Changelog

Release notes for the framework and the documentation site.

The framework uses DateVer (vYYYY.MM) for releases. Patches within a release use vYYYY.MM.N.

v2026.05 - first public release

The first public release of the Hybrid Bias Correction framework. This is the codebase developed for the MSc thesis on daily satellite precipitation bias correction over Indonesia, refactored into a reproducible Python package for the broader community.

What’s in this release

  • The framework (src/) - Linear Scaling, Empirical Quantile Mapping with a Generalized Pareto tail, and a CNN refinement gated by station-density confidence. Implemented as a Python package with one driver config per region.
  • Notebooks (notebooks/) - end-to-end pipeline from AOI definition and data acquisition through bias correction, metrics, QA, station validation, and visualisation.
  • Bali example bundle (data/example_bali/, 11 MB) - ships with the repo and runs end to end in ~15 minutes on a free Colab CPU.
  • Indonesia data bundle - 2001-2025 inputs, masks, and outputs (~40 GB) published at Zenodo 10.5281/zenodo.20287847.
  • Documentation (this site) - Methodology, Implementation, Tutorials, Case Studies (Bali + Indonesia, with interactive figures), FAQ, and auto-generated API Reference.

Backlog

  • Replace the CNN architecture with a U-Net to avoid the Flatten -> Dense bottleneck.
  • tf.keras.backend.clear_session() between dekads in nb02 for memory predictability on Indonesia-scale runs.
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