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Microphysical Characteristics of Melting Layers in North China Revealed by Aircraft and Radar.

Authors :
Hu, Xiangfeng
Hou, Shaoyu
Yang, Jiefang
Zhao, Shuwen
Zhang, Xiaorui
Tao, Yue
Li, Hongyu
Zhang, Xiaotuo
Huang, Hao
Source :
Remote Sensing. Jun2024, Vol. 16 Issue 12, p2120. 16p.
Publication Year :
2024

Abstract

The microphysical processes within the melting layer (ML) of stratiform clouds have been understudied, particularly regarding their intricate properties and behaviors. This study explores the ML's microphysical characteristics in three distinct stratiform cloud occurrences over North China from 2017 to 2019. Our findings reveal that the reflectivity factor, coupled with the volume-weighted diameter (Dm), escalates within the upper and middle sections of the ML across all cases, suggesting that aggregation, primarily in the top 40% of the ML, significantly enhances the bright band phenomenon. Notably, the 2019 case (Spiral3) displayed more vigorous aggregation activities compared to the 2017 event (Spiral1), possibly due to larger initial particle sizes, leading to a swift increase in both mean and maximum particle diameters. Conversely, in the lower 60% of the ML, ongoing melting reduces mean particle diameters and potentially decreases total number concentration (Nt) due to accelerated particle descent. However, the 2018 case (Spiral2) deviated by showing a rapid Nt increase in the lowest 20% of the ML, where breakup mechanisms counteracted melting effects. The MLs in Spiral1 and Spiral3, in which aggregates were mainly formed by plate-like ice crystals, were thicker than those in Spiral2, dominated by low-density aggregates formed by the combination of needle and columnar ice crystals. This analysis underscores how variations in particle characteristics, such as habit, density, and size, along with thermodynamic conditions, dictate the onset temperature for melting, ML thickness, and dominant microphysical processes, which differ markedly among the cases. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
12
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
178191735
Full Text :
https://doi.org/10.3390/rs16122120