WAPOSAL: a multi-regional wave dataset from satellite altimetry for significant wave height, period estimation, and wave power density

Abstract. Accurate assessment of wave power density (WPD) is essential for marine renewable energy development and for validating numerical wave models, particularly in coastal and nearshore regions where in situ observations are sparse. This study presents a high-resolution, multi-region wave dataset generated within the WAPOSAL (Wave Power & Satellite Altimetry) project, derived from Synthetic Aperture Radar (SAR) altimetry data acquired by the Sentinel-3A/B and CryoSat-2 missions. Significant wave height and normalized radar cross-section were obtained using the SAMOSA+ retracker, and zerocrossing wave period was estimated based on an empirical regression method calibrated with in situ buoy and ERA5 data. Wave power density was then computed along altimeter tracks across eleven regions: Norway & Baltic Sea, UK & North Sea, French Facade, Spain Atlantic, Portugal, Mediterranean sea, Madeira, Canary Islands, Azores Archipelago, French Guiana and French Polynesia. The temporal coverage spans 2011–2023, depending on the region and the satellite mission considered. The present paper describes the database and illustrates its use through simple application examples. The dataset in its current format and version can be discovered, shared and cited via the following link: https://doi.org/10.57780/ESA-1AB8CF3 (Ponce de Léon et al., 2026).

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Publication Details

Journal
Earth system science data
Published
2026-10-08
DOI
https://doi.org/10.5194/essd-18-7391-2026
Citations
1
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
3.92

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article

WAPOSAL: a multi-regional wave dataset from satellite altimetry for significant wave height, period estimation, and wave power density

Sonia Ponce de León, Andrés Fernando Orejarena-Rondón, Jérôme Benveniste, Maria Panfilova et al.
1 citations
Earth system science data
Ocean Waves and Remote Sensing
3.92
article

WAPOSAL: a multi-regional wave dataset from satellite altimetry for significant wave height, period estimation, and wave power density

Sonia Ponce de León, Andrés Fernando Orejarena-Rondón, Jérôme Benveniste, Maria Panfilova, Roberto Sabia, Marco Restano
article en
1 citations

Abstract

Abstract. Accurate assessment of wave power density (WPD) is essential for marine renewable energy development and for validating numerical wave models, particularly in coastal and nearshore regions where in situ observations are sparse. This study presents a high-resolution, multi-region wave dataset generated within the WAPOSAL (Wave Power & Satellite Altimetry) project, derived from Synthetic Aperture Radar (SAR) altimetry data acquired by the Sentinel-3A/B and CryoSat-2 missions. Significant wave height and normalized radar cross-section were obtained using the SAMOSA+ retracker, and zerocrossing wave period was estimated based on an empirical regression method calibrated with in situ buoy and ERA5 data. Wave power density was then computed along altimeter tracks across eleven regions: Norway & Baltic Sea, UK & North Sea, French Facade, Spain Atlantic, Portugal, Mediterranean sea, Madeira, Canary Islands, Azores Archipelago, French Guiana and French Polynesia. The temporal coverage spans 2011–2023, depending on the region and the satellite mission considered. The present paper describes the database and illustrates its use through simple application examples. The dataset in its current format and version can be discovered, shared and cited via the following link: https://doi.org/10.57780/ESA-1AB8CF3 (Ponce de Léon et al., 2026).

Earth system science dataVol. 18(10)
University of Lisbon (PT), Instituto Superior Técnico (PT)
European Space Agency
Climate action
Openalex Percentile: Top 10%
Ocean Waves and Remote Sensing
3.92
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WAPOSAL: a multi-regional wave dataset from satellite altimetry for significant wave height, period estimation, and wave power density — Sonia Ponce de León, Andrés Fernando Orejarena-Rondón, et al. · Earth system science data (2026) | TGRS Research Map | TGRS