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Repository Overview
This repository contains an end-to-end workflow for extracting, regridding, and preparing RiOMar data on a HEALPix grid. It includes notebooks and tools to:
Define a Region of Interest (ROI) from a lon/lat bounding box and export parent-level HEALPix cell IDs and a boundary footprint (GeoJSON).
Prepare a temporary Zarr dataset for fast iteration, testing, and reproducible development.
Regrid variables to HEALPix using xarray.apply_ufunc, aligned with the ROI and nested indexing.
Scale to full HPC datasets (planned), with output ready for publication as Zarr.
Additional notebooks provide experiments with parent-level chunks, alternative regridding methods, and Kerchunk virtualized Zarr workflows.
Key Features
EPSG:4326 geographic coordinates to HEALPix (nested/WGS84).
Lightweight workflow for fast testing and reproducible results.
Designed for later scaling to HPC and large datasets.
https://github.com/FAIR2Adapt/softwares_RIOMAR/
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RIOMAR → HEALPix (DGGS) regridding notebooks
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Anne Fouilloux
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High-resolution coastal ocean model outputs are essential for climate change adaptation strategies, yet their large size and complexity create significant barriers to accessibility and use by decision-makers. The RiOMAR (River-influenced Ocean Margin) hindcast simulations (1980--2020) provide valuable insights into water quality degradation along French Atlantic coasts, but transforming these 100+ terabytes of data into actionable knowledge requires systematic FAIRification. This paper presents a comprehensive FAIR (Findable, Accessible, Interoperable, Reusable) data processing pipeline designed within the FAIR2Adapt project that transforms RiOMAR outputs into FAIR Digital Objects. The pipeline regrid curvilinear ocean model outputs to ellipsoidal HEALPix (Hierarchical Equal Area isoLatitude Pixelization) using the healpix-geo library, converts to Analysis-Ready Cloud-Optimized (ARCO) Zarr format, and enriches metadata using STAC (SpatioTemporal Asset Catalog) with oceanographic domain semantics from I-ADOPT. FAIR Digital Objects are packaged as RO-Crates with nanopublications and registered in ROHub for discovery and reuse. The implementation follows AGILE Reproducible Paper Guidelines with version-controlled workflows, containerized environments, and comprehensive provenance metadata. Pilot demonstrations on representative datasets show over 90\% FAIR compliance and establish patterns for scaling to the complete hindcast archive. This reproducible pipeline enables coastal stakeholders to access and analyze critical ocean data for climate adaptation planning.
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Fouilloux, Anne. "FAIR2Adapt Processing Pipeline for Climate Change Adaptation." ROHub. Feb 20 ,2026. https://w3id.org/ro-id/24600867-23ca-4b97-be14-3aa63883c056.
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