Monte Carlo Simulation of Detection of Cirrus Cloud Properties By Micro Pulse Lidar.

Abstract

The development of the Micro Pulse Lidar (MPL) provides researchers with a system capable of continuous, eye-safe monitoring of atmospheric properties. The MPL operates with low energy, high pulse repetition frequency radiation in the visible portion of the spectrum. To investigate the interaction between visible radiation and atmospheric constituents, a model using Monte Carlo techniques has been refined to simulate MPL return profiles. An inherent feature of the MPL is its narrow receiver field of view (FOV) which is necessary to limit background noise. The effect of such a FOV and the role multiple scattering effects play in MPL operations are investigated in this study. Cloud base height and the radiative properties of cirrus clouds are important for determining the radiation budget of the planet. Inferred cirrus cloud radiative properties vary with the type of crystals assumed to compose the model clouds. To properly model optically thin clouds, it is important to include a standard background atmosphere composed of Rayleigh and aerosol scatterers. Its inclusion allows one to take advantage of information deduced from both the cloud and above-cloud layer. Information that is unavailable when sampling optically thick clouds. This capability plays a pivotal role in an inversion algorithm that is developed and described. It is shown that the algorithm allows one to infer important cloud optical properties such as volume extinction coefficient, cloud optical depth, and isotropic backscatter to extinction ratio, also known as the lidar ratio.

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Document Details

Document Type
Technical Report
Publication Date
May 17, 1996
Accession Number
ADA311851

Entities

People

  • James A. Cotturone Jr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Atmospheric Sciences
  • Backscattering
  • Cirrus Clouds
  • Data Science
  • Detection
  • Detectors
  • Forward Scattering
  • Information Science
  • Lidar
  • Monte Carlo Method
  • Optical Properties
  • Optics
  • Radiative Transfer
  • Simulations
  • Statistical Analysis
  • Statistics

Fields of Study

  • Physics

Readers

  • Aerosol Science/Aerosol Physics
  • Computational Fluid Dynamics (CFD)
  • Optical Physics and Photonics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference