A strong convective storm is a disastrous weather system with a small spatio-temporal scale. It often occurs suddenly and can cause huge disasters. Thus, it is necessary to improve the forecast accuracy of strong convective storms. Overshooting cloud top (OT) is the product of strong updrafts in convective storms, which can penetrate the tropopause and enter the lower stratosphere. OT is closely related to severe weather and can influence water vapor transport and the material exchange between the troposphere and stratosphere. Therefore, the timely detection of OT can help improve the accuracy of forecasting. In this study, we develop a new objective OT detection algorithm based on geostationary satellite observations from 2006 to 2017. The accuracy of the new algorithm in identifying OT is verified by manually comparing it with the radar echo images and the cloud images of MODIS 250 m. Then, the OT is statistically analyzed in a long time series. It is found that OT events are mainly concentrated in equatorial and low latitude regions, with higher frequency in summer. There are obvious differences between OT events on land and sea. Additionally, this dataset also reveals the close connection between the seasonal shift of OT and the seasonal average precipitation distribution around the globe. This study provides a scientific basis for determining the geographical characteristics of OT frequency and explores the application of this OT objective detection algorithm in the operational forecast of strong convective weather. We hope this study can benefit OT monitoring in operational weather forecasting.

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The new-generation FengYun geostationary meteorological satellite has a high spatial and temporal resolution, which is advantageous in environmental assessments and air pollution monitoring. This study researched the ground-level particulate matter concentration estimation, based on satellite-observed radiations. The radiation of ground-level particulate matter is separate from the apparent radiation observed by satellites. The positive correlation between PM2.5 and PM10 is also considered to improve the accuracy of inversion results and the interpretability of the estimation model. Then, PM2.5 and PM10 concentrations were estimated synchronously every 5 min in mainland China based on FY-4A satellite directly observed radiations. The validation results showed that the improved model estimated results were close to the ground site measured results, with a high determination coefficient (R2) (0.89 for PM2.5, and 0.90 for PM10), and a small Root Mean Squared Error (RMSE) (4.69 μg/m3 for PM2.5 concentrations, and 13.77 μg/m3 for PM10 concentrations). The estimation model presented a good performance in PM2.5 and PM10 concentrations during typical haze and dust storm cases, indicating that it is applicable in different weather conditions and regions.

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.