In-Flight Turbulence Detection.

Abstract

A limited set of radar and aircraft data acquired during the 1981 and 1982 Joint Agency Turbulence Experiment are used to compare incoherent and coherent radar methods for atmospheric turbulence severity estimation. Time series of ground-based radar in-phase and quadrature signal return data are processed by Doppler (Fast fourier tranform) and incoherent (R-meter with and without noise correction) methods to determine Doppler spectrum variance. These variance data serve as input to a turbulence algorithm to derive estimates of turbulence severity. Theses estimates are then compared with in-situ aircraft measurements. Results show the order of preference for the radar methods is Dopple, R-meter with noise correction, and R-meter without noise correction. The Doppler, R-meter with noise correction, and R-meter without noise correction. The R-meter without noise correction method must be considered unreliable since it results in large overestimates of turbulence severity when the signal to noise ratio is less than about 12 dB. The R-meter with noise correction, and R-meter with noise correction method generally duplicates well the results derived from Doppler analysis and may be considered a reasonable alternative when Doppler capability is not available. Keywords: Incoherent radar; Doppler radar; R-meter; Turbulence severity; Eddy dissipation rate; Composite severity class.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 1985
Accession Number
ADA160380

Entities

People

  • A. R. Bohne

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Atmospheric Motion
  • Atmospheric Sciences
  • Classification
  • Coherent Radar
  • Composite Materials
  • Data Sets
  • Detection
  • Doppler Radar
  • False Alarms
  • Geophysics
  • Ground Based
  • Measurement
  • Radar
  • Remote Sensing
  • Turbulence

Fields of Study

  • Physics

Readers

  • Approximation Theory.
  • Radar Systems Engineering.
  • Regression Analysis.