the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jan 2026
Mean Energy variation of Diffuse Aurora Observed with the ASIS Spectrograph
Abstract. We analyze more than 20,000 spectra of diffuse aurora recorded by the ASIS spectrograph over two winters, focusing on events classified as diffuse by an AI-based system, which includes pulsating auroras as a major subset. Electron characteristic energies were derived from calibrated red/blue emission ratios using a lookup-table approach based on forward modeling. To assess the systematic evolution of these energies with magnetic local time (MLT), we applied a Bayesian regression framework to the median of the mean-energy estimates, providing a robust characterization despite the large intrinsic variability of diffuse precipitation. The results reveal a clear hardening of the precipitation toward the dawn sector. Using the AE index as a proxy for geomagnetic activity, we further show that the MLT-dependent trend is influenced not only by the instantaneous AE level but also by the recent magnetospheric history, as revealed by time-lagged classifications. These findings indicate that diffuse auroral precipitation retains an imprint of preceding magnetospheric dynamics and are consistent with chorus-wave scattering modulated by evolving plasma-sheet conditions.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Annales Geophysicae.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-385', Kirsti Kauristie, 18 Feb 2026
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RC2: 'Comment on egusphere-2026-385', Anonymous Referee #2, 19 Apr 2026
This review covers the revised manuscript entitled “Mean Energy variation of Diffuse Aurora Observed with the ASIS Spectrograph” by Cessateur et al.
"General comments"
This manuscript presents a statistical analysis of diffuse auroral electron precipitation energies derived from more than 20,000 spectra obtained with the ASIS spectrograph over two winter seasons. The dataset is substantial and valuable in the aurora science community, and the combination of spectroscopic observations with AI-based auroral classification and Bayesian statistical methods is technically sound and timely. In particular, the availability of a large spectroscopic dataset of diffuse aurora represents a significant observational asset. Such datasets are relatively rare and have clear potential to provide important constraints on the energy characteristics of auroral precipitation.
However, in its current form, the manuscript does not demonstrate sufficient scientific novelty. The main result, a post-midnight hardening of the electron precipitation, is broadly consistent with previous studies and is not developed into a fundamentally new physical insight. While the methodology is modern and carefully implemented, the scientific interpretation remains largely confirmatory. For these reasons, I believe that the manuscript would require substantial revision, including additional analysis and a clearer formulation of the scientific contribution.
"Specific comments"
1. Lack of clear scientific novelty ("Conclusion" section)
The primary conclusion (MLT-dependent hardening of electron precipitation) has already been reported in multiple previous studies using optical, radar, and satellite observations.
The present work confirms this trend using a different dataset and methodology, but does not go beyond confirmation toward new physical understanding. The manuscript would benefit from explicitly addressing:
What new physics is revealed by this dataset?
What aspect of the problem was previously inaccessible?
At present, the novelty appears methodological rather than scientific.
2. Mixing of physically distinct diffuse auroral subtypes ("Results" section)
The manuscript groups together multiple types of diffuse aurora (including pulsating, patchy, amorphous, and diffuse aurora) into a single category based on AI classification. While this approach is reasonable as a first-order selection, it raises a significant physical concern. These different auroral subtypes are known to be associated with distinct precipitation mechanisms and electron energy characteristics. For example:
Pulsating aurora is typically linked to chorus-driven scattering of higher-energy electrons,
Patchy or amorphous diffuse aurora may involve different source populations and wave modes (e.g., ECH waves),
Their MLT dependencies are also expected to differ.
By combining these regimes into a single statistical ensemble, the analysis effectively mixes different physical processes. As a result, the derived MLT-dependent energy trend may reflect changes in the relative occurrence of different auroral types, rather than a true evolution of a single underlying process.
This issue directly affects the physical interpretation of the results. To strengthen the manuscript, I strongly recommend that the authors perform a refined classification within the “diffuse” category (e.g., isolating pulsating aurora), and re-evaluate the MLT dependence of electron energy separately for each subtype.
3. Ambiguity in the definition of “mean energy” ("Results" section)
The inferred “mean energy” relies on a red/blue line ratio, a Maxwellian assumption, and steady-state transport models. However, diffuse and especially pulsating aurora are known to involve non-Maxwellian distributions, time-dependent precipitation, and multi-component electron populations.
This raises a fundamental question that what physical quantity does the derived “mean energy” actually represent?
The manuscript should more clearly discuss ystematic biases in the energy estimation, possible MLT-dependent biases, and limitations in interpreting absolute energy values.
4. Limited physical interpretation of AE dependence ("Dependence on the AE index" section)
The AE-based analysis shows that trends are clearer under quiet conditions and more scattered during active periods. This behavior is expected due to increased contamination from injections and discrete aurora during active times. However, the manuscript does not extract new physical insight from this result. In particular, the role of different wave modes (e.g., chorus vs ECH) is not disentangled, and the connection to magnetospheric drivers remains indirect. Using AE alone as a proxy may be insufficient to support the proposed interpretation.
5. Limited added value of Bayesian analysis
The Bayesian regression is carefully implemented, but its contribution to the scientific interpretation is limited. It essentially quantifies a trend that is already visible in the data, without leading to new physical conclusions. The manuscript would benefit from clarifying that what additional insight is gained from the Bayesian framework, and beyond confirming the existence of the trend.
The dataset and methodology are promising, but substantial additional analysis and a clearer demonstration of scientific novelty are required. Strengthening the physical interpretation and making fuller use of the spectroscopic capabilities would significantly improve the manuscript. I hope that these comments will assist the authors in further improving the manuscript.
Citation: https://doi.org/10.5194/egusphere-2026-385-RC2
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The manuscript presents results from a statistical study analyzing energy of electron precipitation causing pulsating auroras. Precipitation energy is estimated with the ratio between red (630 nm) and blue (478 nm) emission lines’ intensities as derived from spectrograph data. The study gives a very robust confirmation of earlier findings proposing hardening of the precipitation energy in dawn sector with increasing Magnetic Local Time (MLT). In addition, new quantitative information about the MLT trend is presented. The data analysis methods used in the study are very professional and the manuscript is clearly written. I propose acceptance of the manuscript with some minor clarifications as specified below.
A general remark: I wonder how valid the MLT trend is at late dawn hours? Figure 2 shows that the number of data points in MLT bin 08 is rather small. I get the impression that Figure 5 has given guidance for the formula of Eq. 2, which inherently assumes the trend of increasing energy to cover also the late dawn sector. Do the already known properties of chorus waves or Arase observations support the trend to cover also early prenoon sector (with the recognition that the analysis by Ito et al., covers dawn sector until MLT 6). Furthermore, is there any risk that the energy estimates in the MLT bin 08 are somehow contaminated by illumination of the rising Sun?
Minor comments:
Line 28, Lorentz factor: This may not be a familiar term for all readers. Please, give a more detailed explanation for it.
Line 62, characteristic energies of electron precipitation causing optical pulsations: Extending the range to several hundreds of keV may not be appropriate here, because at those energy levels precipitation is a more relevant factor in D-layer chemistry than in auroral observations.
Line 84, introduction of the ASIS at Tromsö: It would be good to give here also the relationship between UT and MLT for Tromsö, because it helps the reader to interpret some data plots later in this manuscript (e.g. Figs 1, 3, and 4)
Line 102, classification by Nanjo et al.: Besides the auroral categories, it would be good to mention something about the AI-code capability to make distinction between cloudy conditions with background auroras and diffuse auroras.
Lines 127-128 introduction of the GLOW and Transsolo: The concept “stream” may not be familiar for all readers. Please clarify.
Figure 4, panel a: Colors in the strip showing AI categorization are confusing because they do not match with the colors given in the legend on the right side of the panel.
Equation 2: It becomes later clear in the text that “breakpoint” in the formula refers to MLT=4, but it would be good to mention it explicitly already here when introducing the formula. The “+” sign at the end of the formula is mysterious.
Lines 187-188: I guess that this is textbook material, but if possible with reasonable effort it would be nice to have a reference for the statement of 1.4826xMAD corresponding to the Gaussian-equivalent standard deviation.
Section 4.2, magnetospheric memory: The decision to discuss lags of 2 and 24 hours sounds a bit arbitrary…Is there a specific reason to select those two lag times? The conclusion of AE index exceeding 300 nT during the 24-hour period prior to pulsations to be a characteristic feature for the observed MLT trend is, in my opinion, somewhat weak, because ≥24h periods of continuous AE<300 nT are rather rare.