Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations" ...

• Main Authors: Finn, Tobias, Durand, Charlotte, Farchi, Alban, Bocquet, Marc, Rampal, Pierre, Carrassi, Alberto
• Format: Dataset
• Language: English
• Published: Zenodo 2024
• Subjects: Sea-ice model; Geophysical model; Generative deep learning; Machine learning; Deep learning; Arctic; Sea ice
• Online Access: https://dx.doi.org/10.5281/zenodo.10949056; https://zenodo.org/doi/10.5281/zenodo.10949056

All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".While most of the code is a frozen clone of the original Repository, this capsule also includes the dataset and neural network weights to train and apply the surrogate models. The dataset for training and evaluation can be found at data/nextsim, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the SASIP shared data OpenDAP server: The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework" (Boutin et al., 2023) and available as Zenodo dataset (Boutin et al., 2022). The forcing data is based on the ERA5 reanalysis dataset published in the paper: "The ERA5 global reanalysis" (Hersbach et al., 2020) and available as dataset from ... : This research has received financial support from the project SASIP (grant no. 353) funded by Schmidt Science – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. ...