ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Publication Date: 2019
    Description: Abstract Fast magnetosonic (MS) waves can play an important role in the evolution of the inner magnetosphere. However, there is still not an effective method to quantitatively identify such waves for observations of the Van Allen Probes reasonably. In this paper, we used Van Allen Probes data from 18 September 2012 to 30 September 2014 to find a more comprehensive automatic detection algorithm for fast MS waves through statistical analysis of the major properties, including the planarity, ellipticity, and wave normal angle of whole fluctuations using the singular value decomposition method. According to a control variate method, we find an obvious difference between fast MS waves and other waves in the statistical distribution of their major properties. After eliminating the influence of background noises, by excluding fluctuations at L 〈 1.8, we set up an automatic detection algorithm applied to fast MS waves, that is, smaller than 0.2 for the absolute value of wave ellipticity, larger than 70° for the wave normal angle, with frequency range of 2 Hz to 1.5 fLHR (fLHR is the local lower hybrid resonance frequency). Finally, we have checked the planarity to verify availability of this method and tested this completely automatic method on the Van Allen Probes data and found some results consistent with previous studies. Inside the plasmapause, we found that there is a more obviously favorable occurrence of MS waves at dusk sector with increasing magnetic latitudes.
    Print ISSN: 2169-9380
    Electronic ISSN: 2169-9402
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...