Some Comparisons between Probabilities of Cloud-Free Lines-of-Sight Estimated from Aircraft and from Sky Cover Observations.

Abstract

For a period of more than 6 years, aircrews made observations of the presence, or absence, of clouds on lines-of-sight between the aircraft and the surface of the earth. These observations were used to estimate the probability of a cloud-free line-of-sight (CFLOS) from a given altitude at a given angle. The probabilities obtained from observations in the vicinity of six stations in the United States were compared with probabilities estimated from routine sky-cover observations taken by weather observers at these stations. A model was used to transform sky-cover observations to CFLOS probabilities. The estimates obtained from the model were generally higher than those obtained from the aircrews' observations. If it is assumed that probabilities obtained from the samples of aircrew observations are better estimates of the true probabilities than estimates inferred from frequency distributions of sky cover categories, the CFLOS probabilities obtained from the CFLOS model are biased. The CFLOS model will yield CFLOS probabilities for any hour of the day, season of the year, and altitude at any geographical location where routine observations of sky cover, altitude and amount are taken. When the model probabilities are increased to correct for bias, they provide good estimates of true probabilities of a CFLOS. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 08, 1980
Accession Number
ADC021847

Entities

People

  • Eugene A. Bertoni
  • Iver A. Lund

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Aircrafts
  • Altitude
  • Frequency
  • Line Of Sight
  • Military Aircraft
  • Observation
  • Observers
  • Probability
  • United States
  • Vehicles

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference