The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection
Feature detection is one of the hot topics in the weather radar research community. This study employed a convective–stratiform classification algorithm to detect features in polarimetric radar variables and Quantitative Precipitation Estimation (QPE) retrieval during a heavy precipitation event in...
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1176 |
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| author | Ndabagenga Daudi Mikidadi Xingyou Huang Lingbing Bu |
| author_facet | Ndabagenga Daudi Mikidadi Xingyou Huang Lingbing Bu |
| author_sort | Ndabagenga Daudi Mikidadi |
| collection | DOAJ |
| description | Feature detection is one of the hot topics in the weather radar research community. This study employed a convective–stratiform classification algorithm to detect features in polarimetric radar variables and Quantitative Precipitation Estimation (QPE) retrieval during a heavy precipitation event in Crossville, Tennessee, during warm-season convection. Analysis of polarimetric radar variables revealed that strong updrafts, mixed-phase precipitation, and large hailstones in the radar resolution volume during the event were driven by the existence of supercell thunderstorms. The results of feature detection highlight that the regions with convective–stratiform cores and strong–faint features in the reflectivity field are similar to those in the rainfall field, demonstrating how the algorithm more effectively detects features in both fields. The results of the estimates, accounting for uncertainty during feature detection, indicate that an offset of +2 dB overestimated convective features in the northeast in both the reflectivity and rainfall fields, while an offset of −2 dB underestimated convective features in the northwest part of both fields. The results highlight that convective cores cover a small area with high rainfall exceeding 50 mmh<sup>−1</sup>, while stratiform cores cover a larger area with greater horizontal homogeneity and lower rainfall intensity. These findings are significant for nowcasting weather, numerical models, hydrological applications, and enhancing climatological computations. |
| format | Article |
| id | doaj-art-c9d344a891f6409f9a1acceeeb66f164 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c9d344a891f6409f9a1acceeeb66f1642025-08-20T03:03:21ZengMDPI AGRemote Sensing2072-42922025-03-01177117610.3390/rs17071176The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season ConvectionNdabagenga Daudi Mikidadi0Xingyou Huang1Lingbing Bu2School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaFeature detection is one of the hot topics in the weather radar research community. This study employed a convective–stratiform classification algorithm to detect features in polarimetric radar variables and Quantitative Precipitation Estimation (QPE) retrieval during a heavy precipitation event in Crossville, Tennessee, during warm-season convection. Analysis of polarimetric radar variables revealed that strong updrafts, mixed-phase precipitation, and large hailstones in the radar resolution volume during the event were driven by the existence of supercell thunderstorms. The results of feature detection highlight that the regions with convective–stratiform cores and strong–faint features in the reflectivity field are similar to those in the rainfall field, demonstrating how the algorithm more effectively detects features in both fields. The results of the estimates, accounting for uncertainty during feature detection, indicate that an offset of +2 dB overestimated convective features in the northeast in both the reflectivity and rainfall fields, while an offset of −2 dB underestimated convective features in the northwest part of both fields. The results highlight that convective cores cover a small area with high rainfall exceeding 50 mmh<sup>−1</sup>, while stratiform cores cover a larger area with greater horizontal homogeneity and lower rainfall intensity. These findings are significant for nowcasting weather, numerical models, hydrological applications, and enhancing climatological computations.https://www.mdpi.com/2072-4292/17/7/1176weather radarfeature detectionpolarimetric variablesQPEalgorithm |
| spellingShingle | Ndabagenga Daudi Mikidadi Xingyou Huang Lingbing Bu The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection Remote Sensing weather radar feature detection polarimetric variables QPE algorithm |
| title | The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection |
| title_full | The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection |
| title_fullStr | The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection |
| title_full_unstemmed | The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection |
| title_short | The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection |
| title_sort | application of the convective stratiform classification algorithm for feature detection in polarimetric radar variables and qpe retrieval during warm season convection |
| topic | weather radar feature detection polarimetric variables QPE algorithm |
| url | https://www.mdpi.com/2072-4292/17/7/1176 |
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