This study applies a K-means cluster analysis to the DPR reflectivity profiles collected over the VN’s 118 ground radar domains between 2016-2020, from which twelve precipitation regimes sampled by the DPR were identified. These clusters are characterized by several modes of stratiform and convective precipitation, including isolated cells as well as different regions of mesoscale convective systems. We use hydrometeor identification and vertical motion retrieved from ten dual-Doppler radar domains in the VN to examine the characteristic shape of each cluster of DPR reflectivity profiles and gain further insight about the the DPR algorithm (2ADPR) profile classifications of “stratiform,” “convective,” and “other.” A quick check confirms that stratiform clusters are characterized by weak vertical motion and convective clusters by much stronger vertical motion, especially above the melting level. The deepest convective cluster consists of the strongest vertical motions (>12 m/s) and greatest fraction of heavily rimed ice (>30%) in the precipitation column. However, this most kinematically intense cluster does not include profiles with the greatest reflectivity below the melting layer. Instead, its profiles have much larger volumes of graupel and hail distributed across a 5 km layer above the melting level with lesser amounts of rain below it. This result implies that the convection with the most kinetic energy may not produce the most intense precipitation at the Earth’s surface, which has implications for depicting convective precipitation in large scale models and precipitation conversion rates used in cloud models. Additionally, the dataset assembled in this study is ripe for machine learning applications to extract additional information from DPR observations and future missions that use satellite radars to study convection.

