Airborne Remote Sounding of Cirrus Cloud Parameters Using ARES Channel Data

Abstract

Results of the phase function, single scattering albedos, and extinction coefficients for three representative cirrus cloud ice crystal size distributions are presented for the Airborne Remote Earth Sensing (ARES) System 2-6.4 microns wavelengths. The ice crystal sizes range from 5 to 800 microns with shapes spanning from bullet rosettes, hollow and solid columns, plates, to aggregates. The computations are carried out on the basis of a unified theory for light scattering by ice crystals recently developed by our research group. Effects of incorporating small ice crystals on the scattering and absorption calculations are assessed. An airborne retrieval algorithm to infer cirrus cloud temperature, optical depth, and mean effective sizes is subsequently presented using the ARES 5.1-5.3 and 3.7 microns channels data. This scheme has been applied to the ARES data collected on September 16, 1995, over the Hanscom AFB area. Validation of the retrieved cloud temperature, optical depth, and mean effective size is carried out using the collocated and coincident ground-based 8.6-mm radar data and in-situ size distributions from the 2D probe on board HARP.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 1997
Accession Number
ADA338109

Entities

People

  • K. N. Liou
  • P. Rolland
  • Pei Yang
  • S. C. Ou

Organizations

  • University of Utah

Tags

Communities of Interest

  • Air Platforms
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Cirrus Clouds
  • Computations
  • Crystal Structure
  • Detection
  • Ground Based
  • Light Scattering
  • Optical Phenomena
  • Optical Properties
  • Optics
  • Particle Size
  • Radiative Transfer
  • Refractive Index
  • Remote Sensing
  • Scattering
  • Validation

Fields of Study

  • Environmental science

Readers

  • Aerosol Science/Aerosol Physics
  • Atmospheric Science/Meteorology
  • Spectroscopy.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference