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Observation of Atmospheric Boundary-Layer Based on Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 3142

Special Issue Editors


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Guest Editor
Department of Physics, Institute of Earth Sciences, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal
Interests: atmospheric sciences; air pollution control; differential optical absorption spectroscopy (DOAS); ozone hole; optoelectronic remote sensing instrumentation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Applied Physics Department, University of Granada, 18071 Granada, Spain
Interests: aerosol; lidar; remote sensing; atmosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Most human activities take place within the atmospheric boundary layer (ABL), the layer of the atmosphere closest to the Earth's surface. The ABL is directly influenced by the exchange of heat, moisture, and aerosol and gaseous constituents with the surface, as well as the biosphere and the anthropogenic activities. Furthermore, the main physical phenomena occurring in the ABL, such as temperature, wind, humidity, fog, clouds and precipitation, have a direct influence on human activities. Atmospheric profiling allows the measurement and characterization of atmospheric conditions at various heights, and hence, is critical for improving weather forecasts, air quality, and the projections of future climate scenarios, thereby leading to a better understanding of the atmospheric processes occurring in the climatic system. Energy security, public health and safety, transportation, water resources, and food production are the five societal requirements that necessitate  the determination and characterization of the ABL height and profile, respectively. Ground-based sensors and satellite observations provide information on the high temporal variability and strong vertical gradients experienced within and above the ABL. Despite its significance, the ABL remains the most important under-sampled part of the atmosphere, and the scientific community is still trying to fill this informational gap.

With this aim and aligned with the objectives of the EU COST action PROBE (http://probe-cost.eu), this Special Issue is open to contributions dealing primarily with the remote sensing of the ABL, including support from in situ data, modelling approaches, and synergy using different techniques and equipment.

Prof. Dr. Daniele Bortoli
Prof. Dr. Juan Luis Guerrero Rascado
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

19 pages, 6468 KiB  
Article
Near-Surface Wind Profiling in a Utility-Scale Onshore Wind Farm Using Scanning Doppler Lidar: Quality Control and Validation
by Teng Ma, Ye Yu, Longxiang Dong, Guo Zhao, Tong Zhang, Xuewei Wang and Suping Zhao
Remote Sens. 2024, 16(6), 989; https://doi.org/10.3390/rs16060989 - 12 Mar 2024
Viewed by 621
Abstract
Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In [...] Read more.
Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (CNR) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R2) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R2 increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R2 increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms. Full article
(This article belongs to the Special Issue Observation of Atmospheric Boundary-Layer Based on Remote Sensing)
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10 pages, 2781 KiB  
Communication
Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning
by Taekyeong Jin, Jeongmin Cho, Doyoung Jang and Hosung Choo
Remote Sens. 2024, 16(4), 674; https://doi.org/10.3390/rs16040674 - 14 Feb 2024
Viewed by 609
Abstract
This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input [...] Read more.
This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921. Full article
(This article belongs to the Special Issue Observation of Atmospheric Boundary-Layer Based on Remote Sensing)
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19 pages, 4130 KiB  
Article
The Impact of Internal Gravity Waves on the Spectra of Turbulent Fluctuations of Vertical Wind Velocity in the Stable Atmospheric Boundary Layer
by Viktor A. Banakh and Igor N. Smalikho
Remote Sens. 2023, 15(11), 2894; https://doi.org/10.3390/rs15112894 - 01 Jun 2023
Cited by 1 | Viewed by 1187
Abstract
The wave turbulence interactions in the stable boundary layer (SBL) of the atmosphere are studied based on data from lidar measurements of the vertical component of wind velocity during the propagation of internal gravity waves (IGWs). It is shown that as an IGW [...] Read more.
The wave turbulence interactions in the stable boundary layer (SBL) of the atmosphere are studied based on data from lidar measurements of the vertical component of wind velocity during the propagation of internal gravity waves (IGWs). It is shown that as an IGW appears, the amplitude of the spectra of turbulent fluctuations of vertical wind velocity nearby the frequency of quasi-harmonic oscillations induced by an IGW increases significantly, sometimes by several orders of magnitude, compared to the spectra in the absence of an IGW. Since IGW energy is transferred to small-scale turbulence, the amplitude of spectra with the Kolmogorov–Obukhov −5/3 power-law frequency dependence in the inertial frequency range increases. The slope of the spectra in the low-frequency range between the frequency of IGW-induced oscillations and the frequency of the lower boundary of the inertial range exceeds the slope, corresponding to the −5/3 power-law dependence. In this frequency range, the spectra obey the power-law dependence on the frequency with the exponent ranging from −4.2 to −1.9. The average value of the exponent −3 is consistent with a low-frequency slope caused by IGWs in turbulent spectra in the lower SBL. Full article
(This article belongs to the Special Issue Observation of Atmospheric Boundary-Layer Based on Remote Sensing)
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