A Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology Based on Deep Learning Methods

Abstract. Marine low clouds tend to organize into larger mesoscale patterns with distinct morphological appearances over the ocean, referred to as mesoscale morphology. While prior studies have mainly examined the fundamental characteristics and shortwave radiative effects of these mesoscale morphologies, their behaviour in the nighttime marine boundary layer (MBL) remains underexplored due to limited observations. To address this, we created a global classification dataset of daytime and nighttime mesoscale morphologies of marine low clouds using a deep residual network model and Moderate Resolution Imaging Spectroradiometer (MODIS) infrared radiance data, with machine-learning-retrieved all-day cloud optical thickness aiding in model training. We analysed day-night contrasts in climatology, seasonal cycles, and cloud properties of different cloud morphology types in this study. Results show that relative frequency of occurrence (RFO) of closed mesoscale cellular convection (MCC) significantly increase at night, while that of suppressed cumulus (Cu) shows a remarkable decrease. Disorganized MCC and clustered Cu display a slight frequency increase during night. In addition, solid stratus and three MCC types exhibit distinct seasonal variations, whereas two cumuliform types show no clear seasonal cycle. Our dataset extends the study of mesoscale cloud morphologies from daytime to nighttime and 1° × 1° resolution makes it better match with other climate datasets. It will provide a vital foundation for further research on the interactions between cloud morphology and climate processes. Our dataset is open-access and available at https://doi.org/10.5281/zenodo.13990646 (Wu et al., 2024).