This study focuses on analyzing the changes to aerosol properties caused by the dust storm called “China’s Great Wall of Dust” that originated from the Taklimakan Desert in April 2014. IDDI (Infrared Difference Dust Index) images from FY-2E and true color composite images from FY-3C MERSI (Medium Resolution Spectral Imager) show the breakout and transport path of the dust storm. Three-hourly ground-based measurements from MICAPS (Meteorological Information Comprehensive Analysis and Process System) suggest that anticyclonic circulation occupying the Southern Xinjiang basin and cyclonic circulation in Mongolia form a dipole pressure system that leads to strong northwesterly winds (13.7–20 m/s), which favored the breakout of the dust storm. IDDI results indicate that the dust storm breakout occurred at ~2:00 UTC on 23 April in the Taklimakan Desert. Four-day forward air mass trajectories with the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model gives the simulation results of the dust transport paths and dust vertical distributions, which are consistent with the corresponding aerosol vertical distributions derived from CALIPSO. The Aerosol Index (AI) data of TOU (Total Ozone Unit) aboard FY-3B are first used to study the areas affected by the dust storm. From the AI results, the dust-affected areas agree well with the synoptic meteorological condition analysis, which supports that the synoptic meteorological conditions are the main reason for the breakout and transport of the dust storm. Anomalies of the average MODIS (Moderate Resolution Imaging Spectroradiometer) AOD (Aerosol Optical Depth) distributions over northeast Asia during the dust storm to the average of the values in April between 2010 and 2014 are calculated as a percent. The results indicate high aerosol loading with a spatially-averaged anomaly of 121% for dusty days between 23 April and 25 April. Aerosol Robotic Network (AERONET) retrievals of VSD (Volume Size Distribution) and SSA (Single Scattering Albedo) show that while the aerosol properties in Dalanzadgad, which is closer to the dust source, were influenced primarily by coarse dust particles, the aerosol properties in Beijing were mostly contributed by fine dust particles that transported over longer distances and at high atmospheric levels.

The Tibetan Plateau vortex (TPV), as an α-scale mesoscale weather system, often brings severe weather conditions like torrential rain and severe convective storms. Based on the detections from the Global Precipitation Measurement (GPM) Core Observatory’s Dual-frequency Precipitation Radar (DPR) and the FY-4A satellite’s Advanced Geostationary Radiation Imager (AGRI), combined with ERA5 reanalysis data, the precipitation characteristics of a TPV moving eastward during 8–13 July 2021 at different developmental stages are explored in this study. It was clear that the near-surface precipitation rate of the TPV during the initial stage at the eastern Tibetan Plateau (TP) was below 1 mm·h⁻¹, implying overall weak precipitation dominated by stratiform clouds. After moving out of the TP, the radar reflectivity factor (Ze), precipitation rate, and normalized intercept parameter (dBNw) significantly increased, while the proportion of convective clouds gradually rose. Following the TPV movement, the distribution range and vertical thickness of Ze, mass-weighted mean diameter (Dm), and dBNw tended to increase. The high-frequency region of Ze appeared at 15–20 dBZ, while Dm and dBNw occurred at around 1 mm and 33 mm⁻¹·m⁻³, respectively. Near the melting layer, Ze was characterized by a significant increase due to the aggregation and melting of ice crystals. The precipitation rate of convective clouds was generally greater than that of stratiform clouds, whilst both of them increased during the movement of the TPV. Particularly, at 01:00 on 12 July, there was a significant increase in the precipitation rate and Dm of convective clouds, while dBNw noticeably decreased. These findings could provide valuable insights into the three-dimensional structure and microphysical characteristics of the precipitation during the movement of the TPV, contributing to a better understanding of cloud precipitation mechanisms.

This paper proposes a novel precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI). This method facilitates the spatiotemporal matching of 125 features derived from the multi-temporal and multi-channel data of the FY-4B satellite with precipitation data at stations. Subsequently, a precipitation model was constructed using the light gradient boosting machine (LGBM) algorithm. A comparative analysis of FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 revealed the following: (1) The results demonstrate that FY-4B_AI is more accurate than GPM/IMERG-L. Six of the eight evaluation indices exhibit superior performance for FY-4B_AI in comparison to GPM/IMERG-L. These indices include the mean absolute error (MAE), root mean square error (RMSE), relative error (RE), correlation coefficient (CC), probability of detection (POD), and critical success index (CSI). As for the MAE, the results are 1.67 (FY-4B_AI) and 1.92 (GPM/IMERG-L), respectively. The RMSEs are 3.68 and 4.07, respectively. The REs are 17.72% and 26.28%, respectively. The CCs are 0.44 and 0.36, respectively. The PODs are 61.84% and 47.31%, respectively. The CSIs are 0.30 and 0.27, respectively. However, with regard to the mean errors (MEs) and false alarm rates (FARs), FY-4B_AI (−0.88 and 62.85%, respectively) displays a slight degree of inferiority in comparison to GPM/IMERG-L (−0.80 and 62.21%, respectively). (2) An evaluation of two strong weather events to represent the spatial distribution of precipitation in different climatic zones revealed that both FY-4B_AI and GPM/IMERG-L are equally capable of accurately representing these phenomena, irrespective of whether the region in question is humid, as is the case in the southeast, or dry, as is the case in the northwest.

A smart precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI) is proposed in this paper. This method spatiotemporally matches 125 features derived from multi-temporal and multi-channels of FY-4B satellite data with the precipitation at stations. Then a precipitation model using the Light Gradient Boosting Machine algorithm is constructed. Comparative results between FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 shows that: 1) FY-4B_AI is superior to GPM IMERG-L in the average absolute error, root mean square error, relative error, correlation coefficient, probability of detection and critical success index. While in the mean error and false alarm rate, FY-4B_AI is slightly inferior to GPM/IMERG-L. 2) Evaluation of strong weather event applications reveals that both FY-4B_AI and GPM/IMERG-L can accurately represent the spatial distribution characteristics of precipitation, no matter in the southeast humid region or the northwest dry region. Notably, FY-4B_AI, due to its higher spatiotemporal resolution, provides a more detailed distribution of precipitation.

In mid-December 2023, East Asia experienced an extreme sea-effect snowstorm, with snowfall intensity and snow depth breaking historical records. Based on FengYun (FY) meteorological satellite datasets, the atmospheric circulation, the evolution of the sea-effect clouds, and the marine and atmospheric environments are examined. The results show that the temperature anomalies at 850 hPa from the polar regions to East Asia and the Northwest Pacific exhibited a “positive-negative-positive” pattern in early December, which is in favor of the polar vortex moving away from the polar region to lower latitudes causing cold waves. Caused by the northerly winds over the ocean the sea-effect snowstorm cloud systems exhibit as a wide range of cellular cumulus moving towards the land. The weakening wind speed is beneficial for the maintaining of the sea-effect snowstorm. The average cloud top temperature (CTT) of sea-effect snowstorm clouds over the Shandong Peninsula is from −18 to −10°C, and the cloud top height (CTH) is about 1.5 km, showing significant differences from that of the cold front snowstorm clouds. The precipitation rate from FY-3G precipitation measurement radar (PMR) shows that the precipitation top of the cellular cumulus in the Yellow Sea and the East China Sea is 2.0-2.5 km, and the maximum precipitation rate is 0.5-1.0 mm h⁻¹. The high sea surface temperature (SST) provides warm and humid conditions for the cellular cumulus. The colder air moving into warmer and more humid sea surface can result in higher development of cellular cumulus and increasing precipitation rate. The overall performance is characterized by the higher SST, higher atmospheric humidity layers, higher temperature inversion layers, lower CTT, higher CTH, and greater precipitation intensity on the western coast of Honshu Island in Japan compared to the Shandong Peninsula. Additionally, the topography has impact on the distribution and intensity of sea-effect snowstorm.

Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high (SAH) anticyclone and their application in the real-time monitoring of rainstorm disasters are studied. The results show that the AMVs’ vertical distribution characteristics are different across regions and seasons. AMVs from 150 to 350 hPa can be chosen as the upper troposphere wind (the total number accounts for about 77.2% on average). The center and shape of the upper tropospheric anticyclone obtained from AMVs are close to or slightly southward compared with those of the SAH at 200 hPa obtained from the ERA5 geopotential height. The SAH ridge line identified using the upper troposphere AMV zonal wind (the zonal wind is equal to zero) is slightly southward by about 1–2 degrees of latitude from that identified using ERA5 at 200 hPa but with a similar seasonal advance. The upper troposphere AMV can be used to monitor the location of the SAH and the evolution of its ridge line. The abnormally strong precipitation in South China is related to the location of the SAH and its ridge line. When the precipitation is abnormally strong/weak, the upper troposphere AMV deviation airflow shows divergence/convergence. During the “Dragon Boat Water” period in South China in 2022, strong precipitation occurred in the strong westerly winds or divergent flow on the northeast side of the upper troposphere anticyclone obtained from AMVs, and the precipitation intensity was the strongest when the divergence reached its peak, but this is not shown clearly in the EAR5 dataset.