Development of a Novel One-Dimensional Nested U-Net Cloud-Classification Model (1D-CloudNet)

Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prior knowledge, leading to frequent misclassifications in regions with high latitudes or complex terrains. Machine learning approaches based on two-dimensional images face challenges such as data scarcity and high annotation costs, which hinder accurate pixel-level cloud identification. Additionally, single-pixel classification methods fail to effectively exploit the spatial correlations inherent in cloud structures. In this paper, we introduce the one-dimensional nested U-Net cloud-classification model (1D-CloudNet), which was developed using Himawari-8 and CloudSat data collected over two years (2016–2017), comprising a total of 27,688 samples. This model is explicitly tailored for the analysis of one-dimensional, multi-channel images. Experimental results indicate that 1D-CloudNet achieves an overall classification accuracy of 88.19% during the day and 87.40% at night. This represents a 3–4% improvement compared to traditional models. The model demonstrates robust performance for both daytime and nighttime applications, effectively addressing the absence of nighttime data in the Himawari-8 L2 product. In the future, 1D-CloudNet is expected to support regional climate research and extreme weather monitoring. Further optimization could enhance its adaptability to complex terrains.