1. Introduction
Observations of ionization trails from meteors using so-called meteor-wind radars (MWRs) have, of late, become the de facto method of determining winds and temperatures in the upper mesosphere. Currently, research is being performed and publications are produced using such observations: for example, Shepherd et al. (Reference Shepherd, Meek, Hocking, Hall, Partamies, Sigernes, Manson and Ward2020), Pancheva et al. (Reference Pancheva, Mukhtarov, Hall, Smith and Tsutsumi2020), and Pedatella et al. (Reference Pedatella, Liu, Chau, Hall, Jacobi, Mitchell and Tsutsumi2020) to name but a few, not to mention studies in progress. While these studies have most focus on mesospheric dynamics, radar echoes also reveal diffusion coefficients for the ions in the meteor trails, and these are used to estimate temperatures (e.g., Hocking, Reference Hocking1999; Hocking et al., Reference Hocking, Singer, Bremer, Mitchell, Batista, Clemesha and Donner2004; Holdsworth et al., Reference Holdsworth, Morris, Murphy, Reid, Burns and French2006; McKinley, Reference McKinley1961). Results are thereafter used to investigate inter alia middle atmosphere temperature trends (e.g., Hall et al., Reference Hall, Dyrland, Tsutsumi and Mulligan2012; Holmen et al., Reference Holmen, Hall and Tsutsumi2015), and thus, the reliability of the temperature determinations is becoming increasingly important.
Ambipolar diffusion determined by MWRs has been critically examined by Chilson et al. (Reference Chilson, Czechowsky and Schmidt1996), Dyrud et al. (Reference Dyrud, Oppenheim and vom Endt2001), Hall (Reference Hall2002), Hocking et al. (Reference Hocking, Singer, Bremer, Mitchell, Batista, Clemesha and Donner2004), Hall et al. (Reference Hall, Aso, Tsutsumi, Nozawa, Manson and Meek2005), Dyrland et al. (Reference Dyrland, Hall, Mulligan and Tsutsumi2010), and so on. Recently, however, it was noticed that increases in diffusivity are associated with strong geomagnetic activity (Hall & Johnsen, Reference Hall and Johnsen2020). The implications of this include overestimation of temperatures particularly in the auroral zones regions (i.e., high latitudes). Secular changes in geomagnetic activity and even migration of the geomagnetic poles could easily induce misleading identification of temperature trends, but any research based on high latitude temperature observations from MWRs could be affected. Furthermore, the findings of Hall and Johnsen (Reference Hall and Johnsen2020) were based on the results from two high-latitude radars, and lower latitude effects were not dismissed. The derivation of temperatures from MWR echo fading times via estimation of ambipolar diffusivity is well described in the literature (and the references herein) and will not be explained in detail here. Instead, we focus on the effects of geomagnetic activity on temperature estimates (note use of the term “estimates”) and how we may correct for them, both for long-term time-series and conceivably in quasi-real-time.
It must be stressed that this study is intended to highlight potential problems using MWR data and thereafter drawing scientific conclusions from derived temperatures without taking geomagnetic activity into account. We give an account of how temperatures may be corrected to alleviate the geomagnetic effects, but do not attempt to present a comprehensive set of tools that result in reliable temperatures under all circumstances.
2. Method and results
The Nippon/Norway Svalbard Meteor Radar (NSMR at 78°N, 16°E, since 2002) is used for provision of temperature estimates, in particular, employing the pressure method of Holdsworth et al. (Reference Holdsworth, Morris, Murphy, Reid, Burns and French2006). A comprehensive list of references is given above and thus the exact method of derivation is not described here. It should be mentioned, however, that models for air pressure and composition of the meteor trail are implicit. Because of these, the initial derivations of temperatures are normalized to satellite data (AURA specifically, as explained in the references). Data between 2002 and 2019 are employed here and analyzed to yield daily temperature estimates at 90 km altitude
In order to parameterize geomagnetic activity, we employ the auroral zone activity index as introduced by Hall and Johnsen (Reference Hall and Johnsen2020), this being a local metric and, in this study, determined from magnetometer observations in the vicinity of NSMR. Figure 1 shows both the geomagnetic and temperature data on the same plot. These values are those used in the subsequent analyses.
Figure 1. Geomagnetic Activity Indices (blue) in nT and, from start of operations, derived neutral air temperatures at 90 km (red) in Kelvin, both from Svalbard (78°N, 16°E).
In Figure 2, the results of Hall and Johnsen (Reference Hall and Johnsen2020) are, in part, reproduced. Temperature estimates are plotted as a function of auroral zone activity index as are linear least-squares fits with geomagnetic activity as the independent variable; all years are utilized and June and December months are selected as representative for winter and summer, respectively. It can be seen that the summer and winter slopes are very similar, indeed, but that the intercepts (corresponding to no geomagnetic disturbance at all) reflect the typical summer and winter upper mesosphere temperatures. The most striking difference is in the spread—points being much more concentrated near the regression line in summer. The 95% confidence limits are indicated (Working & Hotelling, Reference Working and Hotelling1929). Intercepts and slopes for Individual months are shown in Figure 3. Again, the intercepts illustrate the seasonal variation of the upper mesosphere temperature and with little uncertainty. In contrast, the slopes vary considerably with month and uncertainties are considerable.
Figure 2. Scatter plots of derived 90 km temperatures versus geomagnetic activity index employing data from the preceding figure. Left panel: June; right panel: December. The plots show the same results as those of Hall & Johnsen, Reference Hall and Johnsen2020. Lines show least-squares fit regressions, taking geomagnetic activity indices as the independent variable. Dotted hyperbolae indicate the 95% confidence limits according to Working and Hotelling (Reference Working and Hotelling1929).
Figure 3. Results of linear fits exemplified by the previous figure, here all available years and sorted by month. Upper panel intercepts (zero activity index); lower panel: slopes. Error bars (1-sigma) are indicated by the vertical lines.
Here, we shall illustrate corrections to the original temperatures estimates based on the dependence on auroral zone activity index. The presentation is merely a demonstration; different scientific requirements may dictate choice of length of time series for performing preceding regression analyses. In this instance, we employ the monthly mean coefficients (i.e., Figure 3) and correct the temperature time-series at one-day resolution. Examining the entire time-series, we find the correction is small, but it is possible to discern an overall reduction in temperatures. Rather than presenting these, and in order to better illustrate the effect, Figure 4 shows December and July time-series from 2018 where differences between individual days are evident. Both original and corrected values, the latter consistently slightly lower, are shown together with the average difference over the respective month. In summer, the difference is somewhat larger than in winter. If we place these results in the framework of observation of phenomena related to low temperatures in the summer mesopause (e.g., noctilucent clouds, etc.), the difference discerned in the latter part of July has significance, indicating the importance of making this correction.
Figure 4. Typical examples of temperatures corrected for geomagnetic effects. Upper panel: December 2018 and lower panel: June 2018. Original temperature estimates are shown in red, and corrected values in blue. The mean corrections over all days are indicated in the plot: 4 K in winter and ˜7 K in summer.
3. Conclusion
It has been demonstrated that temperature estimates derived from meteor-radar data, wherein the method involves estimating the diffusion coefficient of ions in the meteor trail can be falsely enhanced during conditions of high geomagnetic activity. The enhancement is however quite well-defined, and by obtaining coefficients of linear fits of temperature estimated to the geomagnetic activity (which we parameterize by the activity index presented by Hall & Johnsen, Reference Hall and Johnsen2020) it is possible to apply a correction. For the data presented here from Svalbard (78°N, 16°E) a typical summer overestimate can be around 7 K whereas in winter only 4 K. The timescales used to obtain the dependence depend somewhat on the scientific circumstances, and in principle corrected temperatures could be delivered daily assuming magnetometer and radar installations are similarly located.
Acknowledgments
The meteor-wind-radar (NSMR) providing results for this study is partly owned by National Institute of Polar Research, Japan and the authors therefore thank co-investigator Masaki Tsutsumi for his involvement.
Funding statement
This research employed data from instruments supported by the Research Council of Norway under the project Svalbard Integrated Arctic Earth Observing System—Infrastructure development of the Norwegian node (SIOS-InfraNor, Project No. 269927).
Conflict of interest
The authors declare no conflicts of interest.
Authorship contributions
The NSMR system is operated by C.M.H., and the magnetometer systems by M.G.J. Both the authors contributed equally to data analysis and interpretation.
Data availability statement
Data from the radar are available from the Svalbard Integrated Arctic Earth Observing System portal (https://sios-svalbard.org/): via https://sios-svalbard.org/metsis/display/metadata/?core=l1&datasetID=308345a1-51ea-5751-b67b-f31afc6c5c71&calling_results_page= https://sios-svalbard.org/results?page=1. The Arctic University of Norway has a repository at: http://radars.uit.no/MWR/NSMR/METADATA.txt.
The geomagnetic data used in this research are available via TGO’s data repository (http://www.tgo.uit.no), IMAGE (https://space.fmi.fi/image/www/index.php?page=home), SuperMAG (http://supermag.jhuapl.edu/) and, for Tromsø, World Data Centre for Geomagnetism (Edinburgh), (http://www.wdc.bgs.ac.uk/, IAGA code TRO). The first of these is the primary repository for Norwegian geomagnetic data. The last also provides a URL to the Norwegian national repository.
All sites were accessed on 20th January 2021.
Open access
Comments
Comments to the Author: The authors present a solution for correction of upper atmosphere temperature derived from meteor radars as a function of geomagnetic activity. The results presented show a consistent overestimation of temperature that must be corrected, and this correction factor is generally larger in polar summer. While the manuscript does not go into detail (but this is covered in references) proving that this is indeed an overestimation of temperature, the authors do sufficiently show that there is a relation between temperature estimation and geomagnetic activity and how one might correct for it.