Comments on “Long‐Term Variations of Exospheric Temperature Inferred From foF1 Observations: A Comparison to ISR T<sub><i>i</i></sub> Trend Estimates” by Perrone and Mikhailov

Zhang, Shun‐Rong; Holt, J. M.; Erickson, P. J.; Гончаренко, Л. П.

doi:10.1029/2017ja024948

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…The values shown in supporting information Figure S14 agree with the ion temperature increase calculated by Zhang et al () in the case of positive ion composition errors ( ℇ p > 0). Nevertheless, negative ion composition estimation errors obtained larger ion temperature errors.…”

Section: Resultssupporting

confidence: 87%

“…Different combinations of molecular and atomic ion compositions and temperatures generate similar values of

\sqrt{T_{i} / m_{i}}

, hindering the correct discrimination of signals (Aponte et al, ). This effect is currently a relevant issue under discussion that affects the determination of long‐term trends of ionospheric and thermospheric variables (Perrone & Mikhailov, ; Perrone & Mikhailov, ; Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Martínez-Ledesma

Quezada

2019

JGR Space Physics

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Unambiguously estimating the plasma parameters of the ionosphere at altitudes between 130 and 300 km presents a problem for the incoherent scatter radar (ISR). At these ranges, ISR is unable to distinguish between different mixtures of molecular ions (NO + and O 2 + ) and atomic oxygen ions (O + ). Common solutions to this problem are either to employ empirical or theoretical models of the ionosphere or to add a priori known plasma parameter information obtained from the plasma line of the ISR spectrum. Studies have demonstrated that plasma parameters can be unambiguously estimated in almost noiseless scenarios, not commonly feasible during routine monitoring. In this study, we define a theoretical framework to quantify the ambiguity problem and determine the maximum signal fluctuation levels of the ISR signal to unambiguously estimate plasma parameters. We conduct Monte Carlo simulations for different plasma parameters to evaluate the estimation performance of the most commonly used nonlinear least squares optimization algorithm. Results are shown as probability curves of valid convergence and correct estimation. We use simulations to quantify the estimation error when using ionospheric models as initial conditions of the optimization algorithm. We also determine the contribution to the estimation process of different combinations of parameters known from the plasma line, the particular contribution of each plasma parameter, and the effect of increasing the level of uncertainty of the parameters known a priori. Results suggest that knowing a priori both electron density and electron temperature parameters allows an unambiguous estimation even at high fluctuation levels.

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Section: Resultssupporting

confidence: 87%

“…Different combinations of molecular and atomic ion compositions and temperatures generate similar values of

\sqrt{T_{i} / m_{i}}

Section: Introductionmentioning

confidence: 99%

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Martínez-Ledesma

Quezada

2019

JGR Space Physics

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“…(2018) pointed out that their approach, developed in the authors’ earlier papers (also similar to what is used in MP20), is questionable; however, the reply by Perrone and Mikhailov (2018) did not seem to have addressed some of the key issues raised in the Zhang et al. (2018) commentary. For example, the ambiguity/uncertainty among the derived parameters is not quantified.…”

Section: The Methodology Used In Mp20 Is Questionable and In Fact Incorrect And The Results Lack Of Proper Uncertainty Estimate And Verifmentioning

confidence: 99%

Comments on “Poststorm Thermospheric NO Overcooling?” by Mikhailov and Perrone (2020)

et al. 2021

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Recently Mikhailov and Perrone (2020; hereafter MP20) proposed the mechanism of neutral atomic oxygen reduction to explain the density decrease during the storm recovery phase, and claimed that there is no need to introduce the storm-time overcooling concept. Unfortunately, their methodology, model applicability and hypothesis all have problems, resulting in conclusions that are not meaningful. The reasons are given as follows. The MethodologyUsed in MP20 is Questionable, and in Fact, Incorrect, and the Results Lack of Proper Uncertainty Estimate and Verification MP20 determined simultaneously several thermospheric parameters including [O], [O 2 ], [N 2 ], exospheric temperature Tex and ion vertical drifts by matching the calculated ionospheric parameters using a 1-D ionospheric model with ionosonde observations during the stormtime. The only ionosonde parameters used are F 2 and F 1 peak electron densities (foF2 and foF1). These results arrive with ambiguity and inadequate uncertainty estimate. In a commentary paper, Zhang et al. ( 2018) pointed out that their approach, developed in the authors' earlier papers (also similar to what is used in MP20), is questionable; however, the reply by Perrone and Mikhailov (2018) did not seem to have addressed some of the key issues raised in the Zhang et al. (2018) commentary. For example, the ambiguity/uncertainty among the derived parameters is not quantified. This problem arises because a foF2 increase, for example, could be related to [O] increase, [O 2 ] and/or [N 2 ] decrease, changes in the neutral winds, changes in ion drifts and changes in ambipolar diffusion, and/or a different combination of these parameters. Zhang et al. (2001) demonstrated this ambiguous behavior even when full Ne profiles were used. It is interested to note that Perrone and Mikhailov (2017) compared the differences between neutral mass densities from CHAMP and their method that found the deviations are larger than 10% for approximately 45 out of 70 samples. We question whether this kind of accuracy is typical during storms and is adequate to explain a small neutral mass density reduction of less than 5%-10% (see Tables in MP20). Another major problem with the technique is that the derived neutral parameters are not physically self-consistent, but are modifications to the background MSIS model. The complicated, coupled relationship between density, temperature, and composition in the thermosphere exhibits large nonlinearity, which is not well represented by the empirical MSIS model, particularly, during storms.

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“…Comments on our method (Perrone & Mikhailov, 2018a) used in the paper are the same reported by Zhang at al. (2018) and they are repeated again. Our detailed answer was given by Perrone and Mikhailov (2018b), so there is no need to explain the method once again.…”

Section: The Methodology Used In Mp20 Is Questionable and In Fact Incorrect And The Results Lack Of Proper Uncertainty Estimate And Verifmentioning

confidence: 99%

Reply to Comments by Lei et al. on the Paper “Poststorm Thermospheric NO Overcooling?” by A.V. Mikhailov and L. Perrone (2020)

Mikhailov

Perrone

2021

JGR Space Physics

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The Methodology Used in MP20 is Questionable, and in Fact, Incorrect, and the Results Lack of Proper Uncertainty Estimate and Verification Comments on our method (Perrone & Mikhailov, 2018a) used in the paper are the same reported by Zhang at al. (2018) and they are repeated again. Our detailed answer was given by Perrone and Mikhailov (2018b), so there is no need to explain the method once again. Our method is not a 1-D model (as the authors of Comments call it) but a method to extract a consistent set of the main aeronomic parameters responsible for the formation of noontime F-layer at middle latitudes using observed Ne(h) distribution. This Ne(h) totally manifests the state of surrounding thermosphere and the intensity of incident solar EUV. The method was tested using CHAMP/STAR neutral gas density observations under various seasons, levels of solar and geomagnetic activity, and it was shown that the method provided statistically significant better results in a comparison to modern empirical thermospheric models. A comparison with Swarm neutral density observations was used to explain the post-storm neutral density decrease in the thermosphere (Mikhailov & Perrone, 2020). Millstone Hill ISR noontime h m F 2 observations (as the most reliable h m F 2 data) in 2000-2016 have been also used to test the method (Perrone et al., 2020a). The retrieved h m F 2 values demonstrated a standard deviation close to the expected inaccuracy of h m F 2 determination. A comparison of the retrieved EUV to observed one as well to satellite neutral gas density observations given by Perrone et al. (2020b) provides an absolutely independent check of the method as the observed EUV and neutral gas density have nothing common with the retrieval process. Figure 1 gives a comparison of June monthly median retrieved EUV flux to composite HLα (Machol et al., 2019) and EUVAC model (Richards et al., 1994) variations for the period.The correlation coefficient between the retrieved EUV and HLα variations is 0.978 ± 0.018 being significant at the 99.9% confidence level according to Student criterion. A coincidence is seen even in details, notice two-hump maxima in the even solar cycles. A comparison with the EUVAC model gives the correlation coefficient 0.987 ± 0.011 which is also significant at the 99.9% confidence level.Therefore, there are no reasons not to rely on the results obtained with this method. Anyway, nobody has shown this yet.

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Comments on “Long‐Term Variations of Exospheric Temperature Inferred From foF1 Observations: A Comparison to ISR T_i Trend Estimates” by Perrone and Mikhailov

Cited by 8 publications

References 31 publications

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Comments on “Poststorm Thermospheric NO Overcooling?” by Mikhailov and Perrone (2020)

Reply to Comments by Lei et al. on the Paper “Poststorm Thermospheric NO Overcooling?” by A.V. Mikhailov and L. Perrone (2020)

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Comments on “Long‐Term Variations of Exospheric Temperature Inferred From foF1 Observations: A Comparison to ISR Ti Trend Estimates” by Perrone and Mikhailov

Cited by 8 publications

References 31 publications

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Comments on “Poststorm Thermospheric NO Overcooling?” by Mikhailov and Perrone (2020)

Reply to Comments by Lei et al. on the Paper “Poststorm Thermospheric NO Overcooling?” by A.V. Mikhailov and L. Perrone (2020)

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Comments on “Long‐Term Variations of Exospheric Temperature Inferred From foF1 Observations: A Comparison to ISR T_i Trend Estimates” by Perrone and Mikhailov