As the main sensor of China’s Fengyun series meteorological satellites, spectral imagers are indispensable for observing the characteristics of atmosphere, surface and ocean, and play an important role in weather prediction and climate research due to their high sensitivity and high spatio-temporal resolution. Meanwhile, as a major participant in the energy budget and water cycle of the earth-air system, the cloud is closely related to radiation transmission, weather processes, and climate change. Since its retrieval results are significant for weather analysis, numerical prediction, and disaster warning, the cloud is a main spectral imager detection target. Meanwhile, Fengyun series meteorological satellites are in the rapid development stage. We show the main spectral sensors, the number of channels, and the spatial resolution (Table 1). The spectral response functions of AGRI, MERSI-II, and VIRR cloud sensitive channels in the wavelength range of 0.2‒1.8 μm and 2‒13 μm are presented (Figs. 1 and 2).

The main cloud characteristics include cloud detection, cloud thermodynamic phase, cloud top parameters (cloud top pressure, cloud top height, and cloud top temperature), cloud optical thickness, cloud effective particle radius, and cloud water path. The flow chart of the above cloud products generated by the satellite is given in Fig. 3. As the basis of cloud detection is that clouds have high reflectance and low brightness temperature in the visible and near infrared bands, classifying the radiation received by the passive sensor can help identify whether the pixels are cloudy or clear. Chinese scholars have proposed various threshold-based cloud detection algorithms for different sensors, geographical locations, and underlying surface types of Fengyun series satellites. With the improvement of cloud detection accuracy requirements, algorithms have gradually developed from fixed threshold to dynamic threshold, multi-feature combination threshold, and multi-spectral combination threshold. FY-4/AGRI employs observation of 0.65, 1.65, 3.78, 11.8, and 12 µm channels and various auxiliary data to obtain cloud detection products according to different spectral and spatial characteristics of clouds and clear sky conditions. Compared with MODIS cloud detection products, the accuracy of FY-4/AGRI operational cloud detection products is more than 88%. Cloud thermodynamic phase is generally divided into four categories, i.e., ice, liquid, mixed, and uncertain types. Cloud particles have different radiation characteristics for specific wavelength electromagnetic waves. The universal bispectral cloud thermodynamic phase retrieval algorithm is based on the brightness temperature of 11 μm channel and the brightness temperature difference between 8.5 and 11 μm channels. The FY-4/AGRI cloud thermodynamic phase retrieval algorithm constructs the cloud effective absorption optical thickness ratio (β ratio) based on the different radiation characteristics of water cloud and ice cloud at the infrared band. The β ratio of 8.5 and 11 μm channels is not affected by the observed radiation, cloud height, and cloud optical thickness, and the algorithm has the advantage of retrieving cloud thermodynamic phase. The cloud thermodynamic phase products of FY-4A/AGRI, FY-4B/AGRI, and Himawari-9/AHI at 05:00 UTC on January 1, 2024 are shown. The cloud thermodynamic phase of the three is generally consistent in spatial distribution, but there are some differences in cloud coverage (Fig. 5). The cloud top pressure, height, and temperature can be retrieved according to the different radiation characteristics of clouds with various heights at different channels. At present, the operational cloud top parameter retrieval algorithms mostly employ infrared split-window channels or CO₂ slicing channels. The FY-4/AGRI retrieval algorithm adopts two infrared window channels (10.8 and 12 µm) and one CO₂ absorption channel (13.3 µm). Additionally, the advantages of the infrared window channel sensitive to cloud microphysical characteristics and the CO₂ absorption channel sensitive to cloud height are combined. By conducting an iterative calculation of optimal estimation, the cloud top characteristics are obtained. Cloud top height and cloud top temperature products from FY-4A/AGRI, FY-4B/AGRI and Himawari-9/AHI are displayed, all of which show consistent spatial distribution characteristics, but the retrieval results of AGRI in some regions are invalid (Figs. 6 and 7). Given the current situation of Fengyun’s cloud optical thickness and effective particle radius retrieval, we conduct model development, database establishment, and system optimization for the cloud optical thickness and effective particle radius retrieval of FY-4/AGRI based on the classical double reflection channel algorithm. The algorithm employs a non-absorption channel sensitive to cloud optical thickness (0.87 μm) and an absorption channel sensitive to both cloud optical thickness and effective particle radius (2.25 μm). Meanwhile, it can simultaneously retrieve the daytime cloud optical thickness and effective particle radius. Rigorous forward radiative transfer in retrieval algorithms requires a large amount of calculation. To meet the requirements of satellite operational application, the algorithm pre-constructs a double-channel reflectivity lookup table to simplify the calculation of radiative transfer and adopts the optimal estimation method to realize the retrieval on the premise of ensuring accuracy. The retrieval results of AGRI and the operational Himawari-8/AHI cloud products are shown. Generally, the spatial distributions of the cloud optical thickness and effective particle radius are consistent. There are systematic differences in the cloud optical thickness and effective particle radius results between AGRI and AHI, and the possible reasons are the differences in calibration accuracy, observation geometry, and retrieval algorithms (Fig. 9). We are optimizing the algorithm for FY-4 and making targeted improvements to the MERSI on FY-3.

We review the recent progress in cloud detection, cloud thermodynamic phase, and cloud top parameter retrieval by passive spectral imagers of Fengyun satellite, and introduce the retrieval algorithms of cloud optical thickness and effective particle radius developed by our research group. In general, with the continuous improvement of the spatio-temporal resolution of spectral imagers and the calibration accuracy of Fengyun satellites, more advanced and reliable cloud characteristic retrieval algorithms are needed to meet the requirements of weather monitoring and climate change research.