CloudViT: classifying cloud types in global satellite data and in kilometre-resolution simulations using vision transformers

Abstract. Clouds constitute, through their interactions with incoming solar radiation and outgoing terrestrial radiation, a fundamental element of the Earth’s climate system. Different cloud types show a wide variety in cloud microphysical or optical properties, phase, vertical extent or temperature among others, and thus disparate radiative effects. Both in observational and model datasets, classifying cloud types is also of large importance since different cloud types respond differently to current and future anthropogenic climate change. Cloud types have traditionally been defined using a simplified partition of the space determined by spatially aggregated values e.g. of the cloud top pressure and the cloud optical thickness. In this study, we present a method called CloudViT (Cloud Vision Transformer) building upon spatial extracts of cloud properties from the MODIS instrument to derive cloud types, leveraging spatial features and patterns with a vision transformer model. The classification model is based on global surface observations of cloud types. The method is then evaluated through the distributions of cloud type properties and the corresponding spatial patterns of cloud type occurrences for a global cloud type dataset produced over a year-long period. Subsequently, a first application of the cloud type classification method to climate model data is presented. This application additionally provides insights into how global storm-resolving models are representing clouds as these models are increasingly being used to perform simulations. The global cloud type dataset and the method code constituting CloudViT are available from Zenodo (Lenhardt et al., 2024b).