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GAN weather2climate

This repository contains the code for the paper "Distribution-Guided Ensemble Postprocessing for S2S Precipitation Forecasts: A Seamless Pathway using Deep Generative Models". The code is published for open research purposes.

Key Model Implementation

The proposed model's key training code is located in scripts/3.train/plmodules.py:

  • CvaeGanModule_v2: Our proposed deep generative model for distribution-guided ensemble postprocessing
  • DetermModule: Used for MOS (Model Output Statistics) baseline experiments

Environment Requirements

Key requirement: PyTorch Lightning version 1.9.5

Other packages (PyTorch, NumPy, xarray, etc.) can be installed with standard versions without specific requirements.

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Code for the paper "Distribution-Guided Ensemble Postprocessing for S2S Precipitation Forecasts: A Seamless Pathway using Deep Generative Models"

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