Analysis of Clean-Air Echoes Based on Millimeter Wave Cloud Radar Measurements

Author(s):  
Qian Yang ◽  
Ling Yang ◽  
Zixin Xu ◽  
Qiyuan Yin
2020 ◽  
Vol 37 (8) ◽  
pp. 912-924
Author(s):  
Ling Yang ◽  
Yun Wang ◽  
Zhongke Wang ◽  
Qian Yang ◽  
Xingang Fan ◽  
...  

Author(s):  
Lihua Li ◽  
Stephen M. Sekelsky ◽  
Steven C. Reising ◽  
Calvin T. Swift ◽  
Stephen L. Durden ◽  
...  

2021 ◽  
Author(s):  
Oliver Branch ◽  
Andreas Behrendt ◽  
Osama Alnayef ◽  
Florian Späth ◽  
Thomas Schwitalla ◽  
...  

<p>We present exciting Doppler lidar and cloud radar measurements from a high-vantage mountain observatory in the hyper-arid United Arab Emirates (UAE) - initiated as part of the UAE Research Program for Rain Enhancement Science (UAEREP). The observatory was designed to study the clear-air pre-convective environment and subsequent convective events in the arid Al Hajar Mountains, with the overarching goal of improving understanding and nowcasting of seedable orographic clouds. During summer in the Al Hajar Mountains (June to September), weather processes are often complex, with summer convection being initiated by several phenomena acting in concert, e.g., interaction between sea breeze and horizontal convective rolls. These interactions can combine to initiate sporadic convective storms and these can be intense enough to cause flash floods and erosion. Such events here are influenced by mesoscale phenomena like the low-level jet and local sea breeze, and are constrained by larger-scale synoptic conditions.</p><p>The Doppler lidar and cloud radar were employed for approximately two years at a high vantage-point to capture valley wind flows and observe convective cells. The instruments were configured to run synchronized polar (PPI) scans at 0°, 5°, and 45° elevation angles and vertical cross-section (RHI) scans at 0°, 30°, 60, 90°, 120°, and 150° azimuth angles. Using this imagery, along with local C-band radar and satellite data, we were able to identify and analyze several convective cases. To illustrate our results, we have selected two cases under unstable conditions - the 5 and 6 September 2018. In both cases, we observed areas of low-level convergence/divergence, particularly associated with wind flow around a peak 2 km to the south-west of the observatory. The extension of these deformations are visible in the atmosphere to a height of 3 km above sea level. Subsequently, we observed convective cells developing at those approximate locations – apparently initiated because of these phenomena. The cloud radar images provided detailed observations of cloud structure, evolution, and precipitation. In both convective cases, pre-convective signatures were apparent before CI, in the form of convergence, wind shear structures, and updrafts.</p><p>These results have demonstrated the value of synergetic observations for understanding orographic convection initiation, improvement of forecast models, and cloud seeding guidance. The manuscript based on these results is now the subject of a peer review (Branch et al., 2021).</p><p> </p><p>Branch, O., Behrendt, Andreas Alnayef, O., Späth, F., Schwitalla, Thomas, Temimi, M., Weston, M., Farrah, S., Al Yazeedi, O., Tampi, S., Waal, K. de and Wulfmeyer, V.: The new Mountain Observatory of the Project “Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)” in the United Arab Emirates: First results on Convection Initiation, J. Geophys. Res.  Atmos., 2021. In review (submitted 23.11.2020).</p>


2016 ◽  
Vol 148 ◽  
pp. 64-73 ◽  
Author(s):  
Lingbing Bu ◽  
Honglin Pan ◽  
K. Raghavendra Kumar ◽  
Xingyou Huang ◽  
Haiyang Gao ◽  
...  
Keyword(s):  

2014 ◽  
Vol 26 (10) ◽  
pp. 109003
Author(s):  
卜令兵 Bu Lingbing ◽  
覃艳秋 Qin Yanqiu ◽  
吴放 Wu Fang ◽  
刘新波 Liu Xinbo ◽  
黄兴友 Huang Xingyou

2016 ◽  
Author(s):  
Tao Wen ◽  
Zeng-Liang Zhao ◽  
Zhi-Gang Yao ◽  
Zhi-Gang Han ◽  
Lin-Da Guo

2017 ◽  
Author(s):  
Madhu Chandra R. Kalapureddy ◽  
Sukanya Patra ◽  
Subrata K. Das ◽  
Sachin M. Deshpande ◽  
Kaustav Chakravarty ◽  
...  

Abstract. One of the key parameters that must be included in the analysis of atmospheric constituents (gases and particles) and clouds is the vertical structure of the atmosphere. Therefore high-resolution vertical profile observations of the atmospheric targets are required for both theoretical and practical evaluation and as inputs to increase accuracy of atmospheric models. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure in a resourceful way. However, extracting intended meteorological cloud content from the overall measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work a technique is proposed to identify and separate cloud and non-hydrometeor returns from a cloud radar measurements. Firstly the observed cloud reflectivity profile must be evaluated against the theoretical radar sensitivity curves. This step helps to determine the range of receiver noise floor above which it can be identified as signal or an atmospheric echo. However it should be noted that the signal above the noise floor may be contaminated by the air-borne non-meteorological targets such as insects, birds, or airplanes. The second step in this analysis statistically reviews the continual radar echoes to determine the signal de-correlation period. Cloud echoes are observed to be temporally more coherent, homogenous and have a longer de-correlation period than insects and noise. This step critically helps in separating the clouds from insects and noise which show shorter de-correlation periods. The above two steps ensure the identification and removal of non-hydrometeor contributions from the cloud radar reflectivity profile which can then be used for inferring unbiased vertical cloud structure. However these two steps are insufficient for recovering the weakly echoing cloud boundaries associated with the sharp reduction in cloud droplet size and concentrations. In the final step in order to obtain intact cloud height information, identified cloud echo peak(s) needs to be backtracked along the either sides on the reflectivity profile till its value falls close to the mean noise floor. The proposed algorithm potentially identify cloud height solely through the characterization of high resolution cloud radar reflectivity measurements with the theoretical echo sensitivity curves and observed echo statistics for the cloud tracking (TEST). This technique is found to be more robust in identifying and filtering out the contributions due to insects and noise which may contaminate a cloud reflectivity profile. With this algorithm it is possible to improve monsoon tropical cloud characterization using cloud radar.


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