A knowledge-based data-driven (KBDD) framework for all-day
  identification of cloud types using satellite remote sensing
Longfeng Nie Yuntian Chen Mengge Du Changqi Sun Dongxiao Zhang
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.