (last access: 26 January 2025), 2021. a, b, c, d
E.U. Copernicus Marine Service Information (CMEMS): Mediterranean Sea – High Resolution and Ultra High Resolution L3S Sea Surface Temperature, CEMS [data set], https://doi.org/10.48670/moi-00171, 2023a. a, b
E.U. Copernicus Marine Service Information (CMEMS): European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations, CEMS [data set], https://doi.org/10.48670/moi-00310, 2023b. a, b
Fablet, R., Beauchamp, M., Drumetz, L., and Rousseau, F.: Joint interpolation and representation learning for irregularly sampled satellite-derived geophysical fields, Frontiers in Applied Mathematics and Statistics, 7, 655224, https://doi.org/10.3389/fams.2021.655224, 2021. a, b
Fanelli, C., Ciani, D., Pisano, A., and Buongiorno Nardelli, B.: Deep learning for the super resolution of Mediterranean sea surface temperature fields, Ocean Sci., 20, 1035–1050, https://doi.org/10.5194/os-20-1035-2024, 2024. a, b, c
Garcia-Soto, C., Cheng, L., Caesar, L., Schmidtko, S., Jewett, E. B., Cheripka, A., Rigor, I., Caballero, A., Chiba, S., Báez, J. C., Zielinski, T., and Abraham, J. P.: An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation), Front. Mar. Sci., 8, 642372, https://doi.org/10.3389/fmars.2021.642372, 2021. a
Garrabou, J., Gómez-Gras, D., Medrano, A., Cerrano, C., Ponti, M., Schlegel, R., Bensoussan, N., Turicchia, E., Sini, M., Gerovasileiou, V., Teixido, N., Mirasole, A., Tamburello, L., Cebrian, E., Rilov, G., Ledoux, J.-B., Ben Souissi, J., Khamassi, F., Ghanem, R., Benabdi, M., Grimes, S., Ocaña, O., Bazairi, H., Hereu, B., Linares, C., Kersting, D. K., Rovira, G., Ortega, J., Casals, D., Pagès-Escolà, M., Margarit, N., Capdevila, P., Verdura, J., Ramos, A., Izquierdo, A., Barbera, C., Rubio-Portillo, E., Anton, I., López-Sendino, P., Díaz, D., Vázquez-Luis, M., Duarte, C., Marbà, N., Aspillaga, E., Espinosa, F., Grech, D., Guala, I., Azzurro, E., Farina, S., Gambi, M. C., Chimienti, G., Montefalcone, M., Azzola, A., Pulido Mantas, T., Fraschetti, S., Ceccherelli, G., Kipson, S., Bakran-Petricioli, T., Petricioli, D., Jimenez, C., Katsanevakis, S., Tuney Kizilkaya, I., Kizilkaya, Z., Sartoretto, S., Rouanet, E., Ruitton, S., Comeau, S., Gattuso, J.-P., and Harmelin, J.-G.: Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea, Glob. Change Biol., 28, 5708–5725, 2022. a
Gómez-Gras, D., Linares, C., López-Sanz, A., Amate, R., Ledoux, J. B., Bensoussan, N., Drap, P., Bianchimani, O., Marschal, C., Torrents, O., Zuberer, F., Cebrian, E., Teixidó, N., Zabala, M., Kipson, S., Kersting, D. K., Montero-Serra, I., Pagès-Escolà, M., Medrano, A., Frleta-Valić, M., Dimarchopoulou, D., López-Sendino, P., and Garrabou, J.: Population collapse of habitat-forming species in the Mediterranean: a long-term study of gorgonian populations affected by recurrent marine heatwaves, P. Roy. Soc. B, 288, 20212384, https://doi.org/10.1098/rspb.2021.2384, 2021. a
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R.: Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 16000–16009, https://openaccess.thecvf.com/content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf (last access: 26 January 2025), 2022. a, b
Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., and Wernberg, T.: A hierarchical approach to defining marine heatwaves, Prog. Oceanogr., 141, 227–238, https://doi.org/10.1016/j.pocean.2015.12.014, 2016. a
Ličer, M., Smerkol, P., Fettich, A., Ravdas, M., Papapostolou, A., Mantziafou, A., Strajnar, B., Cedilnik, J., Jeromel, M., Jerman, J., Petan, S., Malačič, V., and Sofianos, S.: Modeling the ocean and atmosphere during an extreme bora event in northern Adriatic using one-way and two-way atmosphere–ocean coupling, Ocean Sci., 12, 71–86, https://doi.org/10.5194/os-12-71-2016, 2016. a
Lloyd, D. T., Abela, A., Farrugia, R. A., Galea, A., and Valentino, G.: Optically enhanced super-resolution of sea surface temperature using deep learning, IEEE T. Geosci. Remote Sens., 60, 1–14, 2021. a
Loshchilov, I. and Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts, arXiv [preprint], https://doi.org/10.48550/arXiv.1608.03983, 2016. a, b
Martin, S. A., Manucharyan, G. E., and Klein, P.: Synthesizing sea surface temperature and satellite altimetry observations using deep learning improves the accuracy and resolution of gridded sea surface height anomalies, J. Adv. Model. Earth Sy., 15, e2022MS003589, https://doi.org/10.1029/2022MS003589, 2023. a
Martin, S. A., Manucharyan, G. E., and Klein, P.: Deep learning improves global satellite observations of ocean eddy dynamics, Geophys. Res. Lett., 51, e2024GL110059, https://doi.org/10.1029/2024GL110059, 2024. a
Mogen, S. C., Lovenduski, N. S., Dallmann, A. R., Gregor, L., Sutton, A. J., Bograd, S. J., Quiros, N. C., Di Lorenzo, E., Hazen, E. L., Jacox, M. G., Buil, M. P., and Yeager, S.: Ocean Biogeochemical Signatures of the North Pacific Blob, Geophys. Res. Lett., 49, e2021GL096938, https://doi.org/10.1029/2021GL096938, 2022. a
O'Carroll, A. G., Armstrong, E. M., Beggs, H. M., Bouali, M., Casey, K. S., Corlett, G. K., Dash, P., Donlon, C. J., Gentemann, C. L., Høyer, J. L., Ignatov, A., Kabobah, K., Kachi, M., Kurihara, Y., Karagali, I., Maturi, E., Merchant, C. J., Marullo, S., Minnett, P. J., Pennybacker, M., Ramakrishnan, B., Ramsankaran, R., Santoleri, R., Sunder, S., Saux Picart, S., Vázquez-Cuervo, J., and Wimmer, W.: Observational Needs of Sea Surface Temperature, Front. Mar. Sci., 6, 420, https://doi.org/10.3389/fmars.2019.00420, 2019. a
Pastor, F. and Khodayar, S.: Marine heat waves: Characterizing a major climate impact in the Mediterranean, Sci. Total Environ., 861, 160621, https://doi.org/10.1016/j.scitotenv.2022.160621, 2023. a
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A.: Automatic differentiation in PyTorch, https://pytorch.org/ (last access: 26 January 2025), 2017. a
Pisano, A., Buongiorno Nardelli, B., Tronconi, C., and Santoleri, R.: The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982–2012), Remote Sens. Environ., 176, 107–116, https://doi.org/10.1016/j.rse.2016.01.019, 2016. a
Ricchi, A., Sangelantoni, L., Redaelli, G., Mazzarella, V., Montopoli, M., Miglietta, M. M., Tiesi, A., Mazzà, S., Rotunno, R., and Ferretti, R.: Impact of the SST and topography on the development of a large-hail storm event, on the Adriatic Sea, Atmos. Res., 296, 107078, https://doi.org/10.1016/j.atmosres.2023.107078, 2023. a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for biomedical image segmentation, in: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, 5–9 October 2015, proceedings, part III 18, 234–241, Springer, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a, b
Senatore, A., Furnari, L., and Mendicino, G.: Impact of high-resolution sea surface temperature representation on the forecast of small Mediterranean catchments' hydrological responses to heavy precipitation, Hydrol. Earth Syst. Sci., 24, 269–291, https://doi.org/10.5194/hess-24-269-2020, 2020. a
Strajnar, B., Cedilnik, J., Fettich, A., Ličer, M., Pristov, N., Smerkol, P., and Jerman, J.: Impact of two-way coupling and sea-surface temperature on precipitation forecasts in regional atmosphere and ocean models, Q. J. Roy. Meteor. Soc., 145, 228–242, https://doi.org/10.1002/qj.3425, 2019. a
Taburet, G., Sanchez-Roman, A., Ballarotta, M., Pujol, M.-I., Legeais, J.-F., Fournier, F., Faugere, Y., and Dibarboure, G.: DUACS DT2018: 25 years of reprocessed sea level altimetry products, Ocean Sci., 15, 1207–1224, https://doi.org/10.5194/os-15-1207-2019, 2019. a
Ubelmann, C., Dibarboure, G., Gaultier, L., Ponte, A., Ardhuin, F., Ballarotta, M., and Faugère, Y.: Reconstructing ocean surface current combining altimetry and future spaceborne Doppler data, J. Geophys. Res.-Oceans, 126, e2020JC016560, https://doi.org/10.1029/2020JC016560, 2021. a
Young, C.-C., Cheng, Y.-C., Lee, M.-A., and Wu, J.-H.: Accurate reconstruction of satellite-derived SST under cloud and cloud-free areas using a physically-informed machine learning approach, Remote Sens. Environ., 313, 114339, https://doi.org/10.1016/j.rse.2024.114339, 2024. a, b
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation, in: Proceedings of the European conference on computer vision (ECCV), 325–341, https://openaccess.thecvf.com/content_ECCV_2018/papers/Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper.pdf (last access: 26 January 2025), 2018. a
Zupančič Muc, M.: CRITER – Coarse Reconstruction with ITerative Refinement network, Zenodo [code], https://doi.org/10.5281/zenodo.15066015, 2025. a
Zupančič Muc, M., Zavrtanik, V., Barth, A., Alvera-Azcarate, A., Licer, M., and Kristan, M.: CRITER 1.0: Sea Surface Temperature Evaluation Datasets, Zenodo [data set], https://doi.org/10.5281/zenodo.13923189, 2024. a
Short summary
Accurate sea surface temperature data (SST) are crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic sea data, CRITER outperforms current methods, reducing errors by up to 44 %.
Accurate sea surface temperature data (SST) are crucial for weather forecasting and climate...
