Cloud types, as a type of meteorological data, are of particular significance for evaluating changes in rainfall, heatwaves, water resources, floods and droughts, food security and vegetation cover, as well as land use. In order to effectively utilize high-resolution geostationary observations, a knowledge-based data-driven (KBDD) framework for all-day identification of cloud types based on spectral information from Himawari-8/9 satellite sensors is designed. And a novel, simple and efficient network, named CldNet, is proposed. Compared with widely used semantic segmentation networks, including SegNet, PSPNet, DeepLabV3+, UNet, and ResUnet, our proposed model CldNet with an accuracy of 80.89+-2.18% is state-of-the-art in identifying cloud types and has increased by 32%, 46%, 22%, 2%, and 39%, respectively. With the assistance of auxiliary information (e.g., satellite zenith/azimuth angle, solar zenith/azimuth angle), the accuracy of CldNet-W using visible and near-infrared bands and CldNet-O not using visible and near-infrared bands on the test dataset is 82.23+-2.14% and 73.21+-2.02%, respectively. Meanwhile, the total parameters of CldNet are only 0.46M, making it easy for edge deployment. More importantly, the trained CldNet without any fine-tuning can predict cloud types with higher spatial resolution using satellite spectral data with spatial resolution 0.02{\deg}*0.02{\deg}, which indicates that CldNet possesses a strong generalization ability. In aggregate, the KBDD framework using CldNet is a highly effective cloud-type identification system capable of providing a high-fidelity, all-day, spatiotemporal cloud-type database for many climate assessment fields.

翻译：云类型作为气象数据的一类，对评估降雨、热浪、水资源、洪涝与干旱、粮食安全、植被覆盖以及土地利用变化具有重要意义。为有效利用高分辨率静止轨道卫星观测数据，本文设计了一种基于 Himawari-8/9 卫星传感器光谱信息的、融合知识驱动与数据驱动（KBDD）的云类型全天候识别框架，并提出一种新颖、简洁且高效的网络 CldNet。与广泛使用的语义分割网络（包括 SegNet、PSPNet、DeepLabV3+、UNet 和 ResUnet）相比，所提出的 CldNet 模型在云类型识别任务上准确率达 80.89±2.18%，分别提升 32%、46%、22%、2% 和 39%，达到当前最优水平。在辅助信息（如卫星天顶角/方位角、太阳天顶角/方位角）支持下，采用可见光与近红外波段的 CldNet-W 以及未采用这些波段的 CldNet-O 在测试集上的准确率分别为 82.23±2.14% 和 73.21±2.02%。同时，CldNet 总参数量仅为 0.46M，易于实现边缘部署。更重要的是，训练后的 CldNet 无需微调，即可利用空间分辨率为 0.02°×0.02° 的卫星光谱数据预测更高空间分辨率的云类型，表明其具备强大的泛化能力。综上，基于 CldNet 的 KBDD 框架是一种高效的云类型识别系统，能够为众多气候评估领域提供高保真、全天候、时空连续的云类型数据库。