Cirrus formation regimes - Data driven identification and quantification of mineral dust effect

This repository contains the data for the paper: Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike LohmannTitel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effectDate: 2024 Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering)Contents:├── cirrus_cloud_trajectories.ftr├── cluster_input_data.ftr├── cluster_models│ └── temperature_clustering_k4_12│ ├── cloud_ids.npy│ ├── model_params.json│ └── trained_model.hdf5 │ └── temperature_clustering_k4_24│ ├── cloud_ids.npy│ ├── model_params.json│ └── trained_model.hdf5 ├── cluster_predictions.ftr└── readme.txtFor more info, please have a look at the readme.txtThis is an updated version of the data, containing updated models and predictions based on the Journal revisions