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Remote Sens. 2017, 9(12), 1282; https://doi.org/10.3390/rs9121282

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

1
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100086, China
2
Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
*
Author to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 4 December 2017 / Accepted: 7 December 2017 / Published: 10 December 2017
(This article belongs to the Section Atmosphere Remote Sensing)
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Abstract

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. View Full-Text
Keywords: cloud radar; C-band frequency modulated continuous wave radar; data merging algorithm cloud radar; C-band frequency modulated continuous wave radar; data merging algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Liu, L.; Ruan, Z.; Zheng, J.; Gao, W. Comparing and Merging Observation Data from Ka-Band Cloud Radar, C-Band Frequency-Modulated Continuous Wave Radar and Ceilometer Systems. Remote Sens. 2017, 9, 1282.

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