Raphael Savelli developed and enhanced biogeochemical and estuarine modeling capabilities within the MITgcm-contrib/ecco_darwin repository, focusing on coastal and estuarine systems such as the CCS kelp region, Amazon, and Guayas estuaries. He engineered new model features including 3D macroalgae dynamics, nutrient runoff integration, and sediment transport, leveraging Fortran and Python for both high-performance simulation and workflow automation. His work included porting legacy code to Python, optimizing numerical stability, and implementing robust data processing pipelines using NetCDF and MATLAB scripting. These contributions improved model fidelity, reproducibility, and usability, supporting scenario analysis and streamlined scientific workflows for environmental and climate research.
Month: 2025-12. This period focused on advancing Amazon estuary capabilities in MITgcm-contrib/ecco_darwin by introducing configurable Amazon estuary settings, laying the foundation for hydrodynamic, sediment, and biogeochemical simulations. The work enhances model realism, reproducibility, and deployment readiness for estuarine studies. No major bugs reported this month; ongoing stabilization and parameterization validation planned for next cycle.
Month: 2025-12. This period focused on advancing Amazon estuary capabilities in MITgcm-contrib/ecco_darwin by introducing configurable Amazon estuary settings, laying the foundation for hydrodynamic, sediment, and biogeochemical simulations. The work enhances model realism, reproducibility, and deployment readiness for estuarine studies. No major bugs reported this month; ongoing stabilization and parameterization validation planned for next cycle.
November 2025: Delivered two key workflow enhancements for ECCO Darwin in MITgcm-contrib/ecco_darwin, focusing on data loading/processing and scalable data handling. These changes improve data management, interoperability with the ECCO grid, and researcher productivity.
November 2025: Delivered two key workflow enhancements for ECCO Darwin in MITgcm-contrib/ecco_darwin, focusing on data loading/processing and scalable data handling. These changes improve data management, interoperability with the ECCO grid, and researcher productivity.
October 2025 delivered a new data-processing capability for GloFAS runoff data within the MITgcm-contrib/ecco_darwin repository. Implemented a Python script to process NetCDF runoff fields: compute annual discharge, apply a coastal mask to isolate near-coast runoff, with optional visualization and binary output for downstream use. This work enables automated, reproducible generation of ready-to-use runoff datasets for coastal impact analyses and model initialization, reducing manual data wrangling and speeding downstream modeling workflows.
October 2025 delivered a new data-processing capability for GloFAS runoff data within the MITgcm-contrib/ecco_darwin repository. Implemented a Python script to process NetCDF runoff fields: compute annual discharge, apply a coastal mask to isolate near-coast runoff, with optional visualization and binary output for downstream use. This work enables automated, reproducible generation of ready-to-use runoff datasets for coastal impact analyses and model initialization, reducing manual data wrangling and speeding downstream modeling workflows.
Summary for 2025-09: Delivered end-to-end enhancements to the CCS kelp biogeochemical model within MITgcm-contrib/ecco_darwin, adding nutrient runoff and TSS dynamics to improve coastal nutrient cycling representation, support scenario analysis, and align with external forcing interfaces. Implementations span external forcing fields, runoff-enabled DARWIN_NUTRIENT_RUNOFF, TSS tracer and sinking physics, and unified TSS runoff forcing with tracer consistency. These changes enhance model fidelity for coastal management and enable more robust forecasts of nutrient and sediment dynamics.
Summary for 2025-09: Delivered end-to-end enhancements to the CCS kelp biogeochemical model within MITgcm-contrib/ecco_darwin, adding nutrient runoff and TSS dynamics to improve coastal nutrient cycling representation, support scenario analysis, and align with external forcing interfaces. Implementations span external forcing fields, runoff-enabled DARWIN_NUTRIENT_RUNOFF, TSS tracer and sinking physics, and unified TSS runoff forcing with tracer consistency. These changes enhance model fidelity for coastal management and enable more robust forecasts of nutrient and sediment dynamics.
In August 2025, MITgcm-contrib/ecco_darwin delivered a focused set of enhancements to advance the Python-based biogeochemical modeling workflow, strengthen numerical stability, improve data management, and extend ocean chemistry capabilities. Key outcomes include a port to Python with performance optimizations for the biogeochemical module and its hydrodynamics/transport components; restoration of convergence criteria with adaptive tolerances; an OutputManager refactor for centralized, reliable I/O; CO2 flux modeling and carbonate chemistry enhancements enabling atmosphere-ocean coupling and better chemical tendencies calculations; and northern boundary condition support for OBCS, complemented by the final CGEM v2 release. Documentation updates were also provided to support adoption. These changes deliver business value through faster, more reliable simulations, improved data integrity and reproducibility, expanded physics capabilities, and clearer setup and usage guidance.
In August 2025, MITgcm-contrib/ecco_darwin delivered a focused set of enhancements to advance the Python-based biogeochemical modeling workflow, strengthen numerical stability, improve data management, and extend ocean chemistry capabilities. Key outcomes include a port to Python with performance optimizations for the biogeochemical module and its hydrodynamics/transport components; restoration of convergence criteria with adaptive tolerances; an OutputManager refactor for centralized, reliable I/O; CO2 flux modeling and carbonate chemistry enhancements enabling atmosphere-ocean coupling and better chemical tendencies calculations; and northern boundary condition support for OBCS, complemented by the final CGEM v2 release. Documentation updates were also provided to support adoption. These changes deliver business value through faster, more reliable simulations, improved data integrity and reproducibility, expanded physics capabilities, and clearer setup and usage guidance.
Summary: In July 2025, the MITgcm-contrib/ecco_darwin project delivered end-to-end CGEM enhancements focused on the Guayas Estuary, strengthened data reliability, and expanded visualization and reporting capabilities. Key features delivered include an estuary-specific Guayas CGEM configuration and setup, a Python-based CGEM Animated Visualization Toolkit, enhanced flux calculations by including U.dat, and new carbon- and sediment-budget tools, plus a robust budget plotting suite. Critical bugs were fixed to improve data parsing, CO2 thermodynamics handling, and pressure calculations, increasing model reliability and reproducibility. The work improves decision-support by enabling accurate estuary initialization, transparent budgets, and stakeholder-ready visuals, while expanding the team's technical proficiency in Python tooling, data handling, and CGEM workflows. Collectively, these changes reduce risk, accelerate analysis, and improve the scientific and business value of CGEM runs.
Summary: In July 2025, the MITgcm-contrib/ecco_darwin project delivered end-to-end CGEM enhancements focused on the Guayas Estuary, strengthened data reliability, and expanded visualization and reporting capabilities. Key features delivered include an estuary-specific Guayas CGEM configuration and setup, a Python-based CGEM Animated Visualization Toolkit, enhanced flux calculations by including U.dat, and new carbon- and sediment-budget tools, plus a robust budget plotting suite. Critical bugs were fixed to improve data parsing, CO2 thermodynamics handling, and pressure calculations, increasing model reliability and reproducibility. The work improves decision-support by enabling accurate estuary initialization, transparent budgets, and stakeholder-ready visuals, while expanding the team's technical proficiency in Python tooling, data handling, and CGEM workflows. Collectively, these changes reduce risk, accelerate analysis, and improve the scientific and business value of CGEM runs.
Month: 2025-06 — MITgcm-contrib/ecco_darwin: Delivered region-specific GoM and Bay of Bengal ECCO-Darwin v06 setup enhancements and North Slope C-GEM regional configuration, complemented by comprehensive documentation and reproducible workflows. Key outcomes include expanded GoM ECCO-Darwin v06 setup and input refinements, GoM boundary/physics adjustments for stable simulations, North Slope regional modules and README for usability and citation, and Bay of Bengal v06 setup instructions enabling end-to-end runs (1992–2024).
Month: 2025-06 — MITgcm-contrib/ecco_darwin: Delivered region-specific GoM and Bay of Bengal ECCO-Darwin v06 setup enhancements and North Slope C-GEM regional configuration, complemented by comprehensive documentation and reproducible workflows. Key outcomes include expanded GoM ECCO-Darwin v06 setup and input refinements, GoM boundary/physics adjustments for stable simulations, North Slope regional modules and README for usability and citation, and Bay of Bengal v06 setup instructions enabling end-to-end runs (1992–2024).
May 2025: Delivered a 3D kelp/macroalgae model and integrated it with the Darwin biogeochemical model to simulate nutrient and carbon cycling, including 3D dynamics, vertical structure, growth and uptake, and diagnostics for kelp physiology and light/nutrient interactions. Achieved full 3D coupling between the kelp model and Darwin nutrients/carbon to enable end-to-end ecosystem simulations. Fixed macroalgae surface length cap by introducing mag_cum_length and adjusting mag_length to respect the defined maximum, improving surface-growth realism and model stability. These enhancements position the project for robust coastal carbon uptake and nutrient cycling projections and enable scenario analyses for ecosystem management.
May 2025: Delivered a 3D kelp/macroalgae model and integrated it with the Darwin biogeochemical model to simulate nutrient and carbon cycling, including 3D dynamics, vertical structure, growth and uptake, and diagnostics for kelp physiology and light/nutrient interactions. Achieved full 3D coupling between the kelp model and Darwin nutrients/carbon to enable end-to-end ecosystem simulations. Fixed macroalgae surface length cap by introducing mag_cum_length and adjusting mag_length to respect the defined maximum, improving surface-growth realism and model stability. These enhancements position the project for robust coastal carbon uptake and nutrient cycling projections and enable scenario analyses for ecosystem management.
April 2025 monthly summary for MITgcm-contrib/ecco_darwin: Delivered integrated macroalgae/kelp modeling for the CCS kelp region, including an enablement flag, parameter updates, interface cleanup, and diagnostics improvements, plus kelp mortality and advection enhancements. Completed kelp region configuration cleanup and updated README to reflect new file structure and build process. Resolved key stability issues in kelp dynamics (non-advected kelp behavior, N uptake, zero advection for fixed tracers) and enhanced wave diagnostics. These efforts improve simulation accuracy, reproducibility, and onboarding for users supporting CCS kelp-region modeling.
April 2025 monthly summary for MITgcm-contrib/ecco_darwin: Delivered integrated macroalgae/kelp modeling for the CCS kelp region, including an enablement flag, parameter updates, interface cleanup, and diagnostics improvements, plus kelp mortality and advection enhancements. Completed kelp region configuration cleanup and updated README to reflect new file structure and build process. Resolved key stability issues in kelp dynamics (non-advected kelp behavior, N uptake, zero advection for fixed tracers) and enhanced wave diagnostics. These efforts improve simulation accuracy, reproducibility, and onboarding for users supporting CCS kelp-region modeling.
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