Split-step approach to electromagnetic propagation through atmospheric turbulence using the modified von Karman spectrum and planar apertures

2014 ◽  
Vol 53 (12) ◽  
pp. 126107 ◽  
Author(s):  
Monish R. Chatterjee ◽  
Fathi H. A. Mohamed
2021 ◽  
Author(s):  
Soo-Hyun Kim ◽  
Jeonghoe Kim ◽  
Jung-Hoon Kim ◽  
Hye-Yeong Chun

Abstract. The cube root of the energy dissipation rate (EDR), as a standard reporting metric of atmospheric turbulence, is estimated using 1-Hz quick access recorder data from Korean-based national air carriers with two different types of aircraft [Boeing 737 (B737) and B777], archived for 12 months from January to December 2012. Various EDRs are estimated using zonal, meridional, and derived vertical wind components, and the derived equivalent vertical gust (DEVG). Wind-based EDRs are estimated by (i) second-order structure function (EDR1), (ii) power spectral density (PSD), considering the Kolmogorov’s -5/3 dependence (EDR2), and (iii) maximum-likelihood estimation using the von Kármán spectral model (EDR3). DEVG-based EDRs are obtained mainly by vertical acceleration with different conversions to EDR using (iv) the lognormal mapping technique (EDR4) and (v) the predefined parabolic relationship between the observed EDR and DEVG (EDR5). For the EDR1, second-order structure functions are computed for zonal, meridional, and vertical wind within the defined inertial subrange. For the EDR2 and EDR3, individual PSDs for each wind component are computed using the Fast Fourier Transform over every 2-minute time window. Then, two EDR estimates are computed separately by employing the Kolmogorov-scale slope (EDR2) or prescribed von Kármán wind model (EDR3) within the inertial subrange. The resultant EDR estimates from five different methods follow a lognormal distribution reasonably well, which satisfies the fundamental characteristics of atmospheric turbulence. Statistics (mean and standard deviation) of log-scale EDRs are somewhat different from those found in a previous study using a higher frequency (10 Hz) of in situ aircraft data in the United States, likely due to different sampling rates, aircraft types, and locations. Finally, five EDR estimates capture well the intensity and location of three strong turbulence cases that are relevant to clear-air turbulence (CAT), mountain wave turbulence (MWT), and convectively induced turbulence (CIT), with different characteristics of the observed EDRs: 1) zonal (vertical) wind-based EDRs are stronger in the CAT (CIT) case, while MWT has a peak of EDRs in both zonal and vertical wind-based EDRs, and 2) the CAT and MWT cases occurred by large-scale (synoptic-scale) forcing have more variations in EDRs before and after the incident, while the CIT case triggered by smaller mesoscale convective cell has an isolated peak of EDR.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ming Li ◽  
Wei Zhao

von Karman originally deduced his spectrum of wind speed fluctuation based on the Stokes-Navier equation. Taking into account, the practical issues of measurement and/or computation errors, we suggest that the spectrum can be described from the point of view of the golden ratio. We call it the golden ratio phenomenon of the von Karman spectrum. To depict that phenomenon, we derive the von Karman spectrum based on fractional differential equations, which bridges the golden ratio to the von Karman spectrum and consequently provides a new outlook of random data following the von Karman spectrum in turbulence. In addition, we express the fractal dimension, which is a measure of local self-similarity, using the golden ratio, of random data governed by the von Karman spectrum.


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