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Periodicities in fair weather potential gradient data from multiple stations at different latitudes

Tacza, J., Nicoll, K. and Macotela, E. (2022) Periodicities in fair weather potential gradient data from multiple stations at different latitudes. Atmospheric Research. ISSN 0169-8059 (In Press)

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Abstract/Summary

Analysis of the variation of the potential gradient (PG) at ground level is important to monitor the global electric circuit and the different solar and geophysical phenomena affecting it. However, this is challenging since several local factors (e.g., meteorological) produce perturbations in the potential gradient. Time series and spectral analysis of PG at several stations can help to minimise local effects so that global effects may be more clearly observed. In this work, for the first time we performed spectral analysis of the potential gradient recorded at several sites located at Vostok, Concordia, Halley and Casleo (Southern Hemisphere), and Sodankyla and Reading (Northern Hemisphere). In order to find the main periodicities and how the amplitude of those periods change as a function of time we use the Lomb-Scargle periodogram and the wavelet transform, respectively. For all PG sites we found periodicities of 0.5-, 1-, ~180- and 365-day. Our results show that the 0.5-day (1-day) periodicity is more prominent during the months of June-July-August (December-January-February). Evidence of ~27- and ~45-day periods was also observed at multiple sites. Further analysis using the cross-wavelet transform for PG versus cosmic rays, PG versus Madden-Julian Oscillation index, and PG versus meteorological parameters, show clues that the 27- and 45-day periods are likely related to the solar rotation and Madden-Julian Oscillation, respectively. Furthermore, our results show that during the passages of co-rotating interaction regions, the 27-day period for PG vs cosmic rays XWT is stronger than for the other XWT analysis.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:105231
Publisher:Elsevier

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