The Northeast China Cold Vortex (NCCV), especially the horizontally uniform cold environment near the NCCV core, is a favorable background for the initiation of mesoscale convective systems (MCSs), but prior research lacks analysis of the attributes of such MCSs. Based on Himawari-8 satellite infrared brightness temperature and NCCV positioning data, this study investigated and compared the characteristics of ordinary MCSs (Ord-MCSs) and MCSs accompanied by the NCCV (CV-MCSs) over Northeast China and its vicinity during April–September of 2018–2022. By using a bidirectional area-overlap algorithm, a total of 1707 Ord-MCSs and 229 CV-MCSs were identified, with the occurrence of both types of MCSs peaking from June to August. Key findings reveal that larger-scale MCSs exhibit extended lifecycle duration, enhanced convection and precipitation intensity, broader convection and precipitation areas, and a faster development rate. Moreover, compared with Ord-MCSs, CV-MCSs are active in environments with lower convective available potential energy (CAPE), lower 500-hPa temperature, and higher relative humidity throughout the mid-to-lower troposphere. Consequently, CV-MCSs have a smaller lifetime maximum convective core area but a larger lifetime maximum convective shield area and greater lifetime maximum precipitation coverage, together with notably longer life duration. Additionally, CV-MCSs show a stronger tendency to move westward, follow longer trajectories and attain higher average speeds than Ord-MCSs moving in the same direction. These results highlight that the cold, humid, and relatively stable environment near the NCCV core favors the development of extensive, long-lived, but not necessarily the most intensely convective MCSs, offering new insights for refining regional forecasts of convective system evolution under cold vortex conditions.

The Advanced Geosynchronous Radiation Imager (AGRI) onboard China’s Fengyun (FY)-4 satellites, which provides observational data across various wavelengths from visible to infrared (IR), holds great potential for diverse applications. However, the FY-4A AGRI mid-wave IR (MWIR) band (3.75 µm) is often contaminated by stray light in the midnight hours during the 1–2 months before and after the vernal or autumnal equinoxes. In this study, a U-Net-based deep learning model was employed to generate an expedient MWIR band from the FY-4A AGRI longwave IR band. Validation using normal radiance measurements revealed that MWIR brightness temperatures generated by the deep learning model are very close to those observed by the FY-4A AGRI, with mean absolute error of 1.48 K, root mean square error of 2.39 K, and a correlation coefficient of 0.99. When applying the model to periods of stray light contamination, the brightness temperature anomalies found in the FY-4A AGRI MWIR band are effectively eliminated. The findings of this study could support various scientific applications that necessitate use of the MWIR band during midnight hours, such as identification of fog/low stratus cloud.