Statistical characterization of joule heating associated with ionospheric ULF perturbations using superDARN data

Ultra low frequency (ULF; 1 mHz ‐ several Hz) waves are key to energy transport within the geospace system, yet their contribution to Joule heating in the upper atmosphere remains poorly quantified. This study statistically examines Joule heating associated with ionospheric ULF perturbations using Super Dual Auroral Radar Network (SuperDARN) data spanning middle to polar latitudes. Our analysis utilizes high‐time‐resolution measurements from SuperDARN high‐frequency coherent scatter radars operating in a special mode, sampling three “camping beams” approximately every 18 s. We focus on ULF perturbations within the Pc5 frequency range (1.6–6.7 mHz), estimating Joule heating rates from ionospheric electric fields derived from SuperDARN data and height‐integrated Pedersen conductance from empirical models. The analysis includes statistical characterization of Pc5 wave occurrence, electric fields, Joule heating rates, and azimuthal wave numbers. Our results reveal enhanced electric fields and Joule heating rates in the morning and pre‐midnight sectors, even though Pc5 wave occurrences peak in the afternoon. Joule heating is more pronounced in the high‐latitude morning sector during northward interplanetary magnetic field conditions, attributed to local time asymmetry in Pedersen conductance and Pc5 waves driven by Kelvin‐Helmholtz instability. Pc5 waves observed by multiple camping beams predominantly propagate westward at low azimuthal wave numbers , while high‐m waves propagate mainly eastward. Although Joule heating estimates may be underestimated due to assumptions about empirical conductance models and the underestimation of electric fields resulting from SuperDARN line‐of‐sight velocity measurements, these findings offer valuable insights into ULF wave‐related energy dissipation in the geospace system.

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Related Links

Related Software #1 : shibaji7/HSR_ULF_Wave_Analysis: HSR Joule Heating Paper

Related Software #2 : SuperDARN/pydarn: pyDARN v3.0

Related Software #3 : SuperDARN Radar Software Toolkit (RST) 5.0

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Author Shi, Xueling
Chakraborty, S.
Baker, J. B. H.
Hartinger, M. D.
Wang, Wenbin
Ruohoniemi, J. M.
Lin, Dong
Lotko, William
Sterne, K.
McWilliams, K. A.
Publisher UCAR/NCAR - Library
Publication Date 2025-03-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2025-07-10T19:53:49.906761
Metadata Record Identifier edu.ucar.opensky::articles:43264
Metadata Language eng; USA
Suggested Citation Shi, Xueling, Chakraborty, S., Baker, J. B. H., Hartinger, M. D., Wang, Wenbin, Ruohoniemi, J. M., Lin, Dong, Lotko, William, Sterne, K., McWilliams, K. A.. (2025). Statistical characterization of joule heating associated with ionospheric ULF perturbations using superDARN data. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7z60tfn. Accessed 31 July 2025.

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