The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds.

In this paper, a new simultaneous retrieval method of the SIRAW algorithm is introduced and carried out on FY3D/MERSI-II satellite images to obtain the aerosol optical thickness (AOT) and normalized water-leaving radiance (WLR) over the ocean. In order to improve the operation efficiency of SIRAW, a machine learning solver is developed to improve the speed of forward radiative transfer computation during retrieval. Ground-based measurement data from AERONET-OC and satellite products from VIIRS are used for comparative verification. The results show that the retrieved AOT and WLR from SIRAW are both in good agreement with those of AERONET-OC and VIIRS. Further, considering the degradation of the MERSI sensor, a new calibration scheme on 412 nm and 443 nm is adopted and an evaluation is carried out. Inter-comparison of derived WLR between MERSI and VIIRS indicates that the new calibration scheme could effectively improve the WLR retrieval accuracy of MERSI with better consistency to the official data of VIIRS. Therefore, this paper confirms that a simultaneous retrieval scheme combined with effective calibration coefficients can be used for high-precision retrieval of real aerosol and water-leaving radiation.

Highly sensitive to climate change and glacier melt, alpine wetlands on the Tibetan Plateau have important implications for carbon storage and animal habitat. However, long-term accurate mapping and changes of wetlands on the Tibetan Plateau remain knowledge gaps. Here, we qualify wetlands' spatial and temporal changes between 1990 and 2020 using Landsat imagery with an optimized random forest model. We find that the total area of marsh, floodplain, and swamp wetlands was approximately 8.25 × 10 ⁴ (2.68% of the plateau area), 6.99 × 10 ⁴ (2.27%), and 8.47 × 10 ⁴ km ² (2.75%) in 1990, 2000, and 2020, respectively. The total area of wetlands decreased by about 1.29 × 10 ⁴ km ² (−15.7%) between 1990 and 2000 but increased by about 1.52 × 10 ⁴ km ² (21.7%) between 2000 and 2020. Marsh and floodplain wetlands show a decrease followed by an increase, while swamps continue to increase. The high-density wetlands are mainly distributed in the Yangtze and Yellow River basins, especially with marsh and floodplain wetlands >40%, and Brahmaputra high-density swamps ∼40%. Conversion of wetland types occurred in a relatively high proportion of all periods, but there was a gradual downward trend. The proportion of conversions from wetlands to nonwetlands decreased and the proportion of conversions within wetlands increased. This study elucidates the distribution characteristics, area change, and type conversion of wetlands on the Tibetan Plateau, which provides scientific support for the development and use of alpine wetlands and governmental risk decision making.

Real-time monitoring of rainfall areas based on satellite remote sensing is of vital importance for extreme rainfall research and disaster prediction. In this study, a new rainfall area identification algorithm was developed for the new generation of geostationary satellites with high spatial and temporal resolution and rich bands. As the main drivers of the rainfall process, the macro and micro physical properties of clouds play an important role in the formation and development of rainfall. We considered differences in the absorption capacity of the water vapor absorption channels in the infrared band and introduced a sensitivity difference of rainfall area in water vapor channels to construct a sensitive detection of the water vapor region. The results of this algorithm were evaluated using Global Precipitation Measurement (GPM) satellite products and CloudSat measurements in various scenarios, with hit rates of 70.03% and 81.39% and false alarm rates of 2.05% and 21.34%, respectively. Spatiotemporal analysis revealed that the types of upper clouds in the rainfall areas mainly consisted of deep convection, cirrostratus, and nimbostratus clouds. Our study provides supporting data for weather research and disaster prediction, as well as an efficient and reliable method for capturing temporal and spatial features.

Hourly Himawari-8 Advanced Himawari Imager Level 3 Ocean Color (L3 OC) products have been recently released. However, a thorough evaluation and uncertainty analysis of L3 OC data spanning full disk as well as applicability to studies on photosynthetically active radiation (PAR) have not yet been conducted. This study evaluates the accuracy of L3 OC products, including normalized water-leaving radiance (Lwn) at 470, 510, and 640 nm, Chlorophyll-a concentration (Chlor-a), aerosol optical thickness (AOT) at 510 nm, and Ångström Exponent (AE), by comparing them to ground-based measurements obtained from AERONET-OC. Our results demonstrate a general agreement with the ground-based measurements, especially Lwn510. Chlor-a and AOT510 also demonstrate an overall consistency, whereas AE shows a larger discrepancy. Uncertainty analysis shows that Lwn remained accurate under different conditions, although increased uncertainties were observed in turbid water and periods of severe air pollution. Spatial analysis revealed that the distribution of L3 OC and Aqua-MODIS L2 OC products were strongly correlated. Lwn and Chlor-a in the Yellow and Bohai Seas exhibit seasonal variations, with both parameters decreasing in summer and increasing in winter. The impact of aerosols and Chlor-a on PAR calculations was investigated by developing a sophisticated algorithm for estimating PAR under clear-sky conditions using a coupled radiative transfer model. An analysis of the May 2021 dust event in the Southern Yellow Sea, which exhibited an AOT of 0.82, showed a notable increase in Chlor-a levels 1-2 days later, while the average daytime PAR forcing was -42.469 W/m ² under clear-sky conditions.

Observations on cloud geometric thickness are crucial for understanding the radiative balance and aerosol indirect radiative effects, and currently, cloud geometric thickness retrieval studies for passive instruments remain constrained due to the lack of the understanding of the incident radiation penetrability. In this work, we firstly analyze the relationship between the cloud droplets distribution and the incident radiation penetrability based on physical model, and then fully utilize the advantages of hyperspectral O ₄ measurements to build a physically based machine learning model to retrieve the cloud geometric thickness. The algorithm retrieves cloud geometric thickness from TROPOMI observations for the first time, and the retrievals are compared with the cloud geometric thickness from active observations. It is found that the mean absolute error of the retrievals using 2B‐CLDPROF‐LIDAR cloud‐top height as input is 0.49 km, which shows the potential of O ₄ band to retrieve cloud geometric thickness.