Our study primarily addresses the feasibility of employing advanced machine learning and physics-based algorithms to capture the diurnal variations in cloud base height parameters using geostationary meteorological satellite remote sensing. The results indicated that the caution is warranted when utilizing cloud base property products trained on satellite and laser radar data for climate research. Fixed training samples might obscure the pronounced diurnal variations in cloud base heights.

Ground-based observations of aerosols and aerosol data verification is important for satellite and climate model modification. Here we present an evaluation of aerosol microphysical, optical and radiative properties measured using a multiwavelength photometer with a highly integrated design and smart control performance. The validation of this product is discussed in detail using AERONET as a reference. This work contributes to reducing AOD uncertainties in China and combating climate change.

Far-ultraviolet (FUV) airglow radiation is particularly well suited for space-based remote sensing. The Ionospheric Photometer (IPM) instrument carried aboard the Feng Yun 3-D satellite measures the spectral radiance of the Earth FUV airglow. IPM is a tiny, highly sensitive, and robust remote sensing instrument. Initial results demonstrate that the performance of IPM meets the designed requirement and therefore can be used to study the thermosphere and ionosphere in the future.

Global precipitable water vapor (PWV) derived from MERSI-II (Medium Resolution Spectral Imager) is compared with PWV from the Integrated Global Radiosonde Archive (IGRA). Our results show a good agreement between PWV from MERSI-II and IGRA and that MERSI-II PWV is slightly underestimated on the whole, especially in summer. The bias between MERSI-II and IGRA grows with a larger spatial distance between the footprint of the satellite and the IGRA station, as well as increasing PWV.