Code and partial data for "Representing sub-grid processes in weather and climate models via sequence learning"

This repository contains the RNN training and evaluation code used in the paper Representing sub-grid processes in weather and climate models via sequence learning. Three parameterization problems from earlier studies are included (we have modified the code from these papers to incorporate RNNs): non-orographic gravity wave drag (Chantry et al. 2021) Based on TensorFlowThis repository uses the CliMetLab plugin and downloads the data from the European Weather Cloudnon-local parameterization (Wang et al. 2022)The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everythingSee original paper for data accessmoist physics (Han et al. 2023, 2020) Based on TensorFlow. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paperSee original paper for data accessEach 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.