TorchDA

Use deep learning in data assimilation workflow. Paper link

Tutorials (4 notebooks)

notebook	open in colab / kaggle
EnKF Testing Demo on Lorenz 63 Model
LSTM on Lorenz 63 Model
Testing Demo on Variational Methods
Testing Demo of Variational Methods on Shallow Water Model

Updates

• Support all torch native optimizers
• Support early stopping for optimization steps in 3D/4D-Var
• Automatic conversion to sparse matrices for sparse B or R in 3D/4D-Var
• Remove logging module dependency

Package Dependencies

• Python 3.10 or later
• PyTorch (Recommend 2.0 or later)

Installation

• Build a conda environment, run:

conda env create -f environment.yml

then activate it with:

conda activate TorchDA

• Install PyTorch with pip, run:

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip3 install -r requirements.txt

then at the root folder of this repo, run:

pip3 install .

• Install torchda from source, run:

pip3 install git+https://github.com/acse-jm122/torchda.git