Confidence and Error Analyses of the Radiosonde and Ka-Wavelength Cloud Radar for Detecting the Cloud Vertical Structure

Yun Yuan; Huige Di; Yuanyuan Liu; Danmin Cheng; Ning Chen; Qing Yan; Dengxin Hua

doi:10.3390/rs14184462

Remote Sensing (Sep 2022)

Confidence and Error Analyses of the Radiosonde and Ka-Wavelength Cloud Radar for Detecting the Cloud Vertical Structure

Yun Yuan,
Huige Di,
Yuanyuan Liu,
Danmin Cheng,
Ning Chen,
Qing Yan,
Dengxin Hua

Affiliations

Yun Yuan: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Huige Di: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Yuanyuan Liu: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Danmin Cheng: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Ning Chen: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Qing Yan: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Dengxin Hua: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China

DOI: https://doi.org/10.3390/rs14184462
Journal volume & issue: Vol. 14, no. 18
p. 4462

Abstract

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A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi’an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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