the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Mar 2025
Satellite impact of Carrington-level geomagnetic storm particle fluxes and fluences
Abstract. The estimated recurrence rates of the most extreme space weather events, like the Carrington event of 1859, warrant investigations of their potential impact on modern satellite-based infrastructure. Our study is based on Extreme Value Theory (EVT) and radial diffusion to estimate worst-case particle fluxes and fluences of relativistic radiation belt (RB) electrons and solar energetic particles (SEPs) for a Carrington-level geomagnetic storm. We use Geant4 to assess the Total Ionizing Dose (TID), Single Event Upset (SEU) rates, and solar cell degradation as a result of such conditions. We find that the electron and proton fluxes exceed the fluxes experienced by the Van Allen probes during nominal conditions by more than an order of magnitude, leading up to 10 krad of TID behind 3 mm of aluminium equivalent shielding. This is equivalent to ten years of nominal operation on geosynchronous orbit and exceeds a century of nominal exposure on the orbit of the International Space Station. Our results show that the expected SEU rates in radiation-hardened satellite electronics would remain below one SEU per MByte per day, equivalent to the nominal rate received in the Van Allen belts. Satellites on lower orbits would experience an increase in SEU rates by up to four orders of magnitude compared to nominal conditions. For satellites using non-radiation hardened, off-the-shelf electronics, this would mean potentially disruptive SEU rates. We estimate up to 3 % reduction in solar cell power output assuming typical cover glass thicknesses, potentially shortening operational lifetimes or requiring mission adjustments. In conclusion, conservatively designed satellites using adequate shielding and radiation-hardened components would likely survive the outlined scenario, experiencing only accelerated ageing during the event. Satellites lacking adequate shielding or radiation-hardening would be disproportionately affected, emphasizing the importance of incorporating radiation resilience into future satellite designs and mission planning.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Annales Geophysicae: Minna Palmroth.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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RC1: 'Comment on egusphere-2025-1279', Piers Jiggens, 13 May 2025
This paper has an admirable scope in deriving a theoretical extreme environment and deriving the effects and comparing the results with quiescent environments. I applaud the effort to combine sophisticated statistical approaches with sophisticated particle transport codes. However, there are some shortfalls which need to be addressed before publication.
Major Issues
p5/$2.1.3 - a solid mathematical background is given into the statistical approach for EVT. However, most readers will be from the heliophyiscs area. In any case the plots of the Q-Q and overall distribution and extension with the EVT would be essential to give trust in the EVT results.
p6/line 165 - Plots of these distributions is essential here and the recurrence of the Carrington event along with overlaid points (at the 3 derived energies) on the fit presented in Figure 1.
p7/line 185 - It's unclear if the spectra were derived from the peak of the particle spectra along the orbit at each energy or if the mean value was used. For GEO the models may consider only small changes in L* but for other orbits this will be significant. There are other models (e.g. FLUMIC in the internal charging part of Spenvis) which are designed to characterise the worst case environment and would be better comparisons for your output than mean model results.
p9/ Table 2 - The SAPPHIRE model run is meaningless with the inputs used. The output is a function of confidence level, solar cycle phase and mission duration. It's not clear what mission duration was used nor which cycle phase. 50% confidence will often yield very low flux outputs especially when combined with short mission durations because, as the authors are aware, the SEP processes are highly stochastic. Furthermore, SAPPHIRE gives outputs of 1-in-n year events with n including values of [100,300] which would be perfect for comparison.
p12/line 215 - Scaling the peak flux to derive the fluence with an assumption of a steady flux of 1.23 days is completely unjustified. Particle fluxes rise and fall by orders of magnitude over this time period and at high energies especially this is likely to generate a very high fluence. If the authors want event fluence they should use the EVT on the list of event fluences and use model outputs of event fluence. Such coarse scaling cannot be justified.
p15/line 280 - Proton-induced SEEs are usually as a result of nuclear interactions and not from direct ionisation. This means that there are dedicated proton cross-sections not as a function of LET but of incident proton energy. Simulating the secondary particles and deriving is physically valid their LET but statistics will may be an issue and possibly the counting of particles locally generated. It's not clear that this is handled efficiently in the simulation. It is possible to use biasing to scale the XS and enhance the probability of interaction, the developers are working on improving the biasing framework in GRAS, it'll come out with GRAS 7, also to make these kind of simulaitons easier. Whether brute force without biasing works really depends on how many other "variables" are in the set up. Given the simulation domain there are serious concerns. It would be better to have used proton cross sections folded with the shielded proton flux at the boundary before the detector. These cross sections handle nuclear interactions and save you from the tyranny of statistics. Note that whilst it's just comparative between environments that can be ok but on p19/line 230 the authors make a statement about absolute risk which is possibly deeply flawed.
p20/Figure 8 - It is not clear how the authors are extending the SEP spectrum below 10 MeV, whether the fit presented in Figure 3 is extended down or not. This matters here because the dominant particle population for solar cell damage is below 10 MeV for nominal coverglass thicknesses. It looks like the degradation at low coverless i might be (severely) under-predicted.
Minor Issues
p6/line 150 - Your data is in GEO so those are the direct comparisons. That might be lost on the user in the way it's put.
p11/Figure 3 - It's not clear from the above text on page 10 whether event fluences or peak fluxes are being applied. The event list is stated to be of event fluences but the results appear to be peak fluxes. Note that if the correct SAPPHIRE model inputs were used they would sit close to the EVT and CREME results (see Jiggens et al. JSWSC 2018).
p13/line 245 - I am curious regarding the width of the simulation domain. The structure is very deep (10m) which for monte-carlo in 3d and a cosine input would surely mean that it matters how wide it is? It's not clear why such a deep structure if the shielding is only 1/10 of the depth and detectors are relatively thin ((10 mm + 0.5 mm) x 101 = 1.0605m). Please check.
p14/line 250 - Why shielding configuration was used with Shieldose-2q is unclear. For comparison with your set-up it should be a slab shielding and not spherical (which yield far higher doses)
Editorial
p3/line 85 expand i.i.d. - independent and identically distributed
Citation: https://doi.org/10.5194/egusphere-2025-1279-RC1 -
CC1: 'Reply on RC1', Anton Fetzer, 03 Jun 2025
RC1: 'Comment on egusphere-2025-1279', Piers Jiggens, 13 May 2025
This paper has an admirable scope in deriving a theoretical extreme environment and deriving the effects and comparing the results with quiescent environments. I applaud the effort to combine sophisticated statistical approaches with sophisticated particle transport codes. However, there are some shortfalls which need to be addressed before publication.
Reply:
Dear Dr Jiggens,
Thank you very much for your thoughtful and thorough review of our manuscript. We appreciate your insightful comments and replied to each of them one by one in the following. As the open review stage is still ongoing we are still revising the manuscript and will implement the changes based on your recommendations as outlined below in the replies to your comments.
Major Issues
p5/$2.1.3 - a solid mathematical background is given into the statistical approach for EVT. However, most readers will be from the heliophyiscs area. In any case the plots of the Q-Q and overall distribution and extension with the EVT would be essential to give trust in the EVT results.Reply:
We agree that including visual validation of the EVT results is important, especially for readers less familiar with Extreme-Value Theory. We have prepared the requested Q-Q plot and will include it in the revised manuscript to support the validity of the EVT fit.p6/line 165 - Plots of these distributions is essential here and the recurrence of the Carrington event along with overlaid points (at the 3 derived energies) on the fit presented in Figure 1.
Reply:
Additionally to adding the Q-Q plot, we understand the value of also illustrating the Carrington-level recurrence points directly on the spectrum in Figure 1. We will retrieve the corresponding flux values from our EVT analysis at the three derived energies and will update the figure accordingly in the revised manuscript.p7/line 185 - It's unclear if the spectra were derived from the peak of the particle spectra along the orbit at each energy or if the mean value was used. For GEO the models may consider only small changes in L* but for other orbits this will be significant. There are other models (e.g. FLUMIC in the internal charging part of Spenvis) which are designed to characterise the worst case environment and would be better comparisons for your output than mean model results.
Reply:
Thank you for pointing out this need for clarification.
The AE9 spectra shown in Fig. 1 are mission-average fluxes. No peak-flux selection was applied.
As stated in line 181, Figure 1 compares the Carrington peak electron flux with long-term mission average fluxes produced using the AE9 model in mean mode for nominal conditions. Our intention was not to compare with other worst-case models (such as FLUMIC), but to illustrate the magnitude of the Carrington-level flux relative to nominal conditions on commonly used orbits.
This comparison is intended to provide context for the severity of the Carrington scenario by showing that the predicted peak fluxes exceed even the harshest nominal Earth orbits by orders of magnitude. These AE9 average spectra are then used as input for the dose estimates presented in Figure 5a.
There, we show how the fluence received in a few days during a Carrington-level event compares to the radiation damage accumulated over years or even decades of exposure under average orbital conditions, underscoring the event's severity.
To avoid confusion, we have clarified this point in the caption of Figure 1 and the corresponding text in the revised manuscript, highlighting that we are indeed using the AE9 model in mean mode to obtain the long-term average fluxes that also average over different L* values.
We will also add a spectrum for worst-case conditions from FLUMIC to Figure 1 to compare our spectrum not only to nominal conditions.
This will allow readers to better evaluate our Carrington scenario relative to both nominal and extreme case models.p9/ Table 2 - The SAPPHIRE model run is meaningless with the inputs used. The output is a function of confidence level, solar cycle phase and mission duration. It's not clear what mission duration was used nor which cycle phase. 50% confidence will often yield very low flux outputs especially when combined with short mission durations because, as the authors are aware, the SEP processes are highly stochastic. Furthermore, SAPPHIRE gives outputs of 1-in-n year events with n including values of [100,300] which would be perfect for comparison.
Reply:
Thank you for this important observation. We acknowledge your authority as the author of the SAPPHIRE model.
In Table 2, we explicitly stated that we used the “SAPPHIRE (total fluence)” model, not the “worst event fluence” or “1-in-n-year event fluence” modes. The table caption includes the sentence: “The SAPPHIRE model Jiggens et al. (2018) estimates long-term average solar proton and heavy ion fluxes,” to clarify our intended usage.
As with the electron fluxes, we chose to compare the Carrington event spectra to nominal background conditions, rather than other worst-case models. Our goal was to highlight the contrast between Carrington-level conditions and the conditions for which our satellites are designed. The long-term average proton fluxes from the AP9, SAPPHIRE and ISO-15390 models were used to produce Figure 5b in which we compare the ionsing dose due to the proton event fluence with the ionising dose that would accumulate over decades or centuries in nominal LEO and GEO environments.
The model inputs for SAPPHIRE as listed in Table 2 were: “total fluence” mode, “Prediction Period: automatic,” “Offset in solar cycle: automatic,” “Confidence level: 50%,” with default magnetic shielding. The mission duration was set to 30 days, with a launch date of 1 January 2025 in the SPENVIS orbit generator as listed in Table 1.
According to the SAPPHIRE report file on SPENVIS the prediction period is then 0.08 years in solar max and 0 years in solar min. The report file also states the warning “for mission durations < 0.5 year (total solar maximum or solar minimum period), the fluences for 0.5 year are used.”, which is why we divided the reported fluence by 0.5 years to obtain the flux spectra shown in Figure 3.We appreciate the comment pointing out that combining a short mission duration with a 50% confidence level can yield non-representative outputs due to the stochastic nature of SEP events. Based on Figure 17 of your publication “Updated Model of the Solar Energetic Proton Environment in Space” (Jiggens et al. 2018), we note that yearly fluence estimates with 50% confidence level converge for prediction periods exceeding 10 years.
To address our oversight we will revise our use of the SAPPHIRE total fluence model by setting the prediction period override to 11 years to average over a whole solar cycle.
This update will be reflected in Figure 3, and we will also recalculate the associated dose and SEE rate estimates in Figures 5b and 7.We also agree that including SAPPHIRE’s 1-in-n-year event mode (for n = 100 or 300 years) would be highly informative and directly relevant to the Carrington-scale scenario. Therefore, we will include those model outputs as additional curves in Figure 3 to complement the EVT-based Carrington estimates and CREME96 comparisons.
p12/line 215 - Scaling the peak flux to derive the fluence with an assumption of a steady flux of 1.23 days is completely unjustified. Particle fluxes rise and fall by orders of magnitude over this time period and at high energies especially this is likely to generate a very high fluence. If the authors want event fluence they should use the EVT on the list of event fluences and use model outputs of event fluence. Such coarse scaling cannot be justified.
Reply:
We understand and share the concern regarding the validity of our fluence-scaling approach. Particle fluxes indeed rise and fall by orders of magnitude over single storms, let alone, over longer periods. The fluxes are also not spatially homogeneous. Spacecrafts can come in and out of magnetic-local times and drift shells where the local thermodynamical conditions of the plasma and the tail components are significantly different. In an ideal world, we would be able to use radiation belt models that would account for transport and fluxes due to extreme driving conditions. But such models are not currently available (the parameters/inputs that go into radiation belt models to determine fluxes are typically derived from average wave power conditions, even when tabulated for “extreme” driving proxies, i.e. Kp> 6, for instance, Watt et al. showing the impact on pitch-angle diffusion coefficients (https://doi.org/10.3389/fspas.2022.1004634). In this context, our aim is not precision or accuracy, but to seek an estimate of the worst-case scenario under the assumption of constant fluxes. We therefore tried to obtain an upper-bound estimate without invoking a full radiation-belt model, whose applicability under extreme (fat-tailed) wave-power conditions is uncertain and remains to this day an outstanding scientific problem. In Equation 10 of the manuscript, we referred to the Kp = 8 radial-diffusion model of Brautigam & Albert (2000) and expressed the time dependence as an exponential decay with the decay constant Tc = 1.23 cited from Sarma et al. (2020). Integrating this decay over time yields the event fluence F = f0 * Tc. which we refer to as “equivalent to 1.23 days of sustained peak flux.” We acknowledge that this phrasing is misleading, as it implies a constant flux over time, which does not reflect the decay behaviour and actual duration of extreme particle events. We also agree that applying this assumption across all energy ranges may overestimate fluence, especially at high energies where rise and fall times are faster and less predictable.p15/line 280 - Proton-induced SEEs are usually as a result of nuclear interactions and not from direct ionisation. This means that there are dedicated proton cross-sections not as a function of LET but of incident proton energy. Simulating the secondary particles and deriving their LET is physically valid but statistics will may be an issue and possibly the counting of particles locally generated. It's not clear that this is handled efficiently in the simulation. It is possible to use biasing to scale the XS and enhance the probability of interaction, the developers are working on improving the biasing framework in GRAS, it'll come out with GRAS 7, also to make these kind of simulaitons easier. Whether brute force without biasing works really depends on how many other "variables" are in the set up. Given the simulation domain there are serious concerns. It would be better to have used proton cross sections folded with the shielded proton flux at the boundary before the detector. These cross sections handle nuclear interactions and save you from the tyranny of statistics. Note that whilst it's just comparative between environments that can be ok but on p19/line 230 the authors make a statement about absolute risk which is possibly deeply flawed.
Reply:
Thank you for raising this critical point and for the valuable guidance on more efficient and representative methods for assessing proton-induced SEEs.
The main reason why we chose to simulate the LET spectrum behind shielding and fold it with heavy ion SEU cross sections was our assumption that the very high-energy protons would produce a significant number of non-proton secondary particles within the aluminium shielding. We considered it therefore inappropriate to treat the particle environment behind shielding as a pure proton beam and assumed that basing our SEU rate estimate on the LET cross-section curves would properly account for secondary particles like nuclear fragments and knocked-out aluminium nuclei.We appreciate the concern regarding the statistical limitations of Monte Carlo simulations involving rare secondary particle interactions.
We were aware of this problem and tried to achieve sufficient statistics in the following ways.
The very simple geometry of large aluminium slabs on top of silicon slabs was specifically chosen to maximise the number of particles reaching the detector volume while still producing meaningful results. As we point out in line 280 on page 15 for the LET histograms we ran each aluminium thickness in a separate run while the TID simulations were performed with multiple plates in the same run.
All LET histogram simulations were run on the Triton high-performance computing cluster of Aalto University using between 100 and 1000 CPU cores per run, with runtimes of up to 20 hours each. This approach allowed us to simulate up to 1010 primary protons per configuration. Even for the 16mm shielding case, each LET histogram contained at least 10⁵ entries. For the Carrington SEP spectrum specifically, all LET histograms exceeded 10⁹ entries, which explains the small error bars on the Carrington EVT SEU rate estimate curve shown in Figure 7.
We plotted all LET and SEU rate histograms as shown in Figure 6. to verify visually that enough data has been collected. The uncertainties for each LET bin were directly taken from the GRAS output files and properly propagated through the SEU rate estimation to produce the statistical error estimates shown in Figure 7. Based on these results, we were confident that the obtained SEU rates are statistically significant.
In the same document from which we took the LET Weibull parameters, NanoXplore also provides Weibull parameters for the SEU cross section depending on proton energy. To address the comment, we will perform the SEU rate analysis based on the proton test data and shielded proton flux spectra.
While we already state in line 291 on page 16 that our results are not meant to provide absolute estimates we agree that the statement on page 19 lines 320 and following about the absolute risk is not sufficiently justified and possibly misleading. To address the comment we will remove the absolute risk estimate and replace it with a clarification that the reported SEU rates are intended for relative comparison between nominal and extreme space weather scenarios, and should not be used as absolute risk predictions.p20/Figure 8 - It is not clear how the authors are extending the SEP spectrum below 10 MeV, whether the fit presented in Figure 3 is extended down or not. This matters here because the dominant particle population for solar cell damage is below 10 MeV for nominal coverglass thicknesses. It looks like the degradation at low coverless might be (severely) under-predicted.
Reply:
We fully agree with this observation and acknowledge the lack of clarity.
The EVT SEP spectrum was not extended below 10 MeV.
After performing the solar cell degradation simulations with MC-SCREAM we also realised that the dominant particle population for solar cell damage is below 10 MeV for nominal coverglass thicknesses. Consequently, using our EVT spectrum as input for MC-SCREAM results in severe underestimation of solar cell degradation, particularly for thin cover glasses.
Instead of extrapolating the EVT spectrum, we noticed that below 30 MeV the CREME96 GEO Peak 5 min Flux spectrum agrees very well with our EVT spectrum as can be seen in Figure 3. While the spectra diverge at higher energies, the discrepancy is not relevant in this context, since the energies above 100 MeV contribute negligibly to solar cell damage.
As you rightfully point out, the simulation with the EVT spectrum severely underpredicts solar cell degradation, especially for thin cover glasses.
This is why, in lines 370–371 on page 21, we explicitly stated that the EVT spectrum cannot be used for estimating solar cell degradation and recommended using the CREME96 spectrum instead.
To address the comment, we will remove the solar cell degradation curves based on the EVT spectrum from Figure 8 and revise the corresponding text in lines 346–357 of the manuscript. The revised section will clearly explain the limitations of the EVT spectrum and justify using the CREME96 spectrum for the solar cell degradation analysis.Minor Issues
p6/line 150 - Your data is in GEO so those are the direct comparisons. That might be lost on the user in the way it's put.
Reply:
We agree that the current phrasing may not make it sufficiently clear that the EVT analysis was performed using GEO flux data and that the Carrington-level flux estimates derived from this analysis are therefore most directly applicable to GEO. To address this, we will revise the text to explicitly state that the reference fluxes shown in Figure 1 are based on GEO data and that comparisons are made accordingly.p11/Figure 3 - It's not clear from the above text on page 10 whether event fluences or peak fluxes are being applied. The event list is stated to be of event fluences but the results appear to be peak fluxes. Note that if the correct SAPPHIRE model inputs were used they would sit close to the EVT and CREME results (see Jiggens et al. JSWSC 2018).
Reply:
The text in lines 200 and following on page 10 states that we used “ the annual integral solar proton fluences for 1984-2019 (Raukunen, O. et al., 2022)”. This dataset lists the integral solar proton fluence for each year between 1984 and 2019, which means it is neither event fluences nor peak fluxes.
To clarify this, we will expand the paragraph following line 200 on page 10 and add the explicit reference to Table D.1. on page 14 of Raukunen, O. et al. (2022) (https://doi.org/10.1051/0004-6361/202243736)
We acknowledge your note regarding the placement of the SAPPHIRE results and we will add the 1-in-100-year SAPPHIRE spectrum as discussed previously for proper comparison with EVT and CREME96 results in the revised Figure 3.p13/line 245 - I am curious regarding the width of the simulation domain. The structure is very deep (10m) which for monte-carlo in 3d and a cosine input would surely mean that it matters how wide it is? It's not clear why such a deep structure if the shielding is only 1/10 of the depth and detectors are relatively thin ((10 mm + 0.5 mm) x 101 = 1.0605m). Please check.
Reply:
We apologise for this misunderstanding. It seems we have not explained the geometry sufficiently.
In line 246 we state that the “edge length of the square tiles was set to 10 m, while the thickness of the aluminium shields is up to 10 mm.”
This means that the structure is extremely shallow in depth (maximum 10.5 mm) compared to the lateral dimensions of (10 m × 10 m) of each of the 101 tiles. The whole setup is therefore 10 m tall, 101 * 10 m wide and only 10.5 mm deep at the thickest tile.In line 247 we wrote: “This high width-to-depth ratio was chosen to minimise the influence of edge effects.”
The “10 mm” mentioned refer specifically to the thickness of the thickest shielding configuration. To simulate different shielding depths, we varied the aluminium thickness using the formula x × 0.1 mm for x in (0, 100), resulting in 101 tiles with thicknesses ranging from 0 to 10 mm in 0.1 mm increments. This range is reflected on the x-axes of Figure 5.We acknowledge that the description of the geometry could have been clearer.
In the revised manuscript, we will rephrase the description of the geometry in lines 246 and following as well as the caption of Figure 4 to better explain the geometry and to prevent future misunderstandings.p14/line 250 - Why shielding configuration was used with Shieldose-2q is unclear. For comparison with your set-up it should be a slab shielding and not spherical (which yield far higher doses)
Reply:
In our manuscript, we did not mention which shielding configuration was used in SHIELDOSE-2Q. We confirm that we used the “finite Al slab shields” geometry setting to ensure consistency with our Geant4/GRAS simulation setup. We agree that using spherical geometry would have resulted in significantly higher dose estimates and would not have been appropriate for comparison. We will clarify this point in the revised manuscript text and explicitly state that the “finite Al slab shields” geometry was used in SHIELDOSE-2Q.
Editorialp3/line 85 expand i.i.d. - independent and identically distributed
Reply:
We will write out "independent and identically distributed" at line 85 to ensure clarity for all readers.We are grateful for your thorough and constructive feedback. We will incorporate all of the changes outlined above, together with changes addressing the second reviewer's comments and will submit the revised manuscript at the end of the open-review period for further consideration for publication in the Annales Geophysicae journal.
With best regards,
Anton Fetzer on behalf of all co-authorsCitation: https://doi.org/10.5194/egusphere-2025-1279-CC1 -
AC3: 'Reply on CC1', Anton Fetzer, 02 Sep 2025
Please see "CC1: 'Reply on RC1', Anton Fetzer, 03 Jun 2025" for our author comment to RC1: 'Comment on egusphere-2025-1279', Piers Jiggens, 13 May 2025.
Citation: https://doi.org/10.5194/egusphere-2025-1279-AC3
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AC3: 'Reply on CC1', Anton Fetzer, 02 Sep 2025
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AC2: 'Reply on RC1', Anton Fetzer, 26 Aug 2025
Please see "CC1: 'Reply on RC1', Anton Fetzer, 03 Jun 2025" for our author comment.
Citation: https://doi.org/10.5194/egusphere-2025-1279-AC2
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CC1: 'Reply on RC1', Anton Fetzer, 03 Jun 2025
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RC2: 'Comment on egusphere-2025-1279', Anonymous Referee #2, 27 Jun 2025
The paper addresses the potential impact of an extreme space weather event on satellites in terms of dose and SEE. It applies EVT to extrapolate from measured environments and then assesses the effects using engineering tools. Some comparison to 'normal' conditions is then made. A range of assumption are used in the process. The authors should be commended on aiming to cross the boundary between space weather environments and their engineering effects - they address a vital question that does need answering, even if it is difficult to do. There are however some aspect of the paper which need revision to make it fully convincing.
19 The Carrington event was not necessarily extreme in all respects -we don't know if there was an SEPE associated with it as there is no evidence. And we don't know if the electron belts were like in 1859. We just know it was biggest geomagnetic event. Overall I think you should make clear that you refer to a rare (1 in 100 yr?) event by using the phrase Carrington, or even drop i the name.
Section 1 - the background section seems to be missing quite a range of prior work which is highly pertinent and should be covered . For example there have been further publications on the worst case environments which could be relevant (e.g. Hapgood et al, Space Weather, 19, e2020SW002593. https://doi.org/10.1029/2020SW002593) here: at least they should be considered. Also there have been some assessments before such as Cannon et al (Roayl Academy of Engineering): https://raeng.org.uk/media/2iclimo5/space_weather_summary_report.pdf.
Furthermore there have been a number of EVT approaches to the extreme space weather event - for example for GEO >2MeV electron see Meredith et al https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014SW001143 and same author has done same for MEO and LEO. How does your work fit in with these and/or differ?
37. I would not recommend to say the main effects are dos and SEE these as there is evidence that charging effects are overall the most frequent and damaging.
165. Inner belt electrons are excluded?
180/Fig.1 Carrington flux is for GEO here I think - we expect other orbits to be very different so what is reason to plot together? Also AE9 has statistical model which can be used for extreme event - did you look at that as would seem logical. It would be good to plot the extreme values other authors and models produce for GEO rather than trying to compare to other orbits.
Table 1 - not sure how you use the VABP model - are you reportign maxima round orbit - please explain.
180 / Fig. 1 GEO flux varies with longitude -is this plot for a specific longitude?
190 The duration of the fluence seems short for a potentially extreme event. How dos this compare to other authors and can you apply EVT to this aspect.
Fig. 3 Sapphire result is for interplanetary space, GEO etc??
205-213 - not sure what is benefit of comparing extreme GEO/interplanetary to average values in other orbits? Please explain. We expect other orbits to have different environments anyway so what it the message you want to give?
217 - the assumption of duration being same as for electrons is not convincing as they are totally different phenomena. We have many non-extreme events that lasted longer.
222 - 'main' effects - see earlier comment.
Fig. 5 - why horizontal bars ?- there is no uncertainly in the shielding depths.
255 etc - can you address why the average models doesn't naturally include the occasional extreme event: is the 1 in 100 year event necessarily 'on top'?
265-275 This part seems very weak with on a broad extrapolation and unstated assumptions about LEO sats. Even if outer belt is very enhanced then LEO sats only experience it briefly. I think you need to simulate this to reach firm conclusions.
296/Fig. 6 Do these plots really help the reader -please explain what they add to the main argument? Can they be in appendix? Upset rate seems (at first sight) low for extreme event but I don't have tome to calculate myself..
311 Not sure where in Van Allen belts you refer to (its a big region!).
315-325 - Again the LEO impact seems weak and unconvincing - there is no analysis really - it might be better to focus on GEO than attempt to deal with LEO too.
327 You make a strong contrast with LEO here but don't see the evidence as mentioned above. Best to keep conclusion limited to where evidence is clear.
Citation: https://doi.org/10.5194/egusphere-2025-1279-RC2 -
AC1: 'Reply on RC2', Anton Fetzer, 26 Aug 2025
RC2: 'Comment on egusphere-2025-1279', Anonymous Referee #2, 27 Jun 2025
The paper addresses the potential impact of an extreme space weather event on satellites in terms of dose and SEE. It applies EVT to extrapolate from measured environments and then assesses the effects using engineering tools. Some comparison to 'normal' conditions is then made. A range of assumptions is used in the process. The authors should be commended for aiming to cross the boundary between space weather environments and their engineering effects - they address a vital question that does need answering, even if it is difficult to do. There are, however, some aspects of the paper which need revision to make it fully convincing.
Reply:
We thank the referee for carefully reading our manuscript and for recognising our effort to bridge the gap between space weather environments and their engineering consequences. We appreciate your detailed analysis and constructive suggestions.
Below, we address each of your points in the order raised and outline how we plan to implement the necessary changes in our revised manuscript.
19 The Carrington event was not necessarily extreme in all respects - we don't know if there was an SEPE associated with it, as there is no evidence. And we don't know if the electron belts were like in 1859. We just know it was the biggest geomagnetic event. Overall, I think you should make clear that you refer to a rare (1 in 100 yr?) event by using the phrase Carrington, or even drop the name.
Reply:
Thank you for pointing out the need for clarification regarding the term "Carrington event”.
We agree that the geomagnetic event of 1859 cannot be used as a reference space weather scenario, especially given the lack of direct observational evidence for an associated SEP event and the unknown electron belt conditions at the time.
Our aim is not to reconstruct 1859, but to determine if our satellite-based infrastructure would survive an upper-bound worst-case space weather scenario. We refer to this type of rare extreme events as "Carrington-level” throughout the paper, but indeed mean 1-in-100-year to 1-in-150-year recurrence.
To address your comment, we will revise the manuscript to clarify that we estimate the satellite impact of a potential future extreme space weather event based on EVT extrapolations of measured contemporary data. We will explain that we refer to an extreme hypothetical scenario with a recurrence period of approximately 100 to 150 years.
Section 1 - the background section seems to be missing quite a range of prior work, which is highly pertinent and should be covered. For example, there have been further publications on the worst case environments which could be relevant (e.g. Hapgood et al, Space Weather, 19, e2020SW002593. https://doi.org/10.1029/2020SW002593) here: at least they should be considered. Also, there have been some assessments before, such as Cannon et al (Royal Academy of Engineering): https://raeng.org.uk/media/2iclimo5/space_weather_summary_report.pdf.
Reply:
We appreciate your concern about missing prior work and fully agree that both Hapgood et al. (2021) and the Royal Academy of Engineering report (Cannon et al., 2013) are highly pertinent to our work.
We will add a paragraph to our introduction, summarising the methods and main conclusions of these publications to place our work in the appropriate context.Furthermore, there have been a number of EVT approaches to the extreme space weather event - for example, for GEO >2 MeV electrons see Meredith et al https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014SW001143, and the same author has done the same for MEO and LEO. How does your work fit in with these and/or differ?
Reply:
Thank you for recommending the highly relevant series of EVT-based studies by Meredith et al.
We share the common approach of using EVT for quantifying worst-case particle fluxes. But in our case, the flux estimates are not the main result, as we focus on the engineering consequences of the events based on particle-transport simulations. We therefore cannot dedicate as large a share of our publication to comparisons with other models and estimates as would be expected in the space environment community.
We will explicitly acknowledge Meredith et al.’s previous work on EVT analysis for particle fluxes in our introduction. In our discussion and conclusions section, we will add a brief comparison of our methods and results with theirs. Based on our initial reading, we conclude that their estimates for GEO are consistent with ours.
- I would not recommend saying the main effects are dose and SEE, as there is evidence that charging effects are overall the most frequent and damaging.
Reply:
We agree with the reviewer that charging effects are likely the most frequent and damaging cause of satellite anomalies, as reported for example by Wrenn (1995, https://doi.org/10.2514/3.26645), Koons et al. (2000, https://articles.adsabs.harvard.edu/pdf/1998sct..conf....7K), Fennell et al. (2001, https://apps.dtic.mil/sti/html/tr/ADA394826/), Lai, S. T. (2011) in Spacecraft Charging (AIAA), and most recently Horne et al. (2025, https://doi.org/10.1029/2024SW004226) among others.
In lines 37 and following, we stated that "the main impact on satellite technology in Earth’s radiation environment” is due to relativistic RB electrons and solar energetic protons, but here we do not specify if these are dose effects or based on charging.
In line 43, we then cite Hands et al. (2018, https://doi.org/10.1029/2018SW001913) that "two key radiation effects are total ionising dose and displacement damage” and "Cumulative effects such as total ionising dose (TID) and displacement damage are also caused by the populations of trapped energetic electrons that form the Van Allen belts.” We acknowledge that we missed the keyword "cumulative” in the citation in line 43 and will include it there in the revised manuscript.
According to Hapgood et al. (2021, https://doi.org/10.1029/2020SW002593), only energies up to "some 10s of keV" contribute to surface charging. Our spectra do not cover this range, which is why we cannot comment on surface charging, but we will include this in the manuscript.
We did not investigate deep dielectric charging, as we lack expertise in this area, and including a sufficiently detailed analysis at this point would further increase the length and complexity of our manuscript. We will acknowledge this limitation and will highlight the importance of charging effects in the revised manuscript. We will cite the relevant literature, noting that our results focus on cumulative dose and single-event effects rather than charging.
- Inner belt electrons are excluded?
Reply:
We appreciate your concern about the omission of inner belt electrons.
For our electron satellite impact analysis, we focus on MeV-range electrons that contribute to the ionising dose in satellites. These are more common in the outer belt than in the inner belt.
For example, Fennell et al. (2015, https://doi.org/10.1002/2014GL062874) report a lack of MeV electrons in the inner belt, while data from the Van Allen Probes indicate that >2 MeV electrons do not penetrate below L ≈ 2.8 (Baker et al., 2014, https://doi.org/10.1038/nature13956). In 2024, the Colorado Inner Radiation Belt Experiment (CIRBE) observed a temporary energetic electron population in the L = 2.5-3.5 range, which is "usually devoid of relativistic electrons” (Li et al., 2025, https://doi.org/10.1029/2024JA033504).
In line 342, we cite Tsurutani and Lakhina (2014, https://doi.org/10.1002/2013GL058825), who discuss magnetospheric compression during extreme events, leading to outer belt electrons reaching satellites on orbits that would be protected during quiet conditions.
We therefore used outer belt electrons as an upper-bound, worst-case scenario for all Earth orbits to estimate the loss of satellite-based infrastructure due to total ionising dose. We will clarify this in Section 2.2.1.
With the future availability of new multi-year datasets, such as those from CIRBE, a similar analysis could be performed using electron fluxes measured in the inner belts.
180/Fig.1 Carrington flux is for GEO here, I think - we expect other orbits to be very different, so what is the reason to plot together? Also, AE9 has a statistical model which can be used for extreme events - did you look at that, as would seem logical? It would be good to plot the extreme values that other authors and models produced for GEO rather than trying to compare to other orbits.
Reply:
Figure 1 introduces the electron spectra used as inputs for the particle transport simulations. The resulting TID values shown in Figure 5a are then used to estimate whether space-based infrastructure could survive the TID of an upper-limit worst-case event fluence, regardless of orbit.
We confirm that our EVT analysis of Carrington-scale RB electron fluxes is based on the GEO dataset provided by Borovsky and Yakymenko (2017, https://doi.org/10.1002/2017JA024250), as stated in lines 148 and following.
We use this electron EVT spectrum as an orbit-agnostic upper bound. The comparison with average conditions on other orbits was chosen for practical policy considerations and to communicate the severity of the radiation threat to decision-makers, not as a physical prediction.
We acknowledge this likely overestimates fluxes on lower orbits (see Fennell et al. (2015, https://doi.org/10.1002/2014GL062874) and Baker et al. (2014, https://doi.org/10.1038/nature13956)).
Thank you for pointing out the statistical features of the AE9 model. We were aware of the percentile mode, but we chose AE9’s "mean mode" to represent the long-term average environment satellites typically experience and are designed for, rather than extreme-case outputs.
If our goal were to provide and validate a state-of-the-art predictive particle flux model for GEO, we would include detailed comparisons with extreme-case GEO predictions from AE9 and other models.
While we fully agree that this would be scientifically justified and appreciated by the space physics community, adding such a comparison at this point would exceed the scope of the paper.
We appreciate your constructive suggestion and hope this clarifies our choice of methods.
Table 1 - not sure how you use the VABP model - are you reporting maxima around the orbit? Please explain.
Reply:
We are not reporting maxima around the orbit.
We apologise for using an incorrect abbreviation for the Van Allen Probes (VAP), formerly known as the Radiation Belt Storm Probes (RBSP). We will use the correct abbreviation (VAP) in the revised manuscript.
In our manuscript, VABP simply referred to the orbit of the Van Allen (Belt) Probes. We used the orbit parameters listed in Table 1 as an extreme example orbit for which satellites have been designed.
In the caption of Table 1, we state, "the perigee, apogee and inclination of the Van Allen Probes were used to construct a realistic worst-case reference for nominal conditions in Earth orbit.”
The orbit parameters listed in Table 1 were entered into the SPENVIS orbit generator to create 30-day time series of latitude, longitude, and altitude for the three different orbits that were then used as input for the radiation environment models AE9/AP9, SAPPHIRE, ISO-15390, and CREME-96. These models were then used to calculate long-term mission-average fluxes along the specified trajectory.
To clarify this point in the revised manuscript, we will explicitly state in the caption of Table 1 and the accompanying text that VAP refers to the orbit parameters used to generate the trajectory for the flux models to generate long-term average spectra along an example orbit during nominal conditions.
180 / Fig. 1 GEO flux varies with longitude -is this plot for a specific longitude?
Reply:
Thank you for pointing out the longitude dependence of GEO fluxes.
The GEO electron flux shown in Figure 1 was generated using the orbit parameters listed in Table 1, which include 0° longitude for the GEO. This is the default setting of the SPENVIS geostationary orbit generator. We did not investigate or discuss this parameter because we assumed the longitude dependence of GEO fluxes not to be significant for our comparison between nominal and worst-case scenario estimates.
Meredith et al. (2015, https://doi.org/10.1002/2014SW001143) report that "the daily averaged flux measured at GOES West is typically a factor of about 2.5 higher than that measured at GOES East”, despite both satellites being on GEO, but at 135° and 75° W, respectively.
AE9 produces different outputs for different GEO longitudes. To quantify the spread, we ran AE9 v1.5 in mean mode for the four GEO longitudes 0°, 90°, 180°, and 270°. The highest variation in the differential flux output was at 3 MeV, with the flux at 180° being 46% higher than the flux at 270°.
This difference is significantly smaller than the 2.5 times factor reported by Meredith et al. (2015) and much smaller than the enhancement during an extreme event. Therefore, the specific longitude for which GEO fluxes are predicted with AE9 does not change our conclusions regarding the relative increase in doses and SEE rates during an extreme event.
To address the issue, we will include a statement in both the caption of Figure 1 and in the corresponding manuscript text, clarifying that the GEO reference spectrum shown was generated for 0° longitude, while other GEOs experience different long-term average fluxes within the same order of magnitude as reported by Meredith et al. (2015).
190 The duration of the fluence seems short for a potentially extreme event. How does this compare to other authors, and can you apply EVT to this aspect?
Reply:
We appreciate your concern about the event duration.
We state in lines 190-192 that we used a simple exponential decay with the flux decay constant T_c = 1.23 d cited from Sarma et al. (2020, https://doi.org/10.1029/2019JA027618). Integrating the exponential over time (Equations 11, 12, and 13) results in a fluence that is equivalent to 1.23 days of sustained peak flux.
We acknowledge that this might be misread as an event duration of only 1.23 days, which is not the case.As shown in Figure 2, the flux peak is followed by several days of elevated but decreasing fluxes. About half of the total event fluence occurs in the first 24 hours, with most of the remaining fluence spread over the following three days.
Durations of extreme particle events reported in the literature vary significantly. For instance, Mourenas et al. (2022, https://doi.org/10.1029/2022JA030661) discuss long-duration events during which electron fluxes stay elevated for ten or more days. Meredith et al. (2023, https://doi.org/10.1029/2023SW003436) state that on GPS orbits, "the fluxes are characterized by relatively rapid increases followed by gradual decays lasting many days”. Meredith et al. (2024, https://doi.org/10.1029/2024SW004042) provide the full-width half-maximum durations of fifty flux enhancement events on GPS orbit ranging from two to more than ten days.
We agree with the reviewer that applying EVT to estimate event durations is a compelling idea. However, this would require a statistically robust dataset of measured event durations, which was not available to us when the presented work was performed. Also, due to time constraints of the project, we opted for a simple exponential decay based on the empirically derived decay constant by Sarma et al.
We acknowledge this as a limitation of our work and will explicitly clarify it in the revised manuscript with a brief discussion of the uncertainty introduced by event duration, referencing existing literature.
Fig. 3 Sapphire result is for interplanetary space, GEO, etc??
Reply:
We apologise for the ambiguity. We did not consider interplanetary space because this paper focuses on space-based infrastructure.
The grey dashed spectrum labelled "SAPPHIRE GEO Solar Proton Flux” in Figure 3 was generated using the SAPPHIRE model on SPENVIS with the model parameters listed in Table 2. This includes the default magnetic shielding along the spacecraft trajectory using the GEO orbit parameters specified in Table 1.
The caption of Figure 3 states: "solar proton flux predicted by the SAPPHIRE model for GEO.”
We also generated SAPPHIRE proton spectra for the LEO and MEO orbits specified in Table 1, but we did not include the curves in this plot because they were lower than the corresponding AP9 trapped proton spectra over the whole energy range.
205-213 - Not sure what is benefit of comparing extreme GEO/interplanetary to average values in other orbits? Please explain. We expect other orbits to have different environments anyway, so what is the message you want to give?
Reply:
The primary motivation for comparing the worst-case scenario with average conditions at LEO, MEO, and GEO is to determine an upper limit for how far an extreme space weather event could exceed the doses and SEE rates under which satellites usually operate.
Figure 3 shows the particle spectra used as inputs for the satellite impact simulations, with results presented in Figures 5, 7, and 8.
We fully agree with the reviewer that different orbits experience different radiation environments. As a simplifying and deliberately conservative upper limit, we assumed the same extreme fluxes reaching all satellites regardless of orbit. We acknowledge this likely overestimates fluxes for LEO satellites. Our main conclusion is that despite these extreme assumptions, the TID, SEU rates, and solar cell damage are survivable by satellites designed for reliability. Assuming more benign environments for lower orbits would not affect this result.
In the revised manuscript, we will add further clarification to the caption of Figure 3 and the paragraph following line 205 that the cross-orbit comparison is to provide context for the conditions for which satellites are designed, while the impact analysis is orbit agnostic.
217 - The assumption of duration being the same as for electrons is not convincing, as they are totally different phenomena. We have many non-extreme events that lasted longer.
Reply:
We understand your concern about our use of the same 1.23-day decay constant for SEP as for outer belt electrons.
We agree that RB electron flux enhancements and SEP events are distinct phenomena, and that SEP events can persist for longer periods.
Firoz et al. (2022, https://doi.org/10.1007/s11207-022-01994-7) report that SEPs lasting for up to one week have been observed on GEO, but state mean event durations between two and four days. SEP event durations observed by PAMELA on LEO show a similar distribution (Bruno et al. 2018, https://doi.org/10.3847/1538-4357/aacc26.
This means our 1.23-day decay assumption is consistent with observations, even though the event durations of RB electrons and SEPs are determined by different physics.
We do not have estimates of the duration over which SEP remain magnetically confined by the Earth’s radiation belts, nor the current capacity at this point to estimate that quantitatively. For the sake of comparison, we assume that satellites interact with energetic protons on comparable timescales, while noting that their large Larmor radius would lower their magnetic confinement.
We will cite Firoz et al. (2022) and Bruno et al. (2018) in Section 2.3.2 to base our choice of decay constant on measured data, while clarifying that RB electrons and SEP follow different physics.
222 - 'main' effects - see earlier comment.
Reply:
Thank you for pointing out this sentence.
As we discussed in our response to your comment concerning line 37, we agree that charging effects are likely the most frequent and damaging cause of satellite anomalies.
We will change this paragraph to acknowledge charging effects and to clarify that our analysis focused on cumulative dose and single-event effects.
Fig. 5 - Why horizontal bars ?- There is no uncertainty in the shielding depths.
Reply:
We confirm that there is no uncertainty in the simulated shielding depths.
What appear to be horizontal bars are the end caps of the vertical error bars, which represent the statistical uncertainty of the Monte Carlo simulation. The legend shows the used error-bar symbol. We performed the simulations with more than 10 billion particles each to reduce the statistical uncertainty in ionising dose reported by the GRAS software to less than ±10%. For most configurations, we achieved less than ±1% statistical uncertainty. With the logarithmic Y-scale ranging over several orders of magnitude, these error bars appear so small that the top and bottom cross bars overlap.
We will remove the horizontal end caps of the error bars to reduce this ambiguity, and we will clarify in the caption of Figure 5 that the error bars show the dose uncertainty due to the statistical nature of the Monte Carlo particle transport simulation, while the simulated shielding depth is exact.255 etc - can you address why the average models doesn't naturally include the occasional extreme event: is the 1 in 100 year event necessarily 'on top'?
Reply:
Thank you for raising this important point about how statistical environment models treat rare, short-duration extremes.
Space environment models are based on datasets that span less than half a century (for example, Ginet et al. (2013, https://doi.org/10.1007/s11214-013-9964-y), Jiggens et al. (2018, https://doi.org/10.1109/TNS.2017.2786581)) while no 1-in-100-year event has occurred during the space age. This means such extreme events are entirely outside the data and can only be inferred from the data using extrapolation (e.g. EVT).
Even if such extremes outside the underlying dataset are modelled appropriately, the duration of a 1-in-100-year event is likely less than two weeks, which means it contributes with a duty cycle of less than 0.04% to the long-term average. A 1-in-100-year event would have to exceed the average flux by more than a factor of 5000 continuously for a whole week to double the long-term average flux. Therefore, even if the rare extreme flux is fully accounted for in the mean flux model outputs, centennial extreme events likely contribute less than 50% to the long-term mean.
Considering that the extreme peak flux exceeds the mean fluxes by several orders of magnitude, the question of whether the mean models contain 1-in-100-year events does not affect our conclusions.
The probability of large fluxes or fluences seems to follow "fat-tailed” distributions. For example, Ruzmaikin et al. (2011, https://doi.org/10.1016/j.jastp.2009.12.016) report a power-law tail for the distribution of 9–60 MeV SEP fluences, while Jiggens et al. (2009, https://doi.org/10.1029/2009JA014291) find that the waiting times between SEP events are described by Lévy-stable (fat-tailed) or time-dependent Poisson distributions.
Recent work by Osmane et al. (2022, https://doi.org/10.5194/angeo-40-37-2022) supports that electron fluxes, driven by ULF waves, in the radiation belts, are characterised by non-Gaussian, heavy-tailed statistics.
We therefore conclude that standard mean-based models can systematically underestimate the likelihood of centennial-scale events and their consequences, while the difference between the mean flux and the extreme flux does not strongly depend on whether the mean flux already accounts for rare extreme events or not.
We will add a statement to Section 2.3.1 clarifying that extreme events do not dominate long-term average fluxes due to their short duration and long recurrence time. We will also refer to evidence in the literature that SEP and relativistic-electron flux statistics are "fat-tailed”, indicating that extreme events are systematically underestimated by mean models.
265-275 This part seems very weak with a broad extrapolation and unstated assumptions about LEO sats. Even if the outer belt is very enhanced, then LEO sats only experience it briefly. I think you need to simulate this to reach firm conclusions.
Reply:
We agree that the paragraph spanning lines 265-275 draws only broad conclusions, because our data does not allow for a detailed, orbit-specific analysis.
Only two assumptions about satellites were used:
1. TID tolerance of satellite electronics (~100krad): stated explicitly in line 260.
2. More than 1 mm of aluminium-equivalent shielding: implied by lines 260-263.As stated in line 266, "applying EVT to geostationary fluxes only provides values at geostationary orbits”. We used the assumption of GEO electron fluxes reaching all orbits as a bounding upper limit for our worst-case scenario. Even in this unlikely extreme scenario, satellites with >1 mm aluminium-equivalent shielding and radiation-hard electronics would survive the deposited TID shown in Figure 5. From this, we conclude that the event TID would be survivable by satellites using radiation-hard electronics and more than 1 mm aluminium-equivalent shielding, partially answering our main question.
For a more detailed estimate of the duration over which LEO satellites would experience the enhanced fluxes compared to GEO, we have to refer to other work, as our data and model cannot provide this answer.
To address the comment, we will:
- Rephrase the paragraph to explicitly state the assumed shielding thickness for different satellite classes.
- Move the statement that EVT results are GEO-specific to Section 2.2, correcting the incorrect section reference.
- Explicitly refer to Tsurutani and Lakhina (2014, https://doi.org/10.1002/2013GL058825) in Section 2.2.2.
- Rephrase the paragraph following line 266 to clarify that the assumption of extreme fluxes reaching all orbits is purely a bounding upper limit, not a physical prediction.296/Fig. 6 Do these plots really help the reader -please explain what they add to the main argument? Can they be in the appendix? Upset rate seems (at first sight) low for an extreme event, but I don't have time to calculate myself..
Reply:
Thank you for the question. We believe Figure 6 belongs in the main text to make our upset rate calculation reproducible and auditable, while visually explaining our method in a concise way.Panel (a) shows a representative LET histogram behind shielding, including both protons and secondary knock-on ions. Panel (b) shows the same histogram with the LET bins scaled for their contribution to the upset rate.
We overlay the cross-section curve provided by NanoXplore for their radiation-hard FPGA to show which parts of the LET spectrum contribute to the upset rate estimate. This allows the reader to evaluate the quality of our LET and cross-section data, which are the basis for the results in Figure 7.The upset rates might seem low because we used the cross-section curve of a specifically radiation-hard FPGA that is likely to be used in future ESA missions and high-reliability applications in space.
Our focus is on the relative increase in upset rates between nominal and extreme conditions, not on the absolute numbers. We conclude that the upset rates due to our worst-case flux estimate are on the same order of magnitude as the rates expected on the orbit of the Van Allen Probes during normal conditions. This proves that the upset rates during the event are survivable by satellites that are designed for high reliability, which means at least some satellite services could be maintained even in our worst-case scenario.
311 Not sure where in Van Allen belts you refer to (it's a big region!).
Reply:
We used the trajectory of the Van Allen Probes with 618 km perigee, 30414 km apogee, and 10.2° inclination as specified in Table 1.
The nominal particle spectra for this orbit were generated using AE9, AP9, SAPPHIRE, and ISO-15390 with the model parameters in Table 2.
When we refer to the conditions in the Van Allen belts, we mean along the orbit of the Van Allen Probes. To remove this ambiguity, we will revise the caption of Figure 7 and the abstract by replacing "in the Van Allen belts” with "on the Van Allen Probes orbit.”
315-325 - Again, the LEO impact seems weak and unconvincing - there is no analysis really - it might be better to focus on GEO than attempt to deal with LEO too.
Reply:
Thank you for highlighting the need for clarification. We do not attempt a quantitative, orbit-specific analysis for LEO in this study. Instead, we present an orbit-agnostic satellite survivability estimate for national risk assessment. We aim to determine if some satellite services could be maintained if an extreme upper-bound particle spectrum reached all satellites. The presented orbit-specific data is only for the nominal conditions for which satellites are designed, and against which we compare our global extreme scenario.
Even under this extreme and simplified assumption, the expected SEU rates would not be catastrophic for a satellite designed for high reliability, as stated in line 320. Based on this, we answer our main question and conclude that at least some satellites would survive the single-event upsets due to a worst-case scenario.
To address the issue, we will rephrase the paragraphs following line 309 to remove any ambiguity about orbit-specific quantitative results and will add a statement clarifying that the analysis presented in this section is purely to determine if some satellites could survive the worst-case single-event upset rates, regardless of orbit.
327 You make a strong contrast with LEO here, but don't see the evidence as mentioned above. Best to keep the conclusion limited to where the evidence is clear.
Reply:
We agree that this section also gives the impression of an orbit-specific assessment that is not supported by the data. As noted in our reply to your previous comment, our conclusions are intended to be orbit-independent.
We will rewrite Section 4 (Discussion and conclusions) to remove this ambiguity and clearly state that the identified worst-case TID, SEU rates, and solar cell degradations are survivable by satellites designed for reliability. We conclude that national infrastructure does not need to prepare for the total loss of space-based services, regardless of orbit.
We thank the reviewer for their careful and constructive review. We hope our reply sufficiently clarifies the scope and goal of our work and provides context for further consideration. We will implement the changes and look forward to your comments on our revised manuscript.
With best regards on behalf of the authors,
Anton FetzerCitation: https://doi.org/10.5194/egusphere-2025-1279-AC1
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AC1: 'Reply on RC2', Anton Fetzer, 26 Aug 2025
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- 1
Mikko Savola
Adnane Osmane
Vili-Arttu Ketola
Philipp Oleynik
Minna Palmroth
Extreme events can pose serious risks to satellites, potentially disrupting communication, navigation, and power systems. Our study estimates the worst-case radiation levels during such an event and assesses their impact on electronics and solar panels.
Extreme events can pose serious risks to satellites, potentially disrupting communication,...