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The lightning clustering algorithm for spaceborne lightning observations clusters events into flash products and plays a crucial role in lightning-related research. This paper modified the lightning clustering algorithm based on the routine algorithm of Lightning Mapping Imager (LMI) and applied it to lightning events recorded by the LMI in 2019 to study the flash duration and flash area and specially to reveal the extreme lightning. To reveal the temporal and spatial features of lightning, the MCA (modified cluster algorithm) flash products defined flash duration and flash area. The results showed that continental regions have higher lightning frequencies but smaller events per flash, flash durations and flash areas than the ocean. The Tibetan Plateau has a low lightning frequency and smaller flash duration and area than eastern China. The flash count, events per flash, flash duration and flash area are higher during nighttime than during daytime. The relationship between flash duration and flash area showed distinct regional differences. In the MCA flash product, about 0.6% (0.1%) flashes have duration (area) that exceeds 1.0 s (5,000 km2). Finally, we analyzed two extreme lightning cases in eastern China, where the longest flash duration was 4.748 s and the largest flash area was 17,100 km2. This research contributes to advancing our understanding in the features of space-based lightning in the Eastern Hemisphere and applications to convection study. • Clustering algorithm was modified to study lightning with extreme duration and area. • Lightning duration and area both implied regional differences. • Quantitative relationship of duration and area was established in different regions. • The longest flash duration/largest flash area was 4.75 s/17100 km2 in eastern China. [ABSTRACT FROM AUTHOR]

The Chinese meteorological satellites Fengyun (FY) have recently developed rapidly, resulting in a substantial wealth of precipitable water vapor (PWV) products. However, these latest operational PWV products still need to be systematically validated. In this paper, we focus on the PWV products derived from the FY-3D/Medium Resolution Spectral Imager II (MERSI-II), FY-2H/Visible and Infrared Spin Scan Radiometer (VISSR), and FY-4A/Advanced Geostationary Radiation Imager (AGRI), and test their performance by using the global navigation satellite system (GNSS) data. We find that FY-4A/AGRI performs best in various regions of China in 2021. Compared to GNSS-PWV, FY-4A/AGRI PWV has a correlation coefficient 0.95, a root mean square error (RMSE) of 4.67 mm, and a mean deviation (MB) of −0.73 mm, respectively. Significant discrepancies between FY-PWV and GNSS-PWV are observed in the Qinghai-Tibetan Plateau (MB: −4.86 mm for FY-2H/VISSR and − 5.12 mm for FY-4A/AGRI) and the Sichuan Basin (MB: 5.52 mm for FY-2H/VISSR, and 3.82 mm FY-4A/AGRI). All FY-PWVs exhibit season-dependent variations, and their discrepancies with GNSS-PWV during winter are smaller than in summer. Finally, we analyze the distribution and changes of water vapor in China from 2019 to 2022 using the preferred FY-4A/AGRI PWV. The results reveal a gradual decrease in the annual mean water vapor content, diminishing by approximately 0.33 mm during this period. Notably, this decrease occurs in southern China, and the water vapor content spikes in summer, culminating at 23.59 mm in 2022. Our findings strongly correlate with the precipitation changes documented in the China Climate Bulletin (2022). • This study is an application of 3S (remote sensing, GNSS, and GIS) technology in meteorology. • A harmonized standard method has been used for the evaluation of FY-PWVs. • FY-4A/AGRI PWV exhibits the best consistency with GNSS PWV in mainland China. • The distribution and changes of water vapor in China in recent years have been investigated. [ABSTRACT FROM AUTHOR]

Aerosol types have crucial and distinguished impacts on climate effects and cause the difference of air pollution. However, direct retrieval of aerosol type by satellite remote sensing is difficult, and the satellite products of aerosol type are seriously lacking. At present, the identification of aerosol type is mainly based on ground-based observation data, but the coverage area of ground-based stations is limited, and large-scale spatial continuous monitoring cannot be carried out. The existing aerosol type identification methods based on satellite data need to combine multiple satellites data (e.g. Moderate-resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), etc.) which leads to a low temporal resolution. The research about aerosol type identification only based on geostationary satellite data is lacking. Therefore, aerosol type identification based on the multi-parameter threshold method and the neural network method are proposed and compared in this paper. The neural network jointly using spectral and temporal information was trained and tested by the measurements from 26 sites of Aerosol Robotic Network (AERONET) and Sun-Sky Radiometer Observation Network (SONET). For the overall validation, the aerosol type identification based on the neural network is better than that based on the multi-parameter threshold method, and the accuracy reaches 71.44%. Atmospheric environment monitoring and quantitative remote sensing can be supported by this study. • Aerosol type, neural network, geostationary satellite [ABSTRACT FROM AUTHOR]

The distribution of ice particles strongly affects the microphysical processes in mixed-phase clouds, but the inhomogeneity of ice distribution is not well understood. In this paper, based on airborne in-situ measurements from three flights on May 31, 2021 in Hulun Buir, China, the inhomogeneity and clustering of ice distribution in a stratiform cloud is quantitatively analyzed using the pair correlation function (PCF). The results show that ice clusters on scales of a few kilometers dominate the inhomogeneity of the ice distribution. Due to the cumulative impact of ice clusters on different scales, the probability of finding relative high ice concentration within a lag of 80 m can be enhanced by 0.1 to 3.5 times. On average, the scale of ice cluster is ∼100 m for a sampling distance of 1 km, and increases to 3.2 km for a sampling distance of 20 km. It is also found that the ice growth is not fast enough to cluster the ice water content (IWC), and the inhomogeneity of IWC is strongly influenced by ice generation in addition to ice growth in the observed cloud. The results are helpful to better understand the distribution of ice in mixed-phase cloud, and provide potentially important information to improve the parameterizations of microphysics in models. • The inhomogeneity of ice distribution in mixed-phase stratiform cloud is quantitatively analyzed using airborne measurement. • Ice clusters on scales of a few kilometers dominate the inhomogeneity of the ice distribution. • Averagely, the ice cluster scale increases from 100 m to 3.2 km as the sampling distance increases from 1 km to 20 km. [ABSTRACT FROM AUTHOR]