Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems

Field experiment in South China was undertaken to improve understanding of cloud and precipitation properties. Measurements of the vertical structures of non-precipitating and precipitating clouds were obtained using passive and active remote sensing equipment: a Ka-band cloud radar (CR) system, a C...

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Main Authors: Liping Liu, Zheng Ruan, Jiafeng Zheng, Wenhua Gao
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/9/12/1282
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spelling doaj-553ce98f1dc6488893c06ca5b22c85fe2020-11-25T02:42:40ZengMDPI AGRemote Sensing2072-42922017-12-01912128210.3390/rs9121282rs9121282Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer SystemsLiping Liu0Zheng Ruan1Jiafeng Zheng2Wenhua Gao3State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100086, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100086, ChinaPlateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100086, ChinaField experiment in South China was undertaken to improve understanding of cloud and precipitation properties. Measurements of the vertical structures of non-precipitating and precipitating clouds were obtained using passive and active remote sensing equipment: a Ka-band cloud radar (CR) system, a C-band frequency modulated continuous wave vertical pointing radar (CVPR), a microwave radiometer and a laser ceilometer (CEIL). CR plays a key role in high-level cloud observation, whereas CVPR is important for observing low- and mid-level clouds and heavy precipitation. CEIL helps us diminish the effects of “clear-sky” in the planetary boundary layer. The experiment took place in Longmen, Guangdong Province, China from May to September of 2016. This study focuses on evaluating the ability of the two radars to deliver consistent observation data and develops an algorithm to merge the CR, CVPR and CEIL data. Cloud echo base, thickness, frequency of observed cloud types and reflectivity vertical distributions are analyzed in the radar data. Comparisons between the collocated data sets show that reflectivity biases between the CR three operating modes are less than 2 dB. The averaged difference between CR and CVPR reflectivity can be reduced with attenuation correction to 3.57 dB from the original 4.82 dB. No systemic biases were observed between velocity data collected in the three CR modes and CVPR. The corrected CR reflectivity and velocity data were then merged with the CVPR data and CEIL data to fill in the gaps during the heavy precipitation periods and reduce the effects of Bragg scattering and fog on cloud observations in the boundary layer. Meanwhile, the merging of velocity data with different Nyquist velocities and resolutions diminishes velocity folding to provide fine-grain information about cloud and precipitation dynamics. The three daily periods in which low-level clouds tended to occur were at sunrise, noon and sunset and large differences in the average reflectivity values were observed. Mid- and high-level clouds tended to occur at 1400 and 1800 BT. Few clouds were found between a height of 3 and 5 km.https://www.mdpi.com/2072-4292/9/12/1282cloud radarC-band frequency modulated continuous wave radardata merging algorithm
collection DOAJ
language English
format Article
sources DOAJ
author Liping Liu
Zheng Ruan
Jiafeng Zheng
Wenhua Gao
spellingShingle Liping Liu
Zheng Ruan
Jiafeng Zheng
Wenhua Gao
Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems
Remote Sensing
cloud radar
C-band frequency modulated continuous wave radar
data merging algorithm
author_facet Liping Liu
Zheng Ruan
Jiafeng Zheng
Wenhua Gao
author_sort Liping Liu
title Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems
title_short Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems
title_full Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems
title_fullStr Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems
title_full_unstemmed Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems
title_sort comparing and merging observation data from ka-band cloud radar, c-band frequency-modulated continuous wave radar and ceilometer systems
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2017-12-01
description Field experiment in South China was undertaken to improve understanding of cloud and precipitation properties. Measurements of the vertical structures of non-precipitating and precipitating clouds were obtained using passive and active remote sensing equipment: a Ka-band cloud radar (CR) system, a C-band frequency modulated continuous wave vertical pointing radar (CVPR), a microwave radiometer and a laser ceilometer (CEIL). CR plays a key role in high-level cloud observation, whereas CVPR is important for observing low- and mid-level clouds and heavy precipitation. CEIL helps us diminish the effects of “clear-sky” in the planetary boundary layer. The experiment took place in Longmen, Guangdong Province, China from May to September of 2016. This study focuses on evaluating the ability of the two radars to deliver consistent observation data and develops an algorithm to merge the CR, CVPR and CEIL data. Cloud echo base, thickness, frequency of observed cloud types and reflectivity vertical distributions are analyzed in the radar data. Comparisons between the collocated data sets show that reflectivity biases between the CR three operating modes are less than 2 dB. The averaged difference between CR and CVPR reflectivity can be reduced with attenuation correction to 3.57 dB from the original 4.82 dB. No systemic biases were observed between velocity data collected in the three CR modes and CVPR. The corrected CR reflectivity and velocity data were then merged with the CVPR data and CEIL data to fill in the gaps during the heavy precipitation periods and reduce the effects of Bragg scattering and fog on cloud observations in the boundary layer. Meanwhile, the merging of velocity data with different Nyquist velocities and resolutions diminishes velocity folding to provide fine-grain information about cloud and precipitation dynamics. The three daily periods in which low-level clouds tended to occur were at sunrise, noon and sunset and large differences in the average reflectivity values were observed. Mid- and high-level clouds tended to occur at 1400 and 1800 BT. Few clouds were found between a height of 3 and 5 km.
topic cloud radar
C-band frequency modulated continuous wave radar
data merging algorithm
url https://www.mdpi.com/2072-4292/9/12/1282
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AT zhengruan comparingandmergingobservationdatafromkabandcloudradarcbandfrequencymodulatedcontinuouswaveradarandceilometersystems
AT jiafengzheng comparingandmergingobservationdatafromkabandcloudradarcbandfrequencymodulatedcontinuouswaveradarandceilometersystems
AT wenhuagao comparingandmergingobservationdatafromkabandcloudradarcbandfrequencymodulatedcontinuouswaveradarandceilometersystems
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