Identification of Convective Precipitation Feature Observed by TRMM/GPM PR Using a Revised Unsupervised Clustering Proposal

The axis ratio of fitted ellipse plays a key role for classifying the linear convective precipitation features (convPFs) from others. One of the limitations in identifying convPFs is the discreteness of convective pixels, which could divide a linear convPFs into multiple segments. It would underestimate the occurrence of severe weather system to some extent. A revised unsupervised clustering method based on the density-based algorithm (DBSCAN) is proposed to improve the identification of linear convPFs. Preliminary study shows, the new method is capable of recognizing the linear convPFs with discrete convective pixels correctly.