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Time-causal decomposition of geomagnetic time series into secular variation, solar quiet, and disturbance signals

Open-File Report 2017-1037
By:
https://doi.org/10.3133/ofr20171037

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Abstract

A theoretical basis and prototype numerical algorithm are provided that decompose regular time series of geomagnetic observations into three components: secular variation; solar quiet, and disturbance. Respectively, these three components correspond roughly to slow changes in the Earth’s internal magnetic field, periodic daily variations caused by quasi-stationary (with respect to the sun) electrical current systems in the Earth’s magnetosphere, and episodic perturbations to the geomagnetic baseline that are typically driven by fluctuations in a solar wind that interacts electromagnetically with the Earth’s magnetosphere. In contrast to similar algorithms applied to geomagnetic data in the past, this one addresses the issue of real time data acquisition directly by applying a time-causal, exponential smoother with “seasonal corrections” to the data as soon as they become available.

Suggested Citation

Rigler, E.J., 2017, Time-causal decomposition of geomagnetic time series into secular variation, solar quiet, and disturbance signals: U.S. Geological Survey Open-File Report 2017–1037, 26 p., https://doi.org/10.3133/ofr20171037.

ISSN: 2331-1258 (online)

Table of Contents

  • Acknowledgments
  • Abbreviations
  • Abstract
  • Introduction
  • Mathematical Theory
  • Numerical Algorithm and Practical Considerations
  • Verification and Validation
  • Summary and Conclusions
  • References Cited
  • Glossary
  • Appendix 1. Pseudocode
Publication type Report
Publication Subtype USGS Numbered Series
Title Time-causal decomposition of geomagnetic time series into secular variation, solar quiet, and disturbance signals
Series title Open-File Report
Series number 2017-1037
DOI 10.3133/ofr20171037
Year Published 2017
Language English
Publisher U.S. Geological Survey
Publisher location Reston, VA
Contributing office(s) Geologic Hazards Science Center
Description iv, 26 p.
Online Only (Y/N) Y
Google Analytic Metrics Metrics page
Additional publication details