the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Mar 2026
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).
- Preprint
(4710 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 21 Jul 2026)
-
RC1: 'Comment on essd-2026-104', Anonymous Referee #1, 11 Apr 2026
reply
-
AC1: 'Reply on RC1', Maria Panfilova, 14 May 2026
reply
We are grateful to the reviewer for insightful comments and suggestions. We provide a point-by-point response to the comments in the attached file.
-
RC2: 'Reply on AC1', Anonymous Referee #1, 08 Jun 2026
reply
Thank you for yor comments whoich seem convincing. I am now waiting for the revised manuscript.
Citation: https://doi.org/10.5194/essd-2026-104-RC2
-
RC2: 'Reply on AC1', Anonymous Referee #1, 08 Jun 2026
reply
-
AC1: 'Reply on RC1', Maria Panfilova, 14 May 2026
reply
-
RC3: 'Comment on essd-2026-104', Anonymous Referee #2, 24 Jun 2026
reply
Please, find the review of the manuscript entitled ”WAPOSAL: A Multi-RegionalWave Dataset from Satellite Altimetry for Significant Wave Height, Period Estimation, and Wave Power Density”.
This manuscript presents the satellite altimeter dataset. The authors develop a high-resolution wave dataset derived from Sentinel-3A/B and CryoSat-2 SAR altimetry, providing significant wave height (Hs), estimated zero-crossing wave period (Tz), and wave power density (WPD) across 11 coastal and oceanic regions. The dataset addresses the gap between sparse buoy observations and numerical model limitations, particularly in coastal environments.
The manuscript is clearly written, however methodology requires clarification. Below are my comments and questions:
1. I suggest the authors to discuss potential limitations in regions where validation relies solely on ERA5 rather than in situ measurements, and the implications for confidence in those areas.
2. The methodology is based on the deep water approximation (eq. 2 in the manuscript). The limitations of this approach should be discussed, how this limitations may influence the result.
3. There are several approaches to estimate wave period using altimeter data, for instance (Mackay et. al. 2008, 10.1029/2007JC004438), (Badulin el.al. 2014 10.1002/2013JC009336). Please, justify why the method (Gommenginger at. al. 2003 10.1029/2003GL017743) was chosen.
4. Please, clarify if for obtaining the equation for wave period and for validation the same buoys are used, or a group of buoys in each region is used to develop the model and another one to validate it.
5. The energy wave period is calculated as Te=1.18Tz, however in fact wave period ratio is not constant (Cahill, 2014.). Please discuss the limitations of this approach and possible ways to solve this problem.
I believe the manuscript can be accepted for publication after some revision.
Citation: https://doi.org/10.5194/essd-2026-104-RC3 -
AC2: 'Reply on RC3', Maria Panfilova, 30 Jun 2026
reply
We are thankful to the reviewer fore important comments. The replies to the comments are in the attached document.
-
RC4: 'Reply on AC2', Anonymous Referee #2, 03 Jul 2026
reply
The authors have addressed all of my comments. I have no further questions and look forward to the revised manuscript.
Citation: https://doi.org/10.5194/essd-2026-104-RC4
-
RC4: 'Reply on AC2', Anonymous Referee #2, 03 Jul 2026
reply
-
AC2: 'Reply on RC3', Maria Panfilova, 30 Jun 2026
reply
-
RC5: 'Comment on essd-2026-104', Anonymous Referee #3, 07 Jul 2026
reply
- General comments
This manuscript presents a new dataset created using Synthetic Aperture Radar (SAR) altimetry data from Sentinel-3A/B and CryoSat-2. Thirteen years of the SAR data are analyzed to retrieve measurements of the significant wave height (Hs), zero crossing wave period (Tz) and wave power density (WPD) around eleven oceanic and coastal regions.
The high resolution data is retrieved using the SAMOSA+ retracker and empirical functions. The new dataset contains crucial information for the assessment of the wave parameters, needed for marine spatial planning, offshore renewable energy development and maritime safety.
Although the manuscript presents a new dataset with products of SAR observation, it lacks details on the methodology of data retrieval and details on limitations and error estimates regarding the two different satellites used.
Below the comments and questions on the manuscript.
- Suggested technical corrections
Line 14-17: Suggest rewriting the sentence to make it easier to follow and more clear: “ Marine renewable energy is a good option for countries with extensive maritime zones. This is due to a growing interest in renewable energy sources for electricity generation to fight climate change, rising global energy consumption, competition for land use (agriculture, urban planning, and protected areas), and high population density.”
Line 46-47: Suggest rewriting the sentence to make it easier to follow and more clear: “This article describes the method used to determine the wave period, the validation of the Hs and Tz obtained from satellite altimetry, the structure of the final dataset and finally the applications of the wave power density dataset.”
Line 98: Suggest rewriting the sentence to make it more coherent and clear: “ with a Root Mean Square Error (RMSE) of 0.22 m and a correlation coefficient (CC)”.
Line 124-129: Suggest rewriting this list of the satellite’s data availability or create a table for better visualization of the information.
Line 65: Clarify and explain with more detail what is the “misfit”.
Line 109: The reference "(Cahill 2012)” is not present in the list of references. Another article is present in the references as (Cahill 2014)
- Specific comments
Line 43: Mentions that “the data is validated against in-situ wave buoys and/or ERA5 data.” suggestion to explain what type of validation technique is applied to the data, and highlight how the data is divided between validation with ERA5 and validation with buoys.
Line 53: Mentions that the “data in high resolution Synthetic Aperture Radar (SAR) mode
were considered and reprocessed.” Suggestion to describe how much data has been used for each location, if there is a minimum threshold of number of images from SAR that are needed for the location to be able to determine the wave height, wave period and wave energy density.
Line 74-77: The method presented by Gommenginger et al. in 2003 calculates the zero crossing wave period for deep water, suggestion to consider and explain limitation on how that applies to the selected buoys and ERA5 points’ data since the area of interest is a coastal area. Or state whether the selected buoys and ERA5 points are located in deep water.
Moreover, suggestion to describe more thoroughly how the coefficients a and b are calculated.
Line 87: Mentions "the selected ERA5 grid nodes" without assessing how and how many grid nodes are selected. Did you select the closest node to the location? Did you consider all the nodes in the radius of 40 km from the location? Or did you interpolate the values of the closest nodes? In addition, how many nodes for location did you use? And does this number change depending on the locations?
Line 89/90: Mentions that "the time interval was 45 minutes, and the spatial distance was 40 km", how were these spatial and temporal conditions for the data collocation selected? Did you follow any previous study done or is there any reference in literature? Did you consider the possibility of the results changing if you selected different spatial and temporal conditions?
Line 93: Assesses "training and validation" which was never mentioned before therefore it is suggested to discuss this part of the method in more detail, specifically describing how the datasets were divided, if they had the same or different sizes, and if there are any constraints on the selection of the data. It would be extremely useful to have more quantitative data on this part of the methodology.
Line 97: States that " for CryoSat-2, similar results are obtained", without giving any type of numerical value of the error metrics like RMSE or CC. It is suggested to add the graph of the CryoSat-2 results, or at least report the numerical qualitative data as done for Sentinel-3A/B data. It is also suggested to integrate information about differences between the Sentinel-3A/B and CryoSat-2, and where such differences may come from, biases or differences in the satellite sensors swath.
Line 116/117: mentions "observation time in seconds since 01-01-2000 for each antenna footprint', without specifying the time zone taken as reference for the observation time data.
Line 169: States " The satellite-derived WPD closely follows the ERA5 observations.", without discussing any numerical value nor quantitative data. What is the bias and RMSE between the satellite derived WPD and the ERA5 observation?
- Data quality related comments
The data is easily downloadable on python by following the suggested code written in Appendix A.
Citation: https://doi.org/10.5194/essd-2026-104-RC5
Data sets
High-resolution WAPOSAL wave period and wave power density from Sentinel-3 A/B, CryoSat-2, and SAMOSA+ retracker S. Ponce de Léon et al. https://doi.org/10.57780/ESA-1AB8CF3
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 320 | 101 | 25 | 446 | 23 | 28 |
- HTML: 320
- PDF: 101
- XML: 25
- Total: 446
- BibTeX: 23
- EndNote: 28
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
see attached document