Horizontal Convective Rolls (HCRs) are defined as horizontal vortices that rotate in the opposite direction within the convective boundary layer. They are one of the common forms of shallow convection in the atmosphere. HCRs can cause strong turbulence and water vapor mixing in the boundary layer as well as exchanges of mass, momentum, and heat flux between the boundary layer and the free atmosphere. Meteorologists have conducted systematic research on structural characteristics, formation mechanisms, and impacts on the boundary layer of HCRs through field observation experiments, theoretical derivation, flume experiments, and numerical simulations. The results indicate that inflection-point instability and thermal instability are the main mechanisms for the formation of HCRs. The organized turbulent transport associated with HCRs can cause non-uniform distribution of flux in the horizontal direction of the boundary layer. The vertical motion, high specific humidity, and positive temperature anomalies in the ascending branch of the HCRs provide favorable conditions for cold-flow snowstorm and deep convection. At present, large eddy simulation is the main numerical method for studying HCRs. However, the mechanisms by which HCRs trigger heavy snowfall and independently trigger deep convection are still unclear.It is recommended that greater use be made of new remote sensing data in the construction of HCRs' three-dimensional structural models and in the analysis of shallow convective cloud morphology. Cloud penetration experiments on cold-flow snow processes should be conducted to explore the effects of HCR-related aerosols and flux transport on ice microphysical processes. Based on a clear understanding of the structural characteristics of HCRs and the environmental conditions favorable for deep convection initiation, a nowcasting method for HCR-triggered deep convection should be developed using key precursor factors to improve the operational forecasting of HCR-induced hazardous weather.

Differential reflectivity (Z_DR) arc is known as an arc-shaped region of high differential reflectivity along the inflow edge of the forward flank, while a down shear elongated K_DP maximum near the echo centerline of the storm is known as K_DP foot. The Z_DR arc and the clear horizontal separation between the areas of Z_DR arc and K_DP foot have been confirmed to be the signatures of hydrometeor size sorting within their forward flank regions in supercell storms. Recent studies have indicated that Z_DR arc and Z_DR arc-K_DP foot separation signatures insupercell storms may be related to environmental storm-relative helicity and low-level shear. Based on the conception model and machine learning, the recognition algorithm for K_DP foot and Z_DR arc is designed, and the separation and angle of Z_DR arc and K_DP foot are then calculated. The recognition effect and quantitative calculation are examined using S band polarimetric radar and auto weather station observations of four supercell storms occurred in East China. The results show that the recognition method introduced in this study can identity Z_DR arc and K_DP foot correctly, the variation of Z_DR arc-K_DP foot centroid distance and separation angle can indicate the occurrence of extreme gust.