Published November 7, 2024
| Version v3
Dataset
Open
Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"
Description
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:
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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.
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Additional details
Dates
- Submitted
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2024-11-07