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

1 file.-- This software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software, This repository is part of the HF-EOLUS project and constitutes the first step towards its Task 2: obtaining wind fields from oceanic HF-radar data. Task 2 aims to use, evaluate, and develop the extraction of wind data from HF-radar data. In the HF-EOLUS methodology, the first step in wind extraction is obtaining radial metrics from the spectra collected by HF-Radar stations. This repository applies a reproducible, containerized workflow to process high-frequency (HF) radar spectra from INTECMAR’s VILA and PRIO HF-Radar stations located on the Galician shelf (NW-Spain). The primary output is LLUV files containing radial metrics derived from CODAR SeaSonde spectral data. For more details, please visit the repository page on GitHub.

The applied workflow features period-specific configurations, automatic manifest generation, and scalable AWS batch processing (Lambda & S3 Batch Operations) for each station, as detailed in [1]. The underlying analysis of the HF radar spectra is carried out by the SeaSondeR R package [2]. Finally, the processed LLUV datasets are publicly available on Zenodo (PRIO [3] VILA [4]), 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

1 file.-- This software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software, This repository implements a complete workflow for batch processing HF-Radar spectra files using the SeaSondeR R package on AWS. For more details, please visit SeaSondeRAWSLambdaDocer on GitHub, 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 goal of SeaSondeR is to provide a comprehensive set of tools for processing and analyzing data from the SeaSonde(R) High-Frequency Radar (HF-Radar) instrument. This package is intended to facilitate the creation of radial metrics files from spectra data. SeaSondeR is an R package developed as an open-source alternative to proprietary tools for processing HF-Radar spectra and generating Radial Metrics. SeaSondeR integrates all key processing steps into a single workflow, reading and processing both spectrum (CS) files and SeaSonde(R) antenna patterns, identifying the first-order spectral region according to CODAR methodology, and applying the MUSIC algorithm to estimate signal arrival directions. Drawing on technical manuals, patent literature, and prior scientific work, SeaSondeR is designed for both experimental and synthetic data, facilitating cloud-based analyses of extensive HF-Radar datasets while promoting free software solutions in operational oceanography and coastal monitoring, This software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software, R package published in CRAN to read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. For more information ,please visit the package page on CRAN and the package pages on GitHub, 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://<your-bucket>/<your-prefix>/` 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://<your-bucket>/<your-prefix>/` 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

1 file, HF‑EOLUS Geospatial Processing Tools is a set of CLI tools for transforming and analyzing large sets of point geospatial data into cloud-native formats, generating GeoParquet and STAC metadata. It offers a step-by-step pipeline to go from tabular data to analysis-ready assets (grids, node mapping, aggregation, and cataloging). More details on GitHub: https://github.com/GOFUVI/hf_eolus_geo_tools, 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

[Workflow overview]
1. Cloud-native ingestion – Parameterised Lambda + Step Functions workflow that downloads MeteoGalicia grids, applies optional spatial filters and stores structured Parquet assets on S3 along with metadata sidecars.
2. Interpolation engine – Containerised R jobs executed via AWS Batch that build refined grids, tune interpolation hyperparameters, compute diagnostics (bias, RSR, LOOCV) and emit GeoParquet layers plus supporting plots.
3. Catalog publishing – Dockerised STAC builder that packages GeoParquet assets, metadata and visual diagnostics into STAC collections/items ready for downstream discovery platforms.
4. Reproducible automation – A single run_pipeline.sh driver wires together ingestion, processing and catalog publication with a consistent set of environment variables, enabling portable redeployments and verifiable outputs without manual intervention., [Capabilities]
- Scalable interpolation: Supports inverse distance weighting, regression kriging and universal kriging with configurable search radii, neighbourhood sizes and cross-validation folds.
- Diagnostics-first design: Automatically tracks bias, RSR, LOOCV scores and quadrant consistency, persisting both numeric summaries and ready-to-publish plots.
- GeoParquet + STAC outputs: Generates spatially indexed GeoParquet layers alongside STAC metadata so datasets can be catalogued, discovered and downloaded with standard tooling.
- Automation hooks: CLI scripts and infrastructure definitions expose every step for reuse inside CI/CD pipelines, AWS Batch arrays or ad-hoc container executions., [Software availability]
The full source code, infrastructure definitions and documentation are available at https://github.com/GOFUVI/hf-eolus-interpolation-toolkit, The HF-EOLUS Wind Interpolation Toolkit scales the methodology originally developed for MeteoGalicia WRF outputs so that end-to-end wind field interpolation can be orchestrated reproducibly on AWS. The toolkit automates ingestion of NetCDF model data, conversion to hourly Parquet partitions, spatial interpolation (IDW, regression kriging and universal kriging), diagnostic scoring and publication of GeoParquet deliverables via STAC catalogs, ensuring reproducibility from raw inputs to discoverable data products, 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

[Workflow overview]
1. Cloud-native ingestion – Parameterised Lambda + Step Functions workflow that downloads MeteoGalicia grids, applies optional spatial filters and stores structured Parquet assets on S3 along with metadata sidecars. 2. Interpolation engine – Containerised R jobs executed via AWS Batch that build refined grids, tune interpolation hyperparameters, compute diagnostics (bias, RSR, LOOCV) and emit GeoParquet layers plus supporting plots. 3. Catalog publishing – Dockerised STAC builder that packages GeoParquet assets, metadata and visual diagnostics into STAC collections/items ready for downstream discovery platforms. 4. Reproducible automation – A single run_pipeline.sh driver wires together ingestion, processing and catalog publication with a consistent set of environment variables, enabling portable redeployments and verifiable outputs without manual intervention., [Capabilities]
- Scalable interpolation: Supports inverse distance weighting, regression kriging and universal kriging with configurable search radii, neighbourhood sizes and cross-validation folds. - Diagnostics-first design: Automatically tracks bias, RSR, LOOCV scores and quadrant consistency, persisting both numeric summaries and ready-to-publish plots. - GeoParquet + STAC outputs: Generates spatially indexed GeoParquet layers alongside STAC metadata so datasets can be catalogued, discovered and downloaded with standard tooling. - Automation hooks: CLI scripts and infrastructure definitions expose every step for reuse inside CI/CD pipelines, AWS Batch arrays or ad-hoc container executions., [What's new]
- Incremental STAC publishing: scripts/11-build_stac_catalog.py and the Docker wrapper now support --incremental, preserving existing assets/Items while appending only new GeoParquet partitions.
- Year-by-year STAC builds: run_build_stac_catalog.sh/11-build_stac_catalog.py add --by-year, optional --years and --limit-years to sync/process only selected years and load just the corresponding Items; the wrapper also raises the Docker nofile ulimit to avoid descriptor exhaustion on large catalogs.
- STAC subsetting utility: New scripts/12-subset_stac_nodes.py (with run_subset_stac_nodes.sh) filters catalogs by node_id and/or polygon and emits a derived STAC plus a single Parquet/GeoParquet.
- Faster subsets on incomplete catalogs: 12-subset_stac_nodes.py accepts --skip-source-items to avoid enumerating every source Item when only a template is needed, enabling subsets even if the source catalog is incomplete.
- Hardened ingestion/interpolation scripts: scripts/08-run_interpolation.sh improves NetCDF download robustness and parameter handling; run_pipeline.sh propagate the new flags and AWS profile/region wiring.
- Wrapper enhancements: run_build_stac_catalog.sh and the subset wrapper reuse a shared Docker image, sync S3 prefixes automatically when needed, and expose AWS profiles/regions consistently., [Software availability]
The full source code, infrastructure definitions and documentation are available at https://github.com/GOFUVI/hf-eolus-interpolation-toolki, The HF-EOLUS Wind Interpolation Toolkit scales the methodology originally developed for MeteoGalicia WRF outputs so that end-to-end wind field interpolation can be orchestrated reproducibly on AWS. The toolkit automates ingestion of NetCDF model data, conversion to hourly Parquet partitions, spatial interpolation (IDW, regression kriging and universal kriging), diagnostic scoring and publication of GeoParquet deliverables via STAC catalogs, ensuring reproducibility from raw inputs to discoverable data products, 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

1 file, This documentation describes how the HF-EOLUS project stores large volumes of high-frequency radar Radial Metrics data in the GeoParquet format and organizes them with STAC (SpatioTemporal Asset Catalog) metadata standards. It serves as a guide for technical users interested in data storage and cataloging strategies for geospatial datasets. While HF-Radar observations are the primary focus, the same principles apply to meteorological model outputs processed in the project. Key benefits include efficient, cloud-optimized storage and internationally standardized metadata for interoperability. For more details, please visit the specifications repository on GitHub, 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 release completes the documentation for HF-EOLUS. Every external workflow now has a clear inventory that lists its GeoParquet deliverables, partition layouts, key columns, STAC extensions, and Zenodo DOIs, giving readers a single place to trace each processing line end to end. README and the overview have been expanded so all seven processing lines (plus the MeteoGalicia interpolation branch) are described consistently, with the interpolation workflow explicitly called out as a parallel branch that is not yet wired into the wind-resource chain. The GeoParquet and STAC specifications now open with shared fundamentals and then dive into per-workflow profiles detailing schemas, geometry choices, asset naming, and catalog hierarchies, making it easier to see how the standards are applied in practice. To tie everything together, a cross-document validation checklist verifies that every workflow is mentioned in README/overview and captured in both specification files, ensuring complete traceability, This documentation describes how the HF-EOLUS project stores large volumes of high-frequency radar Radial Metrics data in the GeoParquet format and organizes them with STAC (SpatioTemporal Asset Catalog) metadata standards. It serves as a guide for technical users interested in data storage and cataloging strategies for geospatial datasets. While HF-Radar observations are the primary focus, the same principles apply to meteorological model outputs processed in the project. Key benefits include efficient, cloud-optimized storage and internationally standardized metadata for interoperability. For more details, please visit the specifications repository on GitHub, 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 release includes links to online browsers and viewers for inspecting the catalogs, This documentation describes how the HF-EOLUS project stores large volumes of high-frequency radar Radial Metrics data in the GeoParquet format and organizes them with STAC (SpatioTemporal Asset Catalog) metadata standards. It serves as a guide for technical users interested in data storage and cataloging strategies for geospatial datasets. While HF-Radar observations are the primary focus, the same principles apply to meteorological model outputs processed in the project. Key benefits include efficient, cloud-optimized storage and internationally standardized metadata for interoperability. For more details, please visit the specifications repository on GitHub, 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

1 file, Automated pipeline to acquire Sentinel-1 SAR OCN/OWI data, convert it into partitioned GeoParquet, and generate a STAC catalog ready for local or cloud analytics. Includes Dockerized scripts (download_sar.sh, ingest_sar.sh) for reproducible download and ingestion, with optional publishing to Amazon S3 and registration in AWS Athena for SQL querying. For setup, usage, and specifications, please refer to the repository: https://github.com/GOFUVI/hf_eolus_sar_ingestion, 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

1 file, Automated pipeline to acquire Sentinel-1 SAR OCN/OWI data, convert it into partitioned GeoParquet, and generate a STAC catalog ready for local or cloud analytics. Includes Dockerized scripts (download_sar.sh, ingest_sar.sh) for reproducible download and ingestion, with optional publishing to Amazon S3 and registration in AWS Athena for SQL querying. For setup, usage, and specifications, please refer to the repository: https://github.com/GOFUVI/hf_eolus_sar_ingestion, 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

9 files.-- Access to catalogs.zip https://doi.org/10.5281/zenodo.17131228, HF-EOLUS project task 2 objective is to achieve HF-Radar Wind Inversion through Artificial Neural Networks training. This Zenodo record documents the methodology and results executed in pursuit of that goal. Readers seeking an extended methodological and results compendium can consult README.md, which delineates each pipeline, its configuration, and the evaluation metrics in detail, 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

9 files.-- Access to catalogs.zip https://doi.org/10.5281/zenodo.17464583, A buoy wind height correction stage has been incorporated through the dockerised wrapper scripts/aggregation/apply_buoy_wind_height_correction.sh, which applies the neutral logarithmic profile to translate the Vilano buoy winds from their native 3 m measurement height to the 10 m reference used across the SAR products. All affected artificial neural network checkpoints were retrained with the corrected buoy series, and the resulting metrics remain within the previously reported uncertainty envelope—no substantial shifts were detected in RMSE, directional error, or range-classification accuracy, This record documents the methodology and results executed in pursuit of that goal. Readers seeking an extended methodological and results compendium can consult README.md, which delineates each pipeline, its configuration, and the evaluation metrics in detail, 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

2 files, This record packages inputs, documentation, scripts and results for the HF-EOLUS Task 3 interpolation experiment over the Vilano HF-radar footprint (open Atlantic, NW Iberia; radar context in [1]). The dataset was produced with the hf-eolus-interpolation-toolkit [2] by ingesting MeteoGalicia's historical WRF_HIST d03 (4 km) winds, refining the grid by a factor of four (native 4 km → ~1 km effective resolution), fitting kriging/IDW models and exporting the resulting GeoParquet layers together with metadata, plots and comparisons against the Puertos del Estado Vilano buoy. Due to size, the interpolation outputs live in two Zenodo supplements; this record points to those packages while keeping the case assets, 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

6 files.-- Access to zip files https://doi.org/10.5281/zenodo.17670561, This record contains the interpolated datasets from 2018 to 2020 for: Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., & Varela Benvenuto, R. (2025). HF-EOLUS Wind Interpolation Toolkit for MeteoGalicia models output (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.17598353, Ministerio de Ciencia, Innovación y Universidades, TED2021-129551B-I00, Peer reviewed

6 files.-- Access to zip files https://doi.org/10.5281/zenodo.17678283, This record contains the interpolated datasets from 2021 to 2023 for: Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., & Varela Benvenuto, R. (2025). HF-EOLUS Wind Interpolation Toolkit for MeteoGalicia models output (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.17598353., Ministerio de Ciencia, Innovación y Universidades, TED2021-129551B-I00, Peer reviewed

2 files, Offshore wind resource assessment is critical for wind energy development, but direct long-term measurements are often sparse. High-frequency (HF) coastal radars offer a potential alternative by indirectly sensing sea-surface winds through their backscatter signals. In the Vilano case study (Galician coast, NW Spain), we leverage an artificial neural network (ANN) that inverts HF radar observations into near-surface wind estimates. This approach (developed under the HF-EOLUS project, Zenodo DOI: 10.5281/zenodo.17464519) enables the retrieval of 10 m wind fields over the coastal ocean by combining HF radar data with limited satellite and buoy observations. The motivation of this case study is to evaluate how well HF-radar--derived winds can characterize the offshore wind resource at a site of interest (the Vilano-Sisargas buoy location) and to demonstrate a workflow for deriving wind resource metrics from remote sensing data, 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

1 file, Offshore wind resource assessment is critical for wind energy development, but direct long-term measurements are often sparse. High-frequency (HF) coastal radars offer a potential alternative by indirectly sensing sea-surface winds through their backscatter signals. In the Vilano case study (Galician coast, NW Spain), we leverage an artificial neural network (ANN) that inverts HF radar observations into near-surface wind estimates. This approach (developed under the HF-EOLUS project, Zenodo DOI: 10.5281/zenodo.17464519) enables retrieval of 10 m wind fields over the coastal ocean by combining HF radar data with limited satellite and buoy observations. The motivation of this case study is to evaluate how well HF-radar--derived winds can characterize the offshore wind resource at a site of interest (the Vilano-Sisargas buoy location) and to demonstrate a workflow for deriving wind resource metrics from remote sensing data, 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
- ctas.sql: Athena CTAS statements for CSV→Parquet conversion
- pipeline.sh: Orchestration script for ingestion and metadata generation
- stac_properties_collection.json: STAC collection‑level properties
- stac_properties_item.json: STAC item‑level properties
- buoy_ingestion.tar.gz: Compressed archive of the STAC catalog and associated GeoParquet asset(s), This record contains the static STAC catalog for the HF-EOLUS ingestion of Puertos del Estado (PdE) buoy wind observations for the Vilano buoy, along with the helper SQL and scripts used to produce the data. The pipeline consolidates per‑buoy time series (wind speed and direction) into analysis‑ready GeoParquet and describes them with STAC for discovery and interoperability. For more details, see the repository, 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

Data preparation stages pivot the radar spectra around Bragg peaks, validate the geometrical consistency of each station, enrich the features with bearings, distances and maintenance metadata, and standardise schema variations across radar sites. Stratified partitions, cross-domain concatenation, and maintenance-aware normalisation ensure that model training, validation and inference rely on reproducible datasets that capture both spatial variability and temporal stability.

Training routines span hyper-parameter optimisation, final non-cross-validated retraining, and multiple fine-tuning strategies—plain adaptation, L2-SP anchoring, and knowledge distillation—to explore cross-domain transfer between the spatially rich SAR corpus and the buoy's high-fidelity point measurements. Inference jobs cover grid-wide predictions, domain-specific test sets, and maintenance-interval diagnostics, delivering quantitative metrics and provenance-rich artefacts that can be catalogued alongside the input datasets. By packaging the entire pipeline, the repository enables systematic experimentation with HF-radar wind inversion models while providing the documentation necessary for reproducibility and scientific auditing, The HF-EOLUS toolkit consolidates all command-line pipelines and artefact definitions required to retrieve near-surface wind speed and direction from HF-radar observations via artificial neural networks. It couples radar-derived backscatter statistics with two supervisory references: Sentinel-1 Level-2 OCN products aggregated on regular spatial grids and collocated in-situ winds measured by an oceanographic buoy embedded in the same footprint. The workflow respects the frequency-dependent radar operating band by combining gated regression heads with a complementary range-classification branch, thereby preserving the physical constraints of the inversion problem. For comprehensive documentation and reproducibility artefacts, consult the project repository on GitHub, 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

Data preparation stages pivot the radar spectra around Bragg peaks, validate the geometrical consistency of each station, enrich the features with bearings, distances and maintenance metadata, and standardise schema variations across radar sites. Stratified partitions, cross-domain concatenation, and maintenance-aware normalisation ensure that model training, validation and inference rely on reproducible datasets that capture both spatial variability and temporal stability.

Training routines span hyper-parameter optimisation, final non-cross-validated retraining, and multiple fine-tuning strategies—plain adaptation, L2-SP anchoring, and knowledge distillation—to explore cross-domain transfer between the spatially rich SAR corpus and the buoy's high-fidelity point measurements. Inference jobs cover grid-wide predictions, domain-specific test sets, and maintenance-interval diagnostics, delivering quantitative metrics and provenance-rich artefacts that can be catalogued alongside the input datasets. By packaging the entire pipeline, the repository enables systematic experimentation with HF-radar wind inversion models while providing the documentation necessary for reproducibility and scientific auditing, [What's New]
A buoy wind height correction utility (scripts/aggregation/apply_buoy_wind_height_correction.sh) now accompanies the pipeline. The helper encapsulates the neutral logarithmic wind-profile adjustment inside a containerised execution environment, accepting configurable source and target heights so deployments can harmonise buoy winds to any reference level without manual scripting. Documentation and the illustrative data-preparation pipeline reflect this addition, ensuring that downstream partitions ingest height-standardised buoy tables while preserving the raw measurements for provenance and auditing workflows, The HF-EOLUS toolkit consolidates all command-line pipelines and artefact definitions required to retrieve near-surface wind speed and direction from HF-radar observations via artificial neural networks. It couples radar-derived backscatter statistics with two supervisory references: Sentinel-1 Level-2 OCN products aggregated on regular spatial grids and collocated in-situ winds measured by an oceanographic buoy embedded in the same footprint. The workflow respects the frequency-dependent radar operating band by combining gated regression heads with a complementary range-classification branch, thereby preserving the physical constraints of the inversion problem. For comprehensive documentation and reproducibility artefacts, consult the project repository on GitHub (https://github.com/GOFUVI/hf-wind-inversion), 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

1 file, The HF EOLUS Wind Resource Toolkit packages a reproducible workflow for deriving offshore wind-resource metrics from HF-radar neural-network inference products. It bundles a CLI, reusable Python modules (hf_wind_resource), configuration templates, and Docker-based helpers so that researchers can transform catalogued GeoParquet inputs into power-ready deliverables without rewriting analytics or orchestration code. The toolkit is dataset-agnostic: any STAC snapshot conforming to the documented ANN schema can be processed after updating the catalogue configuration files under config/. Please check the whole repository on GitHub, 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

1 file, The HF EOLUS Wind Resource Toolkit packages a reproducible workflow for deriving offshore wind-resource metrics from HF-radar neural-network inference products. It bundles a CLI, reusable Python modules (hf_wind_resource), configuration templates, and Docker-based helpers so that researchers can transform catalogued GeoParquet inputs into power-ready deliverables without rewriting analytics or orchestration code. The toolkit is dataset-agnostic: any STAC snapshot conforming to the documented ANN schema can be processed after updating the catalogue configuration files under config/, 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

1 file, HF-EOLUS Puertos del Estado Buoy Ingestion Pipeline converts PdE buoy wind observations into analysis-ready, cloud-optimized GeoParquet with accompanying STAC metadata. It consolidates per-buoy time series (wind speed and direction) into a single GeoParquet file per buoy and publishes a static STAC catalog for easy discovery and interoperability. The approach leverages open standards to enable efficient querying and integration with common geospatial and data science tools. For more details, please see the repository, 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

1 file, Tools to ingest and catalog HF‑Radar “Radial Metrics” (CODAR LLUV) into analysis‑ready GeoParquet. The pipeline converts LLUV files into three GeoParquet v1.1 datasets on AWS S3 (echo‑level radial metrics, header metadata, and range summaries), integrates with AWS Glue for distributed parsing and AWS Athena for SQL querying, and optionally generates station‑level STAC Collections/Items linking to the Parquet assets. See the repository README for setup, usage, and technical details, 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

1 file, S1OCN is an R package for browsing, downloading, and extracting data from Sentinel‑1 Level‑2 OCN (Ocean) products. For more details, please visit S1OCN on GitHub, 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