scholarly journals Remote Sensing And Surface Observations Of The Response Of The Atmospheric Boundary Layer To A Solar Eclipse

2003 ◽  
Vol 106 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Fanny Girard-Ardhuin ◽  
B. Bénech ◽  
B. Campistron ◽  
J. Dessens ◽  
S. Jacoby-Koaly
2015 ◽  
Vol 51 (2) ◽  
pp. 193-202 ◽  
Author(s):  
V. S. Lyulyukin ◽  
M. A. Kallistratova ◽  
R. D. Kouznetsov ◽  
D. D. Kuznetsov ◽  
I. P. Chunchuzov ◽  
...  

2010 ◽  
Vol 23 (6) ◽  
pp. 433-440 ◽  
Author(s):  
G. I. Gorchakov ◽  
A. K. Petrov ◽  
A. A. Isakov ◽  
E. N. Kadygrov ◽  
A. V. Karpov ◽  
...  

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yonghong Zhang ◽  
Tiantian Dong ◽  
Yunping Liu

Among current detection methods of the atmospheric boundary layer, sounding balloon has disadvantages such as low recovery and low reuse rate, anemometer tower has disadvantages such as fixed location and high cost, and remote sensing detection has disadvantages such as low data accuracy. In this paper, a meteorological element sensor was carried on a six-rotor UAV platform to achieve detection of meteorological elements of the atmospheric boundary layer, and the influence of different installation positions of the meteorological element sensor on the detection accuracy of the meteorological element sensor was analyzed through many experiments. Firstly, a six-rotor UAV platform was built through mechanical structure design and control system design. Secondly, data such as temperature, relative humidity, pressure, elevation, and latitude and longitude were collected by designing a meteorological element detection system. Thirdly, data management of the collected data was conducted, including local storage and real-time display on ground host computer. Finally, combined with the comprehensive analysis of the data of automatic weather station, the validity of the data was verified. This six-rotor UAV platform carrying a meteorological element sensor can effectively realize the direct measurement of the atmospheric boundary layer and in some cases can make up for the deficiency of sounding balloon, anemometer tower, and remote sensing detection.


2011 ◽  
Vol 169 (4) ◽  
pp. 741-753 ◽  
Author(s):  
D. Bala Subrahamanyam ◽  
T. J. Anurose ◽  
Mannil Mohan ◽  
M. Santosh ◽  
N. V. P. Kiran Kumar ◽  
...  

2021 ◽  
pp. 105962
Author(s):  
Gregori de Arruda Moreira ◽  
Guadalupe Sánchez-Hernández ◽  
Juan Luis Guerrero-Rascado ◽  
Alberto Cazorla ◽  
Lucas Alados-Arboledas

2021 ◽  
Author(s):  
Etienne Cheynet ◽  
Martin Flügge ◽  
Joachim Reuder ◽  
Jasna B. Jakobsen ◽  
Yngve Heggelund ◽  
...  

Abstract. The paper presents the measurement strategy and dataset collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and Lidar Planner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The preliminary results show an undocumented variation of the lateral coherence with the distance from the coast. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines.


2016 ◽  
Author(s):  
Grigorii P. Kokhanenko ◽  
Yurii S. Balin ◽  
Sergei V. Nasonov ◽  
Ioganes E. Penner ◽  
Svetlana V. Samoilova ◽  
...  

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