Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"

This repository contains the RNN training and evaluation code used in the paper Vertically recurrent neural networks for sub-grid parameterization

 The radiative transfer emulation data can be accessed with through a Climetlab plugin (Climetlab-maelstrom-radiation). 

Datasets are downloaded and explained in the demo notebook here https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb
In addition the full training and testing code used in the paper is uploaded here (pu-maelstrom-radiation.tar.gz).

Three parameterization problems from earlier studies are also included (we have modified the code from these papers to incorporate RNNs): 

non-orographic gravity wave drag (Chantry et al. 2021) 

Based on TensorFlow

This repository uses the CliMetLab plugin and downloads the data from the European Weather Cloud

non-local parameterization (Wang et al. 2022)

The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everything

See original paper for data access

moist physics (Han et al. 2023, 2020) 

Based on TensorFlow and PyTorch. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paper, autoregressive training and experimental model architectures in PyTorch

See original paper for data access

Each of the code repos (unpack the tars) have an updated README.

References:

Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., & Palmer, T. (2021). Machine learning emulation of gravity wave drag in numerical weather forecasting. Journal of Advances in Modeling Earth Systems, 13(7), e2021MS002477

Han, Y., Zhang, G. J., Huang, X., & Wang, Y. (2020). A moist physics parameterization based on deep learning. Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002076.

Han, Y., Zhang, G. J., & Wang, Y. (2023). An ensemble of neural networks for moist physics processes, its generalizability and stable integration. Journal of Advances in Modeling Earth Systems, 15(10), e2022MS003508

Wang, P., Yuval, J., & O’Gorman, P. A. (2022). Non‐local parameterization of atmospheric subgrid processes with neural networks. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS002984.