Resultados de investigación

4 files.-- Access to the zip files: https://doi.org/10.5281/zenodo.16528653, This repository collects the Radial Metrics in CODAR's LLUV format obtained from the SeaSondeR package of the PRIO HF-Radar station maintained by INTECMAR. For more details on the processing of these data, please check: Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., Montero, P., & Varela Benvenuto, R. (2025). Project HF-EOLUS. Task 2. Obtaining Radial Metrics from INTECMAR's VILA and PRIO Stations' Spectra. (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.16679609, This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

8 files.-- Access to zip files https://zenodo.org/records/16458694, This repository collects the Radial Metrics in CODAR's LLUV format obtained from the SeaSondeR package of the VILA HF-Radar station maintained by INTECMAR between 2011-09-30 and 2023-05-10. For more details on the processing of these data, please check: Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., Montero, P., & Varela Benvenuto, R. (2025). Project HF-EOLUS. Task 2. Obtaining Radial Metrics from INTECMAR's VILA and PRIO Stations' Spectra. (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.16679609, This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

[Contents of this record] - hf_eolus_sar.tar.gz: Tarball containing the processed data outputs. It includes the partitioned GeoParquet dataset (OGC GeoParquet v1.1 metadata) and a static STAC catalog (Collection and Items with the STAC Table Extension and Processing metadata) describing the Parquet assets. - pipeline.sh: The exact sequence of commands used to generate this dataset (download and ingestion), serving as an executable provenance log for reproducibility. - stac_properties_collection.json: STAC property definitions/templates applied at Collection level during catalog generation. - stac_properties_item.json: STAC property definitions/templates applied at Item level during catalog generation. - scripts/*.sql (e.g., scripts/athena_create_table.sql): SQL statements used for registering the resulting Parquet dataset as an external table (e.g., in AWS Athena) and for validating schema/partitions. - scripts/downloaded_files.txt: Manifest listing the Sentinel‑1 OCN product identifiers that were downloaded and used as inputs. - scripts/area_boundary.geojson: Area of Interest (AOI) polygon used to spatially constrain the search and download of Sentinel‑1 scenes. - scripts/files_to_download.csv: Input list and/or search results for Sentinel‑1 OCN products targeted by the download stage (includes product IDs and acquisition metadata), [Reproducibility and re‑execution] - Software pipeline: HF‑EOLUS Sentinel‑1 SAR Ingestion Pipeline (https://doi.org/10.5281/zenodo.17011823). The pipeline uses Dockerized R and Python environments for deterministic runs (no manual dependency setup required). - How to re‑run: Review pipeline.sh to see the exact commands, arguments, and environment variables used. If desired, clone the referenced pipeline repository, ensure Docker is available, and re‑execute the same steps. The AOI and time range used are captured in scripts/area_boundary.geojson and scripts/files_to_download.csv; the precise upstream inputs are listed in scripts/downloaded_files.txt. - Optional cloud registration: The SQL files in scripts/ can be used to register the resulting Parquet dataset in AWS Athena (or adapted for other engines like Trino/Spark). This step is optional and not required to read the Parquet files directly with tools such as Python (pyarrow/GeoPandas), R (arrow), or DuckDB. - Copernicus credentials: To re‑download Sentinel‑1 OCN data, provide your own Copernicus Data Space Ecosystem account credentials. Create a plain‑text file (e.g., `credentials`) with one line containing your key and pass its path via `--credentials-file` to `scripts/download_sar.sh`. For account and API access information, see https://dataspace.copernicus.eu and the documentation at https://documentation.dataspace.copernicus.eu/, [Data format and standards] - GeoParquet: Columnar, compressed Parquet files with OGC GeoParquet v1.1 geospatial metadata (geometry column, CRS, encoding). Files are partitioned to support efficient filtering and scalable analytics. - STAC: A static STAC Collection with Items describes each Parquet asset, including schema via the STAC Table Extension and lineage via Processing metadata. The catalog is suitable for static hosting and is interoperable with common STAC tooling., [Provenance and upstream data] - Upstream source: Copernicus Sentinel‑1 SAR Level‑2 OCN (OWI) products provided by the European Space Agency (ESA) under the Copernicus Programme. The list of specific input products for this dataset is included in scripts/downloaded_files.txt. - Processing: All derivations (extraction of ocean wind variables, conversion to GeoParquet, and STAC catalog creation) were performed by the HF‑EOLUS pipeline referenced above. The sequence is captured verbatim in pipeline.sh. - Credit line: Contains modified Copernicus Sentinel‑1 data; we gratefully acknowledge the Copernicus Programme and the European Space Agency (ESA) for providing free and open Sentinel‑1 data., [Usage notes] - Local analysis: The GeoParquet files can be opened directly with Python (pyarrow, pandas/GeoPandas), R (arrow), or SQL engines (DuckDB/Trino) without additional ingestion steps. - Catalog discovery: The STAC catalog in the tarball is static and can be browsed with STAC tools or published on object storage or a simple web server. - AWS/Athena setup (optional): To use the GeoParquet in AWS, upload the dataset to Amazon S3 and adjust the SQL to your paths and names, then execute in Athena: 1) Upload the GeoParquet (and optionally the STAC catalog) to `s3:////` preserving the folder structure. 2) Edit `scripts/athena_create_table.sql` to set the `LOCATION` to your S3 path and customize the database and table (e.g., change `SAR_INGEST.SAR` to `MY_DB.MY_TABLE`). 3) In Athena, run the SQL to create the database (if needed) and the external table. 4) Load partitions with `MSCK REPAIR TABLE MY_DB.MY_TABLE;` (or add partitions explicitly) and validate with a quick query such as `SELECT COUNT(*) FROM MY_DB.MY_TABLE;`., This record packages a reproducible bundle of derived ocean wind data and auxiliary materials produced from Copernicus Sentinel‑1 SAR Level‑2 OCN (OWI) products. The bundle contains the processed dataset as a GeoParquet data lake with a static STAC catalog, together with the exact scripts, configuration, SQL statements, and inputs used to generate it. It is intended to enable full transparency, re‑use, and re‑execution of the data generation workflow. The dataset was generated using the HF‑EOLUS Sentinel‑1 SAR Ingestion Pipeline (software record DOI: https://doi.org/10.5281/zenodo.17011823), which automates download and ingestion into partitioned GeoParquet, and builds a STAC Collection and Items describing the outputs. This record includes all relevant artifacts so that other researchers can verify the steps, inspect lineage, and rerun the pipeline if needed, This work has been funded by the HF‑EOLUS project (TED2021‑129551B‑I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

[Contents of this record] - hf_eolus_sar.tar.gz: Tarball containing the processed data outputs. It includes the partitioned GeoParquet dataset (OGC GeoParquet v1.1 metadata) and a static STAC catalog (Collection and Items with the STAC Table Extension and Processing metadata) describing the Parquet assets. - pipeline.sh: The exact sequence of commands used to generate this dataset (download and ingestion), serving as an executable provenance log for reproducibility. - stac_properties_collection.json: STAC property definitions/templates applied at Collection level during catalog generation. - stac_properties_item.json: STAC property definitions/templates applied at Item level during catalog generation. - scripts/*.sql (e.g., scripts/athena_create_table.sql): SQL statements used for registering the resulting Parquet dataset as an external table (e.g., in AWS Athena) and for validating schema/partitions. - scripts/downloaded_files.txt: Manifest listing the Sentinel‑1 OCN product identifiers that were downloaded and used as inputs. - scripts/VILA_PRIO_hull.json: Area of Interest (AOI) polygon used to constrain the search and download of Sentinel‑1 scenes spatially. This file defines a convex hull bounding the intersection between the areas covering the echos of VILA and PRIO stations. - scripts/files_to_download.csv: Input list and/or search results for Sentinel‑1 OCN products targeted by the download stage (includes product IDs and acquisition metadata)., [Reproducibility and re‑execution] - Software pipeline: HF‑EOLUS Sentinel‑1 SAR Ingestion Pipeline (https://doi.org/10.5281/zenodo.17011823). The pipeline uses Dockerized R and Python environments for deterministic runs (no manual dependency setup required). - How to re‑run: Review pipeline.sh to see the exact commands, arguments, and environment variables used. If desired, clone the referenced pipeline repository, ensure Docker is available, and re‑execute the same steps. The AOI and time range used are captured in scripts/area_boundary.geojson and scripts/files_to_download.csv; the precise upstream inputs are listed in scripts/downloaded_files.txt. - Optional cloud registration: The SQL files in scripts/ can be used to register the resulting Parquet dataset in AWS Athena (or adapted for other engines like Trino/Spark). This step is optional and not required to read the Parquet files directly with tools such as Python (pyarrow/GeoPandas), R (arrow), or DuckDB. - Copernicus credentials: To re‑download Sentinel‑1 OCN data, provide your own Copernicus Data Space Ecosystem account credentials. Create a plain‑text file (e.g., `credentials`) with one line containing your key and pass its path via `--credentials-file` to `scripts/download_sar.sh`. For account and API access information, see https://dataspace.copernicus.eu and the documentation at https://documentation.dataspace.copernicus.eu/, [Data format and standards] - GeoParquet: Columnar, compressed Parquet files with OGC GeoParquet v1.1 geospatial metadata (geometry column, CRS, encoding). Files are partitioned to support efficient filtering and scalable analytics. - STAC: A static STAC Collection with Items describes each Parquet asset, including schema via the STAC Table Extension and lineage via Processing metadata. The catalog is suitable for static hosting and is interoperable with common STAC tooling., [Provenance and upstream data] - Upstream source: Copernicus Sentinel‑1 SAR Level‑2 OCN (OWI) products provided by the European Space Agency (ESA) under the Copernicus Programme. The list of specific input products for this dataset is included in scripts/downloaded_files.txt. - Processing: All derivations (extraction of ocean wind variables, conversion to GeoParquet, and STAC catalog creation) were performed by the HF‑EOLUS pipeline referenced above. The sequence is captured verbatim in pipeline.sh. - Credit line: Contains modified Copernicus Sentinel‑1 data; we gratefully acknowledge the Copernicus Programme and the European Space Agency (ESA) for providing free and open Sentinel‑1 data., [Usage notes] - Local analysis: The GeoParquet files can be opened directly with Python (pyarrow, pandas/GeoPandas), R (arrow), or SQL engines (DuckDB/Trino) without additional ingestion steps. - Catalog discovery: The STAC catalog in the tarball is static and can be browsed with STAC tools or published on object storage or a simple web server. - AWS/Athena setup (optional): To use the GeoParquet in AWS, upload the dataset to Amazon S3 and adjust the SQL to your paths and names, then execute in Athena: 1) Upload the GeoParquet (and optionally the STAC catalog) to `s3:////` preserving the folder structure. 2) Edit `scripts/athena_create_table.sql` to set the `LOCATION` to your S3 path and customize the database and table (e.g., change `SAR_INGEST.SAR` to `MY_DB.MY_TABLE`). 3) In Athena, run the SQL to create the database (if needed) and the external table. 4) Load partitions with `MSCK REPAIR TABLE MY_DB.MY_TABLE;` (or add partitions explicitly) and validate with a quick query such as `SELECT COUNT(*) FROM MY_DB.MY_TABLE;`., This record packages a reproducible bundle of derived ocean wind data and auxiliary materials produced from Copernicus Sentinel‑1 SAR Level‑2 OCN (OWI) products. The bundle contains the processed dataset as a GeoParquet data lake with a static STAC catalog, together with the exact scripts, configuration, SQL statements, and inputs used to generate it. It is intended to enable full transparency, re‑use, and re‑execution of the data generation workflow. The dataset was generated using the HF‑EOLUS Sentinel‑1 SAR Ingestion Pipeline (software record DOI: https://doi.org/10.5281/zenodo.17011823), which automates download and ingestion into partitioned GeoParquet, and builds a STAC Collection and Items describing the outputs. This record includes all relevant artifacts so that other researchers can verify the steps, inspect lineage, and rerun the pipeline if needed, This work has been funded by the HF‑EOLUS project (TED2021‑129551B‑I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

[Contents of this record] - hf_eolus_sar.tar.gz: Tarball containing the processed data outputs. It includes the partitioned GeoParquet dataset (OGC GeoParquet v1.1 metadata) and a static STAC catalog (Collection and Items with the STAC Table Extension and Processing metadata) describing the Parquet assets. - pipeline.sh: The exact sequence of commands used to generate this dataset (download and ingestion), serving as an executable provenance log for reproducibility. - stac_properties_collection.json: STAC property definitions/templates applied at Collection level during catalog generation. - stac_properties_item.json: STAC property definitions/templates applied at Item level during catalog generation. - scripts/*.sql (e.g., scripts/athena_create_table.sql): SQL statements used for registering the resulting Parquet dataset as an external table (e.g., in AWS Athena) and for validating schema/partitions. - scripts/downloaded_files.txt: Manifest listing the Sentinel‑1 OCN product identifiers that were downloaded and used as inputs. - scripts/VILA_PRIO_hull.json: Area of Interest (AOI) polygon used to constrain the search and download of Sentinel‑1 scenes spatially. This file defines a convex hull bounding the intersection between the areas covering the echos of VILA and PRIO stations. - scripts/files_to_download.csv: Input list and/or search results for Sentinel‑1 OCN products targeted by the download stage (includes product IDs and acquisition metadata)., [Reproducibility and re‑execution] - Software pipeline: HF‑EOLUS Sentinel‑1 SAR Ingestion Pipeline (https://doi.org/10.5281/zenodo.17011823). The pipeline uses Dockerized R and Python environments for deterministic runs (no manual dependency setup required). - How to re‑run: Review pipeline.sh to see the exact commands, arguments, and environment variables used. If desired, clone the referenced pipeline repository, ensure Docker is available, and re‑execute the same steps. The AOI and time range used are captured in scripts/area_boundary.geojson and scripts/files_to_download.csv; the precise upstream inputs are listed in scripts/downloaded_files.txt. - Optional cloud registration: The SQL files in scripts/ can be used to register the resulting Parquet dataset in AWS Athena (or adapted for other engines like Trino/Spark). This step is optional and not required to read the Parquet files directly with tools such as Python (pyarrow/GeoPandas), R (arrow), or DuckDB. - Copernicus credentials: To re‑download Sentinel‑1 OCN data, provide your own Copernicus Data Space Ecosystem account credentials. Create a plain‑text file (e.g., `credentials`) with one line containing your key and pass its path via `--credentials-file` to `scripts/download_sar.sh`. For account and API access information, see https://dataspace.copernicus.eu and the documentation at https://documentation.dataspace.copernicus.eu/, [Data format and standards] - GeoParquet: Columnar, compressed Parquet files with OGC GeoParquet v1.1 geospatial metadata (geometry column, CRS, encoding). Files are partitioned to support efficient filtering and scalable analytics. - STAC: A static STAC Collection with Items describes each Parquet asset, including schema via the STAC Table Extension and lineage via Processing metadata. The catalog is suitable for static hosting and is interoperable with common STAC tooling., [Provenance and upstream data] - Upstream source: Copernicus Sentinel‑1 SAR Level‑2 OCN (OWI) products provided by the European Space Agency (ESA) under the Copernicus Programme. The list of specific input products for this dataset is included in scripts/downloaded_files.txt. - Processing: All derivations (extraction of ocean wind variables, conversion to GeoParquet, and STAC catalog creation) were performed by the HF‑EOLUS pipeline referenced above. The sequence is captured verbatim in pipeline.sh. - Credit line: Contains modified Copernicus Sentinel‑1 data; we gratefully acknowledge the Copernicus Programme and the European Space Agency (ESA) for providing free and open Sentinel‑1 data., This record packages a reproducible bundle of derived ocean wind data and auxiliary materials produced from Copernicus Sentinel‑1 SAR Level‑2 OCN (OWI) products. The bundle contains the processed dataset as a GeoParquet data lake with a static STAC catalog, together with the exact scripts, configuration, SQL statements, and inputs used to generate it. It is intended to enable full transparency, re‑use, and re‑execution of the data generation workflow. The dataset was generated using the HF‑EOLUS Sentinel‑1 SAR Ingestion Pipeline (software record DOI: https://doi.org/10.5281/zenodo.17011823), which automates download and ingestion into partitioned GeoParquet, and builds a STAC Collection and Items describing the outputs. This record includes all relevant artifacts so that other researchers can verify the steps, inspect lineage, and rerun the pipeline if needed, This work has been funded by the HF‑EOLUS project (TED2021‑129551B‑I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

18 files, This record presents the results obtained by applying the open, reproducible workflow described in HF-EOLUS Geospatial Processing Tools to the HF-EOLUS Sentinel‑1 SAR Derived Data Bundle and to the VILA & PRIO Radial Metrics GeoParquet datasets. It provides analysis-ready geospatial data products generated to train HF-Radar wind-inversion models. HF-EOLUS Geospatial Processing Tools documents the methodology, software environment, and parameterization; the dataset records contain the data used as input. This record includes the concrete outputs for reuse and citation. Wind-inversion models are trained using supervised learning methods. In the HF-EOLUS project, we tackle this task as a regression problem where, given a set of features (HF-Radar data), we train a model to predict a numerical output (wind speed and direction). Due to the dense nature of the SAR and HF-Radar data and that they are not initially co-located, we need to aggregate them into a grid that allows us to get paired time-series of HF-Radar input <-> SAR output on a grid. HF-EOLUS Geospatial Processing Tools provides a workflow that we use here to 1) compute the coverage area of the HF-Radar data, 2) build a regular grid inside that area, 3) map each SAR and HF-Radar record into that grid, 4) aggregate the features and outputs into time-series for each node in the grid, 5) standarize the result for easy access and downstream analysis, This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

21 files, This record presents the results obtained by applying the open, reproducible workflow described in HF-EOLUS Geospatial Processing Tools to the HF-EOLUS Sentinel‑1 SAR Derived Data Bundle and to the VILA & PRIO Radial Metrics GeoParquet datasets. It provides analysis-ready geospatial data products generated to train HF-Radar wind-inversion models. HF-EOLUS Geospatial Processing Tools documents the methodology, software environment, and parameterization; the dataset records contain the data used as input. This record includes the concrete outputs for reuse and citation., Wind-inversion models are trained using supervised learning methods. In the HF-EOLUS project, we tackle this task as a regression problem where, given a set of features (HF-Radar data), we train a model to predict a numerical output (wind speed and direction). Due to the dense nature of the SAR and HF-Radar data and that they are not initially co-located, we need to aggregate them into a grid that allows us to get paired time-series of HF-Radar input <-> SAR output on a grid. HF-EOLUS Geospatial Processing Tools provides a workflow that we use here to 1) compute the coverage area of the HF-Radar data, 2) build a regular grid inside that area, 3) map each SAR and HF-Radar record into that grid, 4) aggregate the features and outputs into time-series for each node in the grid, 5) standarize the result for easy access and downstream analysis. For convenience, the pipeline.sh script implements all the steps described below, This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

The LLUV inputs used for ingestion are archived at https://doi.org/10.5281/zenodo.16528652 and https://doi.org/10.5281/zenodo.16458693. First, these LLUV files were uploaded to AWS S3; the included pipeline.sh then orchestrates their processing: AWS Glue parses LLUV into partitioned Parquet, Athena registers external tables, and STAC JSON is generated. The STAC property templates applied during generation, stac_properties_collection.json and stac_properties_item.json, are bundled for transparency and reuse. After unpacking, open hf_ingestion/catalog.json with a STAC client to browse collections and items. Parquet assets are partitioned by pos_bragg and hourly timestamp; geometries are WKB points in WGS84 stored in the geometry column (example fields include Pwr and VELO). Total number of rows (radar echoes) in the radial_metrics datasets: VILA: 347,275,384 PRIO: 240,847,334, Access to zip file: https://zenodo.org/records/16892223, This record publishes the outputs of the HF‑EOLUS HF‑radar radial metrics ingestion pipeline, produced with the HF‑EOLUS ingestion code (https://doi.org/10.5281/zenodo.17071947), as a single archive: hf_eolus_hf_ingestion.tar.gz. It contains a STAC 1.1 catalog for stations VILA and PRIO (collections HF‑Radar‑VILA1..4 and HF‑Radar‑PRIO1..2; VILA: 2011‑09–2023‑05; PRIO: 2011‑08–2015‑04 and 2018‑05–2023‑11) and GeoParquet v1.1 datasets for radial metrics (radial_metrics/), header (header/), and range summaries (rng_info/)., This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

This record packages the reference outputs for the HF-EOLUS Task 3 interpolation experiment over the Vilano HF-radar footprint (open Atlantic, NW Iberia). The dataset was produced with the hf-eolus-interpolation-toolkit (Herrera Cortijo et al., 2025) by ingesting MeteoGalicia's historical WRF_HIST d03 (4 km) winds, refining the grid by a factor of eight, fitting kriging/IDW models and exporting the resulting GeoParquet layers together with metadata, plots and buoy comparisons. All counts and metrics below were recomputed with duckdb/duckdb:latest (Docker) over the committed assets in March 2025 so the README reflects the current state of the case study, This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed

Due to file size and number-of-files restrictions, the dataset has not been uploaded as a single zipped folder. Please copy all the files into a folder and decompress them, then move catalog.json and the contents of the buoy file into a catalogs folder to reproduce the original dataset layout, This record packages the reference outputs for the HF-EOLUS Task 3 interpolation experiment over the Vilano HF-radar footprint (open Atlantic, NW Iberia). The dataset was produced with the hf-eolus-interpolation-toolkit (Herrera Cortijo et al., 2025) by ingesting MeteoGalicia's historical WRF_HIST d03 (4 km) winds, refining the grid by a factor of eight, fitting kriging/IDW models and exporting the resulting GeoParquet layers together with metadata, plots and buoy comparisons. All counts and metrics below were recomputed with duckdb/duckdb:latest (Docker) over the committed assets in March 2025 so the README reflects the current state of the case stud, This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3, Peer reviewed