Monthly summary for 2025-09 — ecmwf/anemoi-core. Key feature delivered: loss-aware feature handling via target indices in the Anemoi training pipeline. Introduced explicit 'target' indices to define features used in loss computation that are not predicted by the model, clarifying the separation between inputs and loss-relevant features and enabling more flexible loss computation and improved interpretability. Major bugs fixed: none reported for this repo in September 2025. Overall impact: enables more flexible loss signaling, improves interpretability of training features, and supports faster, more informed experimentation; traceable to commit d8db2a6fc192bc49107df6c137ce4f56866ae4d4 (#426). Technologies/skills: Python-based ML pipeline enhancements, feature engineering for loss computation, clear change-tracking with Git.

Concise monthly summary for 2025-07 focusing on business value and technical achievements for ecmwf/anemoi-core. Delivered a feature that enables selective loss computation with variable filtering, significantly improving training flexibility and capability to experiment with variable-specific loss behavior. No major bugs fixed this month. Impact: supports targeted optimization and more granular experimentation, potentially improving model performance while reducing unnecessary compute. Skills demonstrated: Python-based loss abstractions, modular loss components, and code refactoring to support variable filtering; traceable changes via commits.

June 2025 monthly summary for ecmwf/anemoi-core. Key feature delivered this month: FFT-based Spatial Spectral Loss Functions (LogFFT2Distance and FourierCorrelationLoss) with Python implementations, documentation, and unit tests. No major bugs fixed in this period; focus was on feature development, code quality, and test coverage. The work provides a more principled spectral comparison for fields, enabling improved training stability and model performance in spectral-domain objectives. Impact includes smoother convergence in FFT-based loss scenarios and clearer metrics for evaluating spectral fidelity. Technologies demonstrated include Python, FFT-based spectral analysis, loss function design, unit testing, and documentation.

December 2024 monthly summary for ecmwf/anemoi-core. Focused on expanding data remapping capabilities and ensuring robustness of diagnostic visuals. Highlights include introducing a modular remapping architecture (Monomapper/Multimapper) with enhanced configuration, fixes to diagnostic plotting for improved accuracy, and consistency improvements in naming conventions.

November 2024 monthly summary focusing on key accomplishments and business impact across ecmwf/anemoi-core and ecmwf/anemoi-transform. Delivered a critical bug fix for Voronoi calculation in AreaWeights and introduced new data transformation capabilities with Rescale and Convert filters. Added comprehensive tests and updated changelogs to reflect these changes. Result: more robust data processing pipelines, reduced edge-case failures, and improved unit-aware transformations.