Processing of DMSP Magnetic Data and Its Use in Geomagnetic Field Modeling

Abstract

The DMSP F-7 satellite is an operational Air Force meteorological satellite which carried a magnetometer for geophysical measurements. The magnetometer was located within the body of the spacecraft in the presence of large spacecraft fields. In addition to stray magnetic fields, the data have inherent position and time inaccuracies. Algorithms were developed to identify and remove time varying magnetic field noise from the data. Techniques developed for Magsat were then modified and used to attempt determination of the spacecraft fields, of any rotation between the magnetometer axes and the spacecraft axes, and of any scale changes within the magnetometer itself. The corrected data were then used to attempt to model the geomagnetic field. This was done in combination with data from Magsat, from the standard magnetic observatories, from aeromagnetic and other survey data, and from DE-2 spacecraft magnetic field data. The results obtained are inconsistent and contradictory. Characterization of the problem is clearest when model coefficients are compared between models with DMSP data only and models with other data types only. In that case the gl(1) term in the DMSP models is consistently lower in magnitude by 10-25 nT from the trend inferred from models based on other data.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1989
Accession Number
ADA216440

Entities

People

  • D. Chinn
  • J. R. Ridgway
  • R. A. Langel
  • T. J. Sabaka

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Artificial Satellites
  • Cartesian Coordinates
  • Coordinate Systems
  • Data Processing
  • Detectors
  • Ephemerides
  • Magnetic Fields
  • Magnetometers
  • Measurement
  • Meteorological Satellites
  • Observatories
  • Plastic Explosives
  • Satellite Orbits
  • Space Flight
  • Vector Magnetometers

Fields of Study

  • Physics

Readers

  • Astronomy and Astrophysics.
  • Computational Modeling and Simulation
  • Superconducting Magnet Technology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space