June 2026 focused on stabilizing and expanding the weight averaging workflow in the ecmwf/anemoi-core repository. Delivered Weight Averaging Class Integration in the Anemoi Framework, enabling robust interaction with imputers and scalers and preventing shape mismatches and training crashes. Introduced name-based parameter/buffer matching and dedicated copy/swap logic to ensure reliable synchronization between the live model and the averaged snapshot. Added EMA and SWA weight averaging variants and integrated compatibility safeguards for PyTorch Lightning callbacks. Documented usage patterns and guards to steer users away from incompatible stock callbacks. The work is tracked under commit 1a99ae09313f3324608ab4918e0f88505c789e08 (PR reference 1113).

May 2026 monthly recap for ecmwf/anemoi-core: focused on stabilizing learnable residual connections by fixing Pydantic schema alignment to layer constructors, which reduces runtime errors and improves interoperability across configurations. Major change corrected inconsistencies in schemas for learnable residual connections (ScalarOrnsteinConnectionSchema and SpectralOrnsteinConnectionSchema), aligning fields such as regressors, use_mean, and grid with actual constructor signatures. This work prepares the codebase for robust multi-GPU parallel testing and aligns with documentation and test scaffolding priorities. Overall, this fix enhances reliability of core components used by downstream workloads while enabling safer deployment and experimentation across environments.

April 2026: Delivered Configurable Weight Averaging for Model Training in ecmwf/anemoi-core. Refactored the weight averaging pipeline to be controlled via configuration, enabling EMA and SWA across training runs. This aligns with PyTorch Lightning 2.6.0, improving model generalization and stabilizing training, especially for longer rollout horizons. The change includes a migration note due to breaking changes for previous SWA usage and updated documentation previews to reflect the new workflow. Prepared for multi-GPU validation and testing according to project guidelines; scoped for faster experimentation and more robust validation outcomes.

March 2026 monthly summary — Across the ecmwf/anemoi-inference and ecmwf/anemoi-datasets repositories, the month prioritized data fidelity, robustness, and multi-GPU readiness. Key outcomes include enabling correct accumulation of negative values in the Accumulate post-processor and hardening rolling-average calculations against missing dates. These changes improve physical realism of outputs and reliability of analytics for forecasting and data products.

December 2025 monthly summary for ecmwf/anemoi-core: Delivered a configurable progress bar callback for PyTorch Lightning to ensure reliable progress reporting in SLURM/HPC environments, addressing compatibility gaps introduced by Lightning 2.6.0 and output-to-file workflows. This work enhances usability for HPC users and reduces training monitoring overhead. The effort included end-to-end tests across multiple Lightning versions, documentation updates, and alignment with contributor and testing guidelines.

October 2025 (2025-10) Monthly Summary for ecmwf/anemoi-core: Focused on stability, observability, and ensemble capabilities. Delivered three key improvements: Batch Normalization Consistency Correction across Forecasters; Enhanced Observability with MLflow Logging of Variable Scaling; and Ensemble Forecasting Diagnostics and Visualization Callbacks with RolloutEvalEns. These changes improved training stability across forecaster types, enhanced debugging and reproducibility via MLflow, and provided rich tooling for ensemble evaluation. Impact: stronger business value through more reliable forecasts, faster debugging, and better decision-making support. Technologies/skills demonstrated include Python refactoring, MLflow integration, callback architectures for GraphEnsForecaster, and ensemble performance evaluation.

May 2025: Focused on stabilizing ensemble training workflows in ecmwf/anemoi-core by refactoring ensemble configuration management to align with loss function refactor, introducing a dedicated training configuration file and updating existing configs to reference correct training parameters and data filenames. A targeted fix was applied to ensure configs stay compatible with the updated loss and data handling conventions, reducing training-time drift and setup errors.

April 2025 performance summary: Delivered core CRPS ensemble forecasting improvements in ecmwf/anemoi-core including AnemoiEnsModelInterface and dynamic GraphForecaster instantiation to enable flexible ensemble inference across configurations. Strengthened robustness and backward compatibility by refining predict_step to propagate extra keyword arguments across model interfaces and addressing CRPS-specific kwarg handling in ecmwf/anemoi-inference via a safe try-except pattern and a deprecation notice for CrpsRunner. Expanded CRPS-focused documentation to guide architectures, loss functions, training strategies, and configuration. Accompanying commits include fix: Fix inference with kcrps (#277) and fix: Adapt predict_step in model interface to pass on arguments for model classes (#281), docs: Update docs for kcrps. (#258) and docs: Fix minor mistakes in CRPS user guide. (#264), and the deprecation tweak in CRPS inference (#212).

In March 2025, delivered a comprehensive documentation overhaul for the Anemoi-models package within the ecmwf/anemoi-core repository, focusing on improving onboarding, contributor guidance, and maintainability. The update reorganizes the docs, adds dedicated contributor and usage guidance, and aligns cross-references with the updated structure.

January 2025 monthly summary for ecmwf/anemoi-core focusing on key features delivered, major bugs fixed, business value and technical achievements. Highlights a configuration-driven feature for normalization and linear layers and a robust init fix, both contributing to flexibility, reliability and maintainability.