Investigation of the Prediction of Lightning Strikes Using Neural Networks

Abstract

A neural network is being trained to predict lightning at Cape Canaveral for periods up to two hours in advance. Inputs consists of ground based field mill data, meteorological tower data, lightning location data and radiosonde and rapid changes in the field mill data, offset in time, provide the 'forecasts' or 'desired output values' used to train the neural network through back propagation. Examples of input data are shown and an example of data compression using a hidden layer in the neural network is discussed. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Oct 11, 1990
Accession Number
ADA229042

Entities

People

  • Arnold A. Barnes Jr.
  • Donald S. Frankel

Organizations

  • Air Force Systems Command

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Satellites
  • Cloud Physics
  • Clouds
  • Communication Satellites
  • Department Of Defense
  • Electric Fields
  • Electricity
  • Ground Based
  • Lightning
  • Meteorological Data
  • Meteorological Radar
  • Network Architecture
  • Neural Networks
  • Static Electricity
  • United States

Fields of Study

  • Environmental science

Readers

  • Approximation Theory.
  • Atmospheric Science/Meteorology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks