Abstract: Differential reflectivity ( Z_\mathrmD\mathrmR ) 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_\mathrmD\mathrmP maximum near the echo centerline of the storm is known as K_\mathrmD\mathrmP foot. The Z_\mathrmD\mathrmR arc and the clear horizontal separation between the areas of Z_\mathrmD\mathrmR arc and K_\mathrmD\mathrmP 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_\mathrmD\mathrmR arc and Z_\mathrmD\mathrmR arc- K_\mathrmD\mathrmP 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_\mathrmD\mathrmP foot and Z_\mathrmD\mathrmR arc is designed, and the separation and angle of Z_\mathrmD\mathrmR arc and K_\mathrmD\mathrmP 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_\mathrmD\mathrmR arc and K_\mathrmD\mathrmP foot correctly, the variation of Z_\mathrmD\mathrmR arc- K_\mathrmD\mathrmP foot centroid distance and separation angle can indicate the occurrence of extreme gust.