Modeling the Ion Chemistry of the D-Region: A Case Study Based Upon the 1966 Total Solar Eclipse.

Abstract

The 12 November 1966 solar eclipse has been modeled by a large multi-species chemistry code and the results compared with the numerous experimental measurements which were made at that time. Good agreement between measured preeclipse values of electron density and code-predicted values is obtained when ionization by precipitating electrons from the radiation belts is included. The current gas-phase ion chemistry does not predict the rapid decrease and subsequent reconstitution of the electron density about totality in the 65 to 85 km region, nor does it produce the large amounts of negative ions about 70 km which can be inferred from the experimental data. While basic constraints can be placed on the electron attachment processes because of the experimental data, an entirely new class of physical processes may possibly need to be included to explain this phase of D-region behavior. Comparison with experimental data provides a means of validating the basic atmospheric modeling computer codes which are used as input for Army communication systems and in the Army nuclear weapons effect community. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1978
Accession Number
ADA061619

Entities

People

  • F. E. Niles
  • M. G. Heaps
  • Robert D. Sears

Organizations

  • Atmospheric Sciences Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Counter WMD
  • Electronic Warfare
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Altitude
  • Atmospheres
  • Atmospheric Sciences
  • Chemical Reactions
  • Chemistry
  • Cosmic Rays
  • Databases
  • Electron Density
  • Electron Flux
  • Electrons
  • Experimental Data
  • Ionization
  • Measurement
  • Radiation
  • Solar Eclipses
  • Weapons
  • Weapons Effects

Readers

  • Quantum Chemistry
  • Space/Atmospheric Physics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Microelectronics