  Satellite remote sensing serves as a crucial means to acquire cloud physical
parameters. However, existing official cloud products derived from the advanced
geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary
satellite suffer from limitations in computational precision and efficiency. In
this study, an image-based transfer learning model (ITLM) was developed to
realize all-day and high-precision retrieval of cloud physical parameters using
AGRI thermal infrared measurements and auxiliary data. Combining the
observation advantages of geostationary and polar-orbiting satellites, ITLM was
pre-trained and transfer-trained with official cloud products from advanced
Himawari imager (AHI) and Moderate Resolution Imaging Spectroradiometer
(MODIS), respectively. Taking official MODIS products as the benchmarks, ITLM
achieved an overall accuracy of 79.93% for identifying cloud phase and root
mean squared errors of 1.85 km, 6.72 um, and 12.79 for estimating cloud top
height, cloud effective radius, and cloud optical thickness, outperforming the
precision of official AGRI and AHI products. Compared to the pixel-based random
forest model, ITLM utilized the spatial information of clouds to significantly
improve the retrieval performance and achieve more than a 6-fold increase in
speed for a single full-disk retrieval. Moreover, the AGRI ITLM products with
spatiotemporal continuity and high precision were used to accurately describe
the spatial distribution characteristics of cloud fractions and cloud
properties over the Tibetan Plateau (TP) during both daytime and nighttime, and
for the first time provide insights into the diurnal variation of cloud cover
and cloud properties for total clouds and deep convective clouds across
different seasons.
