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Journals / Turkish Journal of Physics / 2013 / Cilt: 37 - Sayı: 1

Quantitative characterization of atmospheric desert particles via multispectral LiDAR method (UV to IR), using genetic algorithms and compared with Chomette's mode

Samia BOUZID
Samir ZEGHBIB
Abdelha d CHAABI
Pages
151–158
Publication Date
2022-11-05
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our results are compared later with the well-known Chomette's desert model.

📌 3 Key Takeaways

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Objective & Hypothesis:Size distribution of desert aerosols was measured via LiDAR multi-spectral method using laser wavelengths from UV to mid-IR $lambda$ = 0.193, 0.694, 1.064, 3.370 and 10.60 $mu$ m, and applying statistical Markov chains and genetic algorithms.
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Key Findings & Data:Size distribution of desert aerosols was measured via LiDAR multi-spectral method using laser wavelengths from UV to mid-IR $lambda$ = 0.193, 0.694, 1.064, 3.370 and 10.60 $mu$ m, and applying statistical Markov chains and genetic algorithms.
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Conclusion & Impact:Our results are compared later with the well-known Chomette's desert model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Size distribution of desert aerosols was measured via LiDAR multi-spectral method using laser wavelengths from UV to mid-IR $lambda$ = 0.193, 0.694, 1.064, 3.370 and 10.60 $mu$ m, and applying statistical Markov chains and genetic algorithms. Our results are compared later with the well-known Chomette's desert model.

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Cite This Article

BOUZID, S., ZEGHBIB, S., CHAABI, A. D. (2022). Quantitative characterization of atmospheric desert particles via multispectral LiDAR method (UV to IR), using genetic algorithms and compared with Chomette's mode. Turkish Journal of Physics, Cilt: 37 - Sayı: 1, 151–158. https://ajindex.com/articles/614041

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References (1)

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