Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique

We introduce the Whistler Identification by Spectral Power Estimation and Recognition (WhISPER) algorithm, a novel automated technique for detecting whistler waves in the top side of the Earth’s ionosphere. WhISPER is the first step towards a comprehensive system designed to accumulate and analyze a large dataset of whistler observations, which has been developed to advance our understanding of whistler generation and propagation. Unlike conventional image-correlation-based techniques, WhISPER identifies whistlers based on their energy content, enhancing computational efficiency. This work presents the results of applying WhISPER to four years (2019–2022) of top-side ionospheric magnetic field data. A statistical analysis of over 800,000 detected whistlers reveals a strong correlation with lightning activity and (as expected) higher occurrence rates during local summer months. The presented results demonstrate the excellent performance of the WhISPER technique in identifying whistler events.

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Publication Details

Journal
Atmosphere
Published
2025-04-29
DOI
https://doi.org/10.3390/atmos16050522
Citations
2
Primary Topic
Ionosphere and magnetosphere dynamics
Type
article
Field-Weighted Citation Impact
2.31

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article

Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique

Giulia D’Angelo, R. Battiston, C. Neubüser, Dario Recchiuti et al.
2 citations
Atmosphere
Ionosphere and magnetosphere dynamics
2.31
article

Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique

Giulia D’Angelo, R. Battiston, C. Neubüser, Dario Recchiuti, Mirko Piersanti, W.J. Burger, Emanuele Papini
article en
2 citations

Abstract

We introduce the Whistler Identification by Spectral Power Estimation and Recognition (WhISPER) algorithm, a novel automated technique for detecting whistler waves in the top side of the Earth’s ionosphere. WhISPER is the first step towards a comprehensive system designed to accumulate and analyze a large dataset of whistler observations, which has been developed to advance our understanding of whistler generation and propagation. Unlike conventional image-correlation-based techniques, WhISPER identifies whistlers based on their energy content, enhancing computational efficiency. This work presents the results of applying WhISPER to four years (2019–2022) of top-side ionospheric magnetic field data. A statistical analysis of over 800,000 detected whistlers reveals a strong correlation with lightning activity and (as expected) higher occurrence rates during local summer months. The presented results demonstrate the excellent performance of the WhISPER technique in identifying whistler events.

AtmosphereVol. 16(5)
University of Trento (IT), University of L'Aquila (IT), Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications (IT), Institute for Space Astrophysics and Planetology (IT), Istituto Nazionale di Fisica Nucleare, Roma Tor Vergata (IT)
Ministero dell'Università e della Ricerca
Openalex Percentile: Top 12%
Ionosphere and magnetosphere dynamics
2.31
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Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique — Giulia D’Angelo, R. Battiston, et al. · Atmosphere (2025) | TGRS Research Map | TGRS