Iñigo López-García developed advanced climate data processing and modeling tools for the google-research/swirl-dynamics repository, focusing on scalable workflows and robust analytics. He engineered distributed pipelines using Python, Apache Beam, and Xarray to enable trend analysis, bias correction, and downscaling of large-scale climate datasets. His work integrated deep learning models, probabilistic forecasting, and diffusion-based generative methods, supporting reproducible research and high-fidelity projections. By refactoring code, enhancing documentation, and improving data handling, Iñigo ensured maintainable, reliable pipelines for climate science applications. His contributions addressed operational reliability, data quality, and end-to-end analytics, demonstrating depth in scientific computing and machine learning integration.
September 2025 monthly summary for google-research/swirl-dynamics. Focused on expanding diffusion modeling capabilities, enriching evaluation metrics, and improving project documentation. Delivered t-Student distribution-based diffusion models, added unreliability_score metric for probabilistic forecasts, enabled heavy-tailed model inference via script refactor, and improved GenFocal README readability and installation instructions.
September 2025 monthly summary for google-research/swirl-dynamics. Focused on expanding diffusion modeling capabilities, enriching evaluation metrics, and improving project documentation. Delivered t-Student distribution-based diffusion models, added unreliability_score metric for probabilistic forecasts, enabled heavy-tailed model inference via script refactor, and improved GenFocal README readability and installation instructions.
2025-08 Monthly summary for google-research/swirl-dynamics: Focused on delivering humidity diagnostics enhancements, advanced thermodynamics integration, and pipeline reliability improvements across the climate dynamics workflow. Key features delivered include refined humidity diagnostics (RH calculation refactor) with new support utilities, and an advanced Heat Index model based on Lu & Romps (2022) with NOAA compatibility. Major reliability fixes ensure the final inference batch is persisted to the Zarr store, and humidity variable mappings/climatology logic were cleaned up for maintainability. These efforts improve data quality, modeling fidelity, and operational robustness, enabling more accurate climate insights and downstream decision-making.
2025-08 Monthly summary for google-research/swirl-dynamics: Focused on delivering humidity diagnostics enhancements, advanced thermodynamics integration, and pipeline reliability improvements across the climate dynamics workflow. Key features delivered include refined humidity diagnostics (RH calculation refactor) with new support utilities, and an advanced Heat Index model based on Lu & Romps (2022) with NOAA compatibility. Major reliability fixes ensure the final inference batch is persisted to the Zarr store, and humidity variable mappings/climatology logic were cleaned up for maintainability. These efforts improve data quality, modeling fidelity, and operational robustness, enabling more accurate climate insights and downstream decision-making.
July 2025 performance summary for google-research/swirl-dynamics: Delivered Colab-ready stability improvements and launched a new family of tropical cyclone statistics notebooks. Key work includes pinning key dependencies to enable Colab-based inference (orbax-checkpoint 0.6.3, numcodecs 0.15.0), updating the OS_downscaling_inference.ipynb, and switching the Colab notebook backend from gfile_backend to tf_backend to improve reliability. Implemented a stability fix to the TC Colab filesys backend. Introduced a comprehensive TC statistics notebook suite with visualizations for tracks from ERA5, LENS2, GenFocal, downstream ensemble track visualizations, counts and track length analyses, and wind scale/cyclogenesis calibration against ERA5 and LENS2. These changes enable reproducible experiments, broaden accessibility via Colab, and provide end-to-end analytics from data ingest to visualization, aligning with business goals of faster insight and collaborative workflows.
July 2025 performance summary for google-research/swirl-dynamics: Delivered Colab-ready stability improvements and launched a new family of tropical cyclone statistics notebooks. Key work includes pinning key dependencies to enable Colab-based inference (orbax-checkpoint 0.6.3, numcodecs 0.15.0), updating the OS_downscaling_inference.ipynb, and switching the Colab notebook backend from gfile_backend to tf_backend to improve reliability. Implemented a stability fix to the TC Colab filesys backend. Introduced a comprehensive TC statistics notebook suite with visualizations for tracks from ERA5, LENS2, GenFocal, downstream ensemble track visualizations, counts and track length analyses, and wind scale/cyclogenesis calibration against ERA5 and LENS2. These changes enable reproducible experiments, broaden accessibility via Colab, and provide end-to-end analytics from data ingest to visualization, aligning with business goals of faster insight and collaborative workflows.
April 2025 (2025-04) – Focused on advancing data readiness and publication support for swirl-dynamics. Key features delivered across google-research/swirl-dynamics include: Climate data processing and ML inference pipelines with new scripts for computing time-rolling climate variables, spatial interpolation, data transformation for inference, singleton dimension handling, plus improved logging and normalization stats handling in the downscaling pipeline. Geographic data preprocessing utilities were added to convert longitudes between [0, 360] and [-180, 180] formats. The R2-D2 model publication details were refreshed in README, including the latest year, DOI, and a new 'How to Cite' section. These changes reduce data-prep time, improve model input consistency, and streamline publication workflows.
April 2025 (2025-04) – Focused on advancing data readiness and publication support for swirl-dynamics. Key features delivered across google-research/swirl-dynamics include: Climate data processing and ML inference pipelines with new scripts for computing time-rolling climate variables, spatial interpolation, data transformation for inference, singleton dimension handling, plus improved logging and normalization stats handling in the downscaling pipeline. Geographic data preprocessing utilities were added to convert longitudes between [0, 360] and [-180, 180] formats. The R2-D2 model publication details were refreshed in README, including the latest year, DOI, and a new 'How to Cite' section. These changes reduce data-prep time, improve model input consistency, and streamline publication workflows.
March 2025 monthly summary for google-research/swirl-dynamics highlighting key features delivered, major fixes, and overall impact for business value and technical excellence.
March 2025 monthly summary for google-research/swirl-dynamics highlighting key features delivered, major fixes, and overall impact for business value and technical excellence.
February 2025 monthly summary for google-research/swirl-dynamics: Delivered three main outcomes focusing on evaluation robustness, climatology workflow reliability, and enhanced user guidance. The work emphasizes business value by enabling configurable data masking during model evaluation/inference, robust detrended climatology processing across varying chunking and spatial dimensions, and improved documentation to accelerate adoption and reproducibility across teams.
February 2025 monthly summary for google-research/swirl-dynamics: Delivered three main outcomes focusing on evaluation robustness, climatology workflow reliability, and enhanced user guidance. The work emphasizes business value by enabling configurable data masking during model evaluation/inference, robust detrended climatology processing across varying chunking and spatial dimensions, and improved documentation to accelerate adoption and reproducibility across teams.
January 2025 monthly summary for google-research/swirl-dynamics focusing on delivering scalable climate downscaling capabilities, robustness improvements, and reproducible workflows. Key features delivered include STAR-ESDM bias correction and downscaling framework with distributed processing and central utilities, upper tail dependence analysis, baseline dataset recognition with default derived variables, flexible data I/O with multiple xarray backends, LOCA weather analog collection, and Colab notebooks illustrating probabilistic downscaling and uncertainty analysis. Documentation updates for R2-D2 model completed. No explicit major bugs fixed reported this month; the emphasis was on refactoring, reliability, and tooling. Overall impact: enhanced data fidelity, scalable processing, and faster experimentation cycles enabling more reliable climate projections and risk assessment. Technologies/skills demonstrated: distributed processing, xarray backends, data I/O abstraction, scripting for LOCA and tail dependence analyses, Colab notebooks, and model documentation.
January 2025 monthly summary for google-research/swirl-dynamics focusing on delivering scalable climate downscaling capabilities, robustness improvements, and reproducible workflows. Key features delivered include STAR-ESDM bias correction and downscaling framework with distributed processing and central utilities, upper tail dependence analysis, baseline dataset recognition with default derived variables, flexible data I/O with multiple xarray backends, LOCA weather analog collection, and Colab notebooks illustrating probabilistic downscaling and uncertainty analysis. Documentation updates for R2-D2 model completed. No explicit major bugs fixed reported this month; the emphasis was on refactoring, reliability, and tooling. Overall impact: enhanced data fidelity, scalable processing, and faster experimentation cycles enabling more reliable climate projections and risk assessment. Technologies/skills demonstrated: distributed processing, xarray backends, data I/O abstraction, scripting for LOCA and tail dependence analyses, Colab notebooks, and model documentation.
December 2024 — google-research/swirl-dynamics: Delivered scalable climate data trend analysis capabilities and improved repository maintainability. Key features delivered include: Climate Trend Analysis Toolkit (Python script for polynomial trend fitting on Zarr data via Apache Beam) and a CLI for detrended climatologies using distributed processing and statistics; Project structure cleanup renaming input_pipelines/bcsd.py to analysis/bcsd.py with no functional changes. Major bugs fixed: none reported. Overall impact: enables at-scale climate trend analysis and reproducible workflows, reducing processing time and future maintenance overhead. Technologies demonstrated: Python, Apache Beam, Zarr, distributed processing, CLI tooling, and code refactoring.
December 2024 — google-research/swirl-dynamics: Delivered scalable climate data trend analysis capabilities and improved repository maintainability. Key features delivered include: Climate Trend Analysis Toolkit (Python script for polynomial trend fitting on Zarr data via Apache Beam) and a CLI for detrended climatologies using distributed processing and statistics; Project structure cleanup renaming input_pipelines/bcsd.py to analysis/bcsd.py with no functional changes. Major bugs fixed: none reported. Overall impact: enables at-scale climate trend analysis and reproducible workflows, reducing processing time and future maintenance overhead. Technologies demonstrated: Python, Apache Beam, Zarr, distributed processing, CLI tooling, and code refactoring.
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