Evaluation of the AFGL (Air Force Geophysics Laboratory) Cloud Simulation Models Using Satellite Data

Abstract

This report presents the results of the validation of three empirical models of the distribution of the total sky cover over lines and areas; the Burger Area Algorithm (BAA), Burger Line Algorithm (BLA) and the Gringorten Interval Algorithm (GIA). The generation of a sky cover database from satellite imagery, the determination of appropriate sample size and the determination of goodness-of-fit error bounds are discussed. The models are run, and their output compared to empirical cloud cover distributions. Goodness-of-fit statistics are used to evaluate the results of the models. Conclusions and recommendations concerning the utility of the models are presented. Keywords: Atmosphere models, Line of sight, Area coverage, Mathematical models, Statistical analysis, Spatial distributions, Sky cover distributions, Cloud distribution, Sample size determination, Monte Carlo simulation, Cloud detection, Sky cover database, Model validation.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA208713

Entities

People

  • Gary Rasmussen
  • Joseph V. Fiore Jr.
  • Lanning M. Penn

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Cloud Cover
  • Clouds
  • Computational Science
  • Data Science
  • Data Sets
  • Databases
  • Digital Data
  • Goodness Of Fit Tests
  • Information Processing
  • Information Science
  • Monte Carlo Method
  • Statistical Samples
  • Statistical Tests
  • Statistics
  • Surveys

Readers

  • Atmospheric Remote Sensing.
  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

  • Space