Data and codes for a deep convolutional residual neural network moist physics parameterization (ResCu). In this new version, the randomly selected training data samples and part of testing data samples (June, July and August) are provided. They are processed into a new data structure, which can be directly utilized in training and testing. For the entire second year training samples and the entire third year testing samples, we provide them in a repository at Dryad (https://doi.org/10.6075/J0CZ35PP and https://doi.org/10.6075/J03J3BGF). Please download and decompress ResCu_Han_et_al_JAMES.tar.gz. Follow the instructions in README.txt and download the training and testing data (The Dryad depositary is provided in the description). Here we provide 3 parts of data and codes: 1, Training and testing data from SPCAM; 2, Training and testing codes for ResCu and many other NN architectures; 3, SCAM validations.

{"references": ["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, e2020MS002076. https://doi.org/10.1029/2020MS002076"]}