The Core Model Suite, developed in the EDITO Model Lab project for the EDITO platform, is a collection of ocean modelling tools accessible in the Training section . It includes three main sub-sections: Modelling Components (models developed for the core suite), Deep Differential Emulators (AI-based tools from WP2), and Additional Modules (supporting tools like pre-/post-processing). Users can explore the suite either through its dedicated section or via the “Ocean Modelling” category on the training section’s main page.
Typology of Core Model Suite resources
The Core Model Suite includes the following resources:
📚 Documentations: READMEs (general descriptions) and tutorials (usage guides).
🖥️ Applications: Services via Jupyter notebooks (for intermediate users) or web UIs (for end-users), as well as computational processes.
🔧 Codes and configurations: Container images (Docker, Singularity, Apptainer) with build recipes, and code configurations.
💾 Data: Access to simulation data, including reference simulations and data catalogs.
Technical Readiness Level of Core Model Suite
The TRL assessment for Core Model Suite components ranges from TRL 5 (demonstration integration) to TRL 8 (performance optimization and qualification). Different resources (model configurations, DDEs, datasets) may reach different TRLs:
🧪 TRL 5: Components with basic integration, including reference codes, simulations, tutorials, and code configurations.
🔍 TRL 6: Components with reference datasets and completed integration, allowing intermediate users to build applications using pre-built packages and accessible data.
🤝 TRL 7: Full integration with complete workflows, accessible reference data, ready for user engagement.
🚀 TRL 8: Final stage, where components are qualified, optimized, and extensively tested—including user engagement (e.g., Model-Lab Hackathon) and performance quantification.
List of core model suite components
Resource name | Maintainer | Type | Links | TRL | Technical information | Comments | |
|---|---|---|---|---|---|---|---|
NEMO | Nemo-apptainer test | MOi | Process | 5 | HPC (LUMI) Container (Apptainer) | Used to test HPC containerization (on LUMI) – shared at Barcelona meeting. Runs NEMO4.2 from Apptainer with a GYRE_PISCES configuration. | |
NEMO (ORCA36) | ORCA36 model outputs | MOI | Service – Jupyter Notebook | 7 | Data Lake | Load, process and plot data from ORCA36 model outputs See section 3.1 in EDITO-Model Lab_D3.2_Models and Configurations | |
NEMO (ORCA36) | ORCA36 NEMO5-Bench | MOI | Code | 7 | HPC | Shared NEMO5 Bench for ORCA36 HPC optimization See section 3.1 in EDITO-Model Lab_D3.2_Models and Configurations | |
NEMO (SURF) | Surf-nemo | CMCC | Service – Web user interface | 8 | HPC Container (docker) | Hands-on presentation shared to EDITO Model-Lab Hackathon participants. See section 3.5 in EDITO-Model Lab_D3.2_Models and Configurations | |
NEMO (GLO4ENS) | GLO4Ens / MOISICEEF | MOi | Data | 8 | Data Lake | Global Ensemble Forecasting System – 1/4° 50 members developed in collaboration with ACCIBERG Horizon Europe project. Only seaice concentrations available on Catalog yet, but 2D surface variables to come soon. | |
WW3 (uGLOB) | WW3 uGLOB CMCC | CMCC | Code | 6 | HPC | The uGLOB simulations for 2019-2020 simulations used as forcing data in FA2 (Pacific Green Corridor). See section 2.8 in EDITO-Model Lab_D3.2_Models and Configurations for model & section 3.2 for uGLOB-wave configuration. | |
SHYFEM-MPI (ERM) | SHYFEM MPI EMR CMCC | CMCC/UNIBO | Code | 5 | HPC | See section 2.3 in EDITO-Model Lab_D3.2_Models and Configurations for model and 3.4 for ERM configuration. | |
HBMos | HBM Model and HBMos configurations | DMI | Service - Jupyter Notebook | 7 | HPC | See section 2.2 in EDITO-Model Lab_D3.2_Models for model and Configurations and 3.6 for HBMos configuration. | |
HBMos | Fate and pathways of microplastics in the Baltic Sea | DMI | Service – Web user interface | 8 | Container (docker) | See section 3.6 in EDITO-Model Lab_D3.2_Models and Configurations. Application shared at UNOC3 and EDITO Model-Lab Hackathon. | |
HBMos | Microplastic Cleaning scenarios in the Baltic Sea | DMI | Service – Web user interface | 8 | Container (docker) | See section 3.6 in EDITO-Model Lab_D3.2_Models and Configurations | |
Delft3D FM (DWSM-Waq & DWSM-Mud) | Delft3dfm-modelbuilder | DELTA-RES | Service Jupyter Notebook | 8 | Container (docker) | Set up a Delft3D FM model from scratch with the dfm_tools modelbuilder via the JupyterLab IDE and Python 3.11/3.12. Hands-on presentation shared to EDITO Model-Lab Hackathon participants. See section 2.5 in EDITO-Model Lab_D3.2_Models and Configurations | |
Delft3D FM (DWSM-Waq & DWSM-Mud) | Delft3dfm-run-docker | DELTA-RES | Process – software in container | 8 | Container (docker) | Runs a containerised version of Delft3D FM 2D3D-HMWQ (Hydrodynamics, Morphology, Water Quality) software on Cloud Platform.Hands-on presentation shared to EDITO Model-Lab Hackathon participants. See section 2.5 in EDITO-Model Lab_D3.2_Models and Configurations for model and sections 3.7 & 3.8 for DWSM-Waq & DWSM-Mud configurations. | |
Delft3D FM (DWSM-Waq & DWSM-Mud) | Delft3dfm-hpc | DELTA-RES | Process – software in container | 7 | HPC Container (Apptainer) | Runs an Apptainer container of Delft3D FM 2D3D-HMWQ (Hydrodynamics, Morphology, Water Quality) software. Running a Delft3D FM model has been tested on Euro-HPC facilities (Marenostrum V cluster). This workflow is published as a service and allow users to run larger simulations through multi-node computations. See section 2.5 in EDITO-Model Lab_D3.2_Models and Configurations | |
SCHISM | Auto-schism2 | Hereon/BSC | Service - Jupyter Notebook | 6 | HPC (MN5) Autosubmit Container (singularity) | Precomputed data embedded in the “nbs-explorer” (WiS 1) Nature-based Solution use case and model results served as a basis for the development of the FA1. See section 2.6 in EDITO-Model Lab_D3.2_Models and Configurations | |
SCHISM-GB (NBS) | Nbs-explorer | Hereon | Process | 8 | Container | See section 3.9 in EDITO-Model Lab_D3.2_Models and Configurations for SCHISM-GB configuraiton. | |
SCHISM-SNS + Elbe (Biodiv-explorer) | Biodiv-explorer | Hereon | Process | 8 | Container | Reference data published on EDITO Catalog. See section 3.10 in EDITO-Model Lab_D3.2_Models and Configurations for SCHISM-SNS configuraiton. | |
4DVarNet-turbidity | Sea Surface Turbidity case studies for Dutch and German Wadden Sea | IMT | Service - Jupyter Notebook | 8 | Code | A notebook to run in inference model a pre-trained DDE. Application shared at EDITO Model-Lab Hackathon. | |
GLONET | End-to-end Global Neural Forecasting System | MOi | Data – Daily forecasts | 8 | Data Lake | Dedicated website for documentation. | |
On demand GLONET Forecasts | MOi | Process | 8 | Container | Restricted access to process (limited amount of resources). | ||
OceanBench | OceanBench | MOi | Service – Jupyter Notebook | 6 | Code | Used for demonstration at Toulouse 1st integration meeting | |
SR-DA | Super-resolution data assimilation | MOi | Service – Jupyter Notebook | 6 | Code | A notebook to run in inference model a pre-trained DDE. Application shared at EDITO Model-Lab Hackathon. | |
SFINCS | SFINCS | DELTA-RES | Process + Service Jupyter Notebook | 7 | Container | Runs a docker container of SFINCS for CPU’s (link) or for GPU’s (link). For a full workflow for using SFINCS to assess the impact of Nature-based Solutions on flood scenarios - from setting up the model to running and visualizing – a descriptive service was made available. | |
D-EcoImpact | D-ecoimpact | DELTA-RES | Process | 8 | Container | Runs a docker container of D-EcoImpact, which is a Python based kernel to perform spatial and environmental impact assessment). Generally applicable to spatial data in U-Grid format, e.g. Delft3D FM, SHYFEM-MPI or SCHISM (with some extra post-processing). | |
MEDSLIK-II | Witoil-cloud | CMCC | Service – Web user interface | 8 | Container | Demonstrations of service done at UNOC3. | |
VISIR-2 | Pacific Green Corridor | CMCC | Service – Web user interface | 8 | Container | Demonstrations of service done at UNOC3 and DOF4. | |
OpenDrift | OpenDrift | MOi | Service – Jupyter Notebook | 8 | Container | Developed in collaboration with ACCIBERG Horizon Europe project. Used as template service for application publication for EDITO Model-Lab Hackathon (and re-used by at least 3 teams over 13). Demonstrations of service done at UNOC3 and DOF4. |