HF-EOLUS. Task 2. HF-Radar Wind Inversion Models and Results for VILA and PRIO Stations

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 research combines high-frequency radar backscatter from the INTECMAR-maintained VILA and PRIO stations, Sentinel-1 Ocean (OCN) wind products, and in-situ measurements from the Puertos del Estado Vilano buoy. Together they recover 10 m wind vectors over the Galician continental shelf. The HF-radar streams arrive every 30 minutes, the buoy delivers hourly observations, and Sentinel-1 revisits supply intermittent orbital passes, furnishing a multi-scale temporal tapestry for model training. The initiative focuses on a reanalysis setting rather than real-time operations, enforcing the 5.7–17.8 m s⁻¹ validity gate imposed by the radar frequency throughout data preparation, model training, and evaluation. All signals are harmonised on a 20 km grid (10 km aggregation radius), with HF-radar echoes pivoted by Bragg peak, enriched with geometric descriptors, and tagged with maintenance-interval identifiers that support stability diagnostics and maintenance-aware normalisation.

A feed-forward artificial neural network delivers the inversion, sharing a backbone across three specialised heads: wind-speed regression, wind-direction regression (via sine/cosine outputs), and range classification. Hyper-parameter optimisation tunes depth, width, learning rates, and loss weights before definitive no-cross-validation training runs cement the baseline checkpoints. Downstream, fine-tuning explores three strategies—plain adaptation, L2-SP anchoring, and L2-SP augmented with knowledge distillation and rehearsal—to quantify how far knowledge can be transferred between the SAR grid and the buoy domain without eroding physical guardrails.

The study exposes three end-to-end pipelines. The SAR pipeline trains on Sentinel-1 supervision to capture spatial variability, evaluates both the home grid and the Vilano buoy, and measures the penalties incurred when the SAR backbone is adapted to buoy targets. The Vilano pipeline mirrors this flow, starting from the high-fidelity but point-scale buoy measurements before attempting to generalise across the grid. A third pipeline merges both corpora into a unified training/testing split to deliver a single cross-domain checkpoint. Complementary workflows extend the evaluation to a grid displaced 10 km east and north, stress-testing spatial robustness, and compute feature-importance diagnostics that highlight the dominant role of Bragg-peak power statistics, the stabilising influence of radial-velocity aggregates, and the conditional utility of geometric bearings when models venture outside their training domain.

Results confirm that each domain retains its native champion: the SAR baseline posts the lowest in-range root-mean-square error on the satellite grid (≈2.0 m s⁻¹ with ~29° directional RMSE), the Vilano baseline achieves ~1.9 m s⁻¹ and ~31° on the buoy test set, and the joint model sits within tenths of those figures while harmonising behaviour across domains. Cross-domain fine-tuning reveals a marked asymmetry. Regularised SAR→Vilano transfers (L2-SP and L2-SP+KD) cut matched RMSE on the buoy from 2.74 to ~1.6 m s⁻¹ and trim directional error from 43° to ~28°, whereas Vilano→SAR adaptations struggle to match the grid-side baseline even as they improve buoy performance. Plain fine-tuning maximises buoy gains but inflicts severe forgetting on the source domain and degrades the range classifier, underscoring the value of rehearsal and distillation when balanced performance is required.

Reproducibility is encoded in hard-coded orchestration scripts (run_*_pipeline.sh) and mirrored directory structures archived in hf_eolus.zip, capturing configuration manifests, training checkpoints, fine-tuning artefacts, inference metrics, and stage-specific reports. Every materialised dataset passes through GeoParquet consolidation and STAC cataloguing, with the catalogues bundled under catalogs.zip, both preserving the original repository layout so that provenance, spatial metadata, and maintenance-aware normalisation parameters remain auditable. The combined workflow therefore constitutes a physically grounded, fully documented benchmark for HF-radar wind inversion on the Galician shelf, clarifying the trade-offs between spatial coverage and point accuracy when transferring skill across observation domains.

The software toolkit underpinning this research is documented in Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., & Varela Benvenuto, R. (2025). HF-EOLUS HF-Radar Wind Inversion Toolkit for Artificial Neural Networks Training and Inference (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.17369264.

Inputs are documented in:

• Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., & Varela Benvenuto, R. (2025). Project HF‑EOLUS. Task 1. Puertos del Estado Vilano Buoy Data Bundle (GeoParquet + STAC) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17098038
• Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., & Varela Benvenuto, R. (2025). Project HF-EOLUS. Task 2. Aggregated SAR and HF-Radar Radial Metrics for Wind-Inversion Model Training [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17115413.

Acknowledgements

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. Members of the Marine Research Centre (CIM) of the University of Vigo have participated in the development of this repository.

We thank Puertos del Estado for making the Vilano buoy data publicly available through Portus (https://portus.puertos.es).

Aggregated HF-Radar data was derived from spectra from INTECMAR's VILA and PRIO HF-Radar stations, between 2011-09-30 and 2023-11-23 that have been transferred free of charge by the Observatorio Costeiro da Xunta de Galicia (https://www.observatoriocosteiro.gal) for their use. This Observatory is not responsible for the use of these data nor is it linked to the conclusions drawn with them. The Costeiro da Xunta de Galicia Observatory is part of the RAIA Observatory (http://www.marnaraia.org).

Disclaimer

This data and 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.