Year-end Sale % OFF 
Abstract
The article is aimed at presenting a semi-empirical model coded and computed in the programming language Python, which utilizes data gathered with a standard biaxial elastic lidar platform in order to calculate the altitude profiles of the structure coefficients of the atmospheric refraction index C N 2 ( z ) and other associated turbulence parameters. Additionally, the model can be used to calculate the PBL (Planetary Boundary Layer) height, and other parameters typically employed in the field of astronomy. Solving the Fernard–Klett inversion by correlating sun-photometer data obtained through our AERONET site with lidar data, it can yield the atmospheric extinction and backscatter profiles α ( z ) and β ( z ) , and thus obtain the atmospheric optical depth. Finally, several theoretical notions of interest that utilize the solved parameters are presented, such as approximated relations between C N 2 ( z ) and the atmospheric temperature profile T ( z ) , and between the scintillation of backscattered lidar signal and the average wind speed profile U ( z ) . These obtained profiles and parameters also have several environmental applications that are connected directly and indirectly to human health and well-being, ranging from understanding the transport of aerosols in the atmosphere and minimizing the errors in measuring it, to predicting extreme, and potentially-damaging, meteorological events.
Highlights
Performing atmospheric altitude profiles of several atmospheric and meteorological parameters demands great financial costs, cannot be performed at any place or any time, and at present, the current theoretical models have significant limitations [1,2].Currently, the only reliable method to directly obtain meteorological parameters throughout the free atmosphere for low to high altitudes is by launching specialized weather balloons equipped with in-situ sensors [3]
A method for continuously obtaining the altitude profiles of these parameters can be a powerful tool for weather prediction, atmospheric turbulence assessment, and for anticipating temperature inversions which can cause amplified pollution in the environment [4,5], and for predicting extreme weather events [6], and for improving the accuracy of aerosol concentration measurements [7]
Lidar systems are utilized in the fields of high-resolution mapping, geodesy, archaeology, geography, atmospheric physics and many more
Summary
Performing atmospheric altitude profiles of several atmospheric and meteorological parameters demands great financial costs, cannot be performed at any place or any time, and at present, the current theoretical models have significant limitations (reduced spatial and temporal resolutions) [1,2].Currently, the only reliable method to directly obtain meteorological parameters throughout the free atmosphere for low to high altitudes is by launching specialized weather balloons equipped with in-situ sensors [3]. We present a new semi-empirical algorithm based on experimental elastic lidar data and complementary techniques utilized in atmospheric physics to calculate several atmospheric parameters relevant in the study of atmospheric turbulence, and its relation to astronomy, meteorology, environmental studies and human health, without using direct techniques of atmosphere probing. It is primarily based on its implementation in the programming language Python 3.6 to process the large amount of lidar data needed to measure the profiles of the RCS (Range Corrected Signal), and to calculate the structure coefficient of the atmospheric refractive index, C2N (z). The calculation of these parameters yields the others, and can potentially lead to a means of obtaining meteorological parameters through lidar elastic backscatter data alone
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
