LDA_1.41

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The latent data assimilation methods for global atmosphere with the resolution of 1.41 degree.

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# Latent Data Assimilation for Global Atmosphere (1.41° resolution)

This repository contains the implementation of latent data assimilation (LDA) methods for the global atmosphere at a horizontal resolution of 1.41 degrees.

## Contents

- ✅ Autoencoder (AE) architecture
  📄 [`AutoEncoder.py`](./networks/AutoEncoder.py)
- ✅ AI forecast model to realize 4D DA method
  📄 [`forecast_net.py`](./networks/forecast_net.py)
- ✅ Utils for LDA experiments
  📄 [`exp_utils.py`](./LDA_Methods/exp_utils.py)
- ✅ A unified code for Latent 3D-Var (L3DVar) and Latent 4D-Var (L4DVar).
  📄 [`Latent_Var.py`](./LDA_Methods/Latent_Var.py)
- ✅ An Observing System Simulation Experiment (OSSE) example to apply LDA.
  📄 Example notebook: [`LDA_OSSEs.ipynb`](./DA_exps/LDA_OSSEs.ipynb)

The checkpoints for models are provided in https://zenodo.org/records/17210930.

## Paper
Fan, H., Bai, L., Fei, B., Xiao, Y., Chen, K., Liu, Y., Qu, Y., Ling, F., & Gentine, P.  
**Physically consistent global atmospheric data assimilation with machine learning in latent space.**  
*Science Advances*, 2026.

Paper link: https://www.science.org/doi/10.1126/sciadv.aea4248

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