the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 May 2025
Effect of Ionospheric Variability on the Electron Energy Spectrum estimated from Incoherent Scatter Radar Measurements
Abstract. The ion composition in the E-region is modified by auroral precipitation. This affects the inversion of electron density profiles from field-aligned incoherent scatter radar measurements to differential energy spectra of precipitating electrons. Here a fully dynamic ionospheric chemistry model (IonChem) is developed that integrates the coupled continuity equations for 6 ion and 9 neutral species, modeling the rapid ionospheric variability during active aurora. IonChem is used to produce accurate, time-dependent recombination rates for ELSPEC to improve the inversion of electron density profiles to primary electron energy spectra. The improvement of the dynamic recombination rates on the inversion is compared with static recombination rates from the International Reference Ionosphere (IRI) and the steady-state recombination rates from a ionospheric chemistry model, FlipChem. A systematic overestimation at high electron energies can be removed using a dynamic model. The comparison with FlipChem shows that short-timescale density variations are missed in a steady-state chemistry model
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RC1: 'Comment on egusphere-2025-2340', Anonymous Referee #1, 01 Jul 2025
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AC1: 'Reply on RC1', Oliver Stalder, 11 Nov 2025
We thank the referee for their thoughts and inputs. Some revisions were done on that basis, attempting to increase the quality of the paper:
1. The initialisation of minor neutral constituents is clarified and the NRLMSIS2.1 model is used to include NO. The sensitivity to uncertain initial NO density is tested. Minor neutral constituents not represented in NRLMSIS2.1 are initialised with 0 density, with a leading time of half an hour to let them approach steady-state densities. This may not be long enough for all species to reach steady-state, but it also not a requirement. The aim is to construct a reasonable close model of the ionosphere, in regard of uncertain initial conditions. In the auroral region, where precipitation varies spatially and changes rapidly with time, the production of minor species will have a corresponding spatial and temporal variation. Therefore, we cannot expect all species to be at steady-state.
We tested the robustness of this approach towards uncertain initial conditions, and found that the differences are very small.
2. Reaching equilibrium (or steady-state) is not a requirement. While we have uncertain initial conditions and only initialise densities that we have models for, the leading time is intended to mitigate gross mismatches that would otherwise lead to large corrections at the beginning of the dataset, and letting the uninitialised species reach non-zero densities. The largest corrections should occur at the start. Furthermore, both NO and N(4S) are theorised to never reach equilibrium in the limited time between substorms. Auroral precipitation may increase NO and N(4S) concentrations considerably (Bailey et al, 2002). Lastly, due to the rapid variations in precipitation, many other species will also not be in equilibrium over the entire evaluation period. For all these reasons, we feel that our approach of giving the model a limited time to approach steady-state conditions is justified, without the need of actually being at steady-state. This ensures that the all species densities are in a reasonable range.On the time-scales of auroral precipitation, vertical diffusion is small at E-region heights, see e.g. Turunen et al. (2009).
Bernhardt et al. (2000) showed that an artificial airglow is subject to neutral diffusion in all directions, and drifts in response to neutral winds. Ions and electrons are subject to electrodynamic forces present in active aurora (e.g. Gustavsson et al. 2001, Krcelic et al. 2014). Furthermore, there are steep horizontal gradients in auroral precipitation. Therefore we see it more useful to include diffusion, drift and convection in 3 dimensions. This will be incorporated in future work, depending on EISCAT3D data availability.
3. The reaction rates are updated. Future work will include more reactions, in particular decay of excited states.
References:Krcelic, P., Fear, R. C., Whiter, D., Lanchester, B., & Brindley, N. (2024). Variability in the electrodynamics of the small scale auroral arc. Journal of Geophysical Research: Space Physics, 129, e2024JA032623. https://doi.org/10.1029/2024JA032623
Gustavsson, B., et al. (2001), First tomographic estimate of volume distribution of HF-pump enhanced airglow emission, J. Geophys. Res., 106(A12), 29105–29123, doi:10.1029/2000JA900167.
Bernhardt, P. A., M. Wong, J. D. Huba, B. G. Fejer, L. S. Wagner, J. A. Goldstein, C. A. Selcher, V. L. Frolov, and E. N. Sergeev (2000), Optical remote sensing of the thermosphere with HF pumped artificial airglow, J. Geophys. Res., 105(A5), 10657–10671, doi:10.1029/1999JA000366.
Bailey, S. M., C. A. Barth, and S. C. Solomon, A model of nitric oxide in the lower thermosphere, J. Geophys. Res., 107(A8), doi:10.1029/2001JA000258, 2002.
E. Turunen et al., “Impact of different energies of precipitating particles on NOx generation in the middle and upper atmosphere during geomagnetic storms,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 71, no. 10, pp. 1176–1189, July 2009, doi: 10.1016/j.jastp.2008.07.005.
Citation: https://doi.org/10.5194/egusphere-2025-2340-AC1
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AC1: 'Reply on RC1', Oliver Stalder, 11 Nov 2025
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RC2: 'Comment on egusphere-2025-2340', Anonymous Referee #2, 04 Nov 2025
Stalder et al. presents a method for including ion composition changes into the electron flux estimation from incoherent scatter radar observations of electron density. The time variation of the ion composition was taken into account, unlike several other previous investigations. A time-dependent model of ion composition was developed and validated against steady-state models, including the FLIP model. The updated inversion model was used on a previously investigated event. The main takeaway of the paper is that this new ion composition model could be useful for investigations of rapid electron particle precipitation.
The paper presents a useful methodology for including potential impacts from ion composition into the inversion of electron density to electron flux. While there is merit in including these impacts, I did not find the article to be a particularly compelling display of results that would lead one to abandon the steady-state approximation. Figure 9 does not show much of a difference in the inverted numbers, and reading the scales in the bottom panel, the differences are of the order of 10%. Figure 10 seems to present the most compelling result of the investigation and the strongest reason why this method should be used. The impacts of compositional changes associated with precipitation shown here are quite interesting and would be difficult to address with a steady-state model.
Overall, I do not object to the paper being published; however, unless some effort is made to highlight what is truly significant about this investigation, it appears to be a modest advancement in the state of the art. I would recommend including more discussion, specifically on the impact of the results associated with Figure 10.
I have the following comments, which should be addressed:
- Line 65, there needs to be more discussion about this regularization term for the least squares fitting in equation 3. Why was this particular regularization chosen and what does it mean? Considering that the reference is an unpublished reference, it should be explained in more detail why this regularization term is being used. How does this improvement compare to simply fitting the data using least squares?
- Figure 4 does not seem to show any real difference. What is the takeaway point here?
- Figure 6 and Figure 9, bottom panel, should really be plotted as percent differences in the flux. That would be a lot easier to understand. How much of a difference do we actually see here? Is this a 1% difference or a 10% difference?
- As I stated, I think Figure 10 is the most interesting figure in the investigation, and perhaps that should be brought forward in terms of the significance of what is being done here.
- I will just add that there are other investigations that have taken into account the time dependence of the chemistry, in particular, some of the implementations of the GPI D-region model. So, this is not a novel concept. You mentioned a few papers, notably some of Semeter’s work, but there are also other papers which directly solve first order ODEs and then invert the associated ionization rate.
Citation: https://doi.org/10.5194/egusphere-2025-2340-RC2
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This manuscript presents the incorporation of time-dependent chemistry into the ELSPEC inversion technique for estimating precipitating particle information from incoherent scatter radar data. The ability to include time-dependent chemistry is a substantial improvement for ELSPEC with significant utility for future auroral research. The inverse methods are well described and clearly presented. The chemistry underlying the model has several weaknesses, and most of my critiques have to do with the details of the chemical model.
Major Comments:
1. The manuscript does not adequately explain how the concentrations of the minor neutral constituents (NO, N(4S), N(2D), H, O(1D), O(1S)) are initialized in the chemical model. Section 2.4 discusses the initial conditions for the ions in detail, but sensitivity to the initial concentration of the minor neutral constituents are not examined. I am particularly concerned about how NO is handled. The paper cites NRLMSIS2.0 (Emmert et al., 2021), which does not provide NO densities. If the more recent NRLMSIS2.1 was used instead, that version adds NO densities. See Emmert et al. 2022
2. The manuscript does not demonstrate that the time-dependent chemistry is appropriate and reaching equilibrium for all the minor neutral constituents included, particularly NO and N(4S). NO and N(4S) are relatively long-lived species whose concentrations should be effected by vertical diffusion (both molecular and eddy diffusion) in addition to chemical production and loss. The evolution of NO in particular has been studied in depth, for example, by Bailey et al. (2002) and Barth (1992)
Bailey, S. M., C. A. Barth, and S. C. Solomon, A model of nitric oxide in the lower thermosphere, J. Geophys. Res., 107(A8), doi:10.1029/2001JA000258, 2002.
Barth, Charles A. (1992), Nitric oxide in the lower thermosphere, Planetary and Space Science, Volume 40, Issues 2–3, Pages 315-336, ISSN 0032-0633, https://doi.org/10.1016/0032-0633(92)90067-X.
Both of these models are 1-D vertical models describing production, loss, and vertical diffusion by molecular and eddy diffusion. NO takes days to reach equilibrium in these models. Barth (1992) describes running the model for 5 days for it to completely settle. Bailey et al. (2002) writes the following
"The lifetime of an NO molecule to chemical destruction (or e-folding time in the NO density) under illuminated conditions is 19 hours [Barth et al., 2001]. The lifetime of the NO molecule to diffusive transport is approximately one day [Barth, 1992]. Given that the solar illumination varies throughout the day, the abundance of NO at any one time is then representative of the level of solar energy deposition (solar irradiance and auroral energy) over the past day."
For the ground state of atomic nitrogen ( N(4S) ), the principal sink is the reaction
N(4S) + NO -> N2 + O
Therefore, I also expect N(4S) to be similar to NO and take days to equilibrate.
3. The model comparisons between IonChem and FlipChem are using inconsistent reaction rates, which confuses the contrast between time-dependent effects and effects of different rates. The manuscript presents the contrast between IonChem and FlipChem as being primarily due to the time-independent assumptions in FlipChem. The reaction rates in appendix A are provided without a citation to their source. Nonetheless, they appear to be copied from Appendix 5 of the Rees (1989) textbook. FlipChem, however, uses the reaction rates from Richards and Voglozin (2011), which include updates from more recent laboratory measurements.
If you compare appendix A to the table in Richards and Voglozin (2011), many of the rates do not match. The most meaningful way to compare the effects of time-dependent versus time-independent chemistry would be to use two models where the rates exactly match. This could be achieved by making a version of IonChem using the rates from Richards and Voglozin (2011).