Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensiona...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Advances in atmospheric sciences Jg. 39; H. 12; S. 2203 - 2212
Hauptverfasser: Zuo, Yuanyuan, Hu, Zhiqun, Yuan, Shujie, Zheng, Jiafeng, Yin, Xiaoyan, Li, Boyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Heidelberg Science Press 01.12.2022
Springer Nature B.V
State Key Lab of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China
School of Atmospheric Sciences,Chengdu University of Information Technology,Chengdu 610225,China%School of Atmospheric Sciences,Chengdu University of Information Technology,Chengdu 610225,China
Schlagworte:
Aerosols
Algorithms
Atmospheric Sciences
Classification
Cloud classification
Cloud clusters
Cloud structure
Clouds
Clustering
Convective clouds
Density
Developmental stages
Earth and Environmental Science
Earth Sciences
Echoes
Geophysics/Geodesy
Identification
Meteorological radar
Meteorology
Modelling
Notes & Letters
precipitation
Radar
radiation
Reflectance
Residential density
Stratiform clouds
their interactions
Three dimensional models
Thresholds
Two dimensional models
Weather
Weather radar
improved DBSCAN clustering algorithm
天气雷达
三维模型
3D model
2D model
cloud identification and classification
weather radar
云识别与分类
二维模型
DBSCAN 聚类算法
ISSN:0256-1530, 1861-9533
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Correspondence-1
content type line 14
ISSN:0256-1530
1861-9533
DOI:10.1007/s00376-021-1223-7