End-to-end Climate Data Pipeline for Impact Assessment
-
Updated
Aug 12, 2026 - Jupyter Notebook
End-to-end Climate Data Pipeline for Impact Assessment
Climate extraction and downscaling
Personal learning fork of the Climate Change AI tutorial on deep-learning-based statistical downscaling of climate projections.
Source code for benchmarking training losses in deep learning-based statistical downscaling.
An minimal BCSD package in Python for statistical climate downscaling, modified from `pangeo-data/scikit-downscale`
Reproducible pipeline for the integrated downscaling–BMA–BiLSTM framework: CMIP6 GCM evaluation, two-track downscaling (LARS-WG 8 + DQM), EM-based Bayesian Model Averaging, deep-ensemble streamflow projection, and drought / ETCCDI / compound-extreme analysis. Demonstrated on the Haraz basin, northern Iran.
Add a description, image, and links to the statistical-downscaling topic page so that developers can more easily learn about it.
To associate your repository with the statistical-downscaling topic, visit your repo's landing page and select "manage topics."