Long‐term determination of energetic electron precipitation into the atmosphere from AARDDVARK subionospheric VLF observations

Neal, J. J.; Rodger, Craig J.; Clilverd, Mark A.; Thomson, Neil R.; Raita, Tero; Ulich, Thomas

doi:10.1002/2014ja020689

Cited by 30 publications

(47 citation statements)

References 73 publications

(148 reference statements)

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“…Inversion of this combined model gave F 30 as a function of VLF amplitude, if k is given. More details of the procedure are given by Neal et al [] and Rodger et al [].…”

Section: Poes/sem Electron Flux Measurementsupporting

confidence: 61%

A model providing long‐term data sets of energetic electron precipitation during geomagnetic storms

et al. 2016

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The influence of solar variability on the polar atmosphere and climate due to energetic electron precipitation (EEP) has remained an open question largely due to lack of a long-term EEP forcing data set that could be used in chemistry-climate models. Motivated by this, we have developed a model for 30-1000 keV radiation belt driven EEP. The model is based on precipitation data from low Earth orbiting POES satellites in the period 2002-2012 and empirically described plasmasphere structure, which are both scaled to a geomagnetic index. This geomagnetic index is the only input of the model and can be either Dst or Ap. Because of this, the model can be used to calculate the energy-flux spectrum of precipitating electrons from 1957 (Dst) or 1932 (Ap) onward, with a time resolution of 1 day. Results from the model compare well with EEP observations over the period of 2002-2012. Using the model avoids the challenges found in measured data sets concerning proton contamination. As demonstrated, the model results can be used to produce the first ever >80 year long atmospheric ionization rate data set for radiation belt EEP. The impact of precipitation in this energy range is mainly seen at altitudes 70-110 km. The ionization rate data set, which is available for the scientific community, will enable simulations of EEP impacts on the atmosphere and climate with realistic EEP variability. Due to limitations in this first version of the model, the results most likely represent an underestimation of the total EEP effect.

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“…Inversion of this combined model gave F 30 as a function of VLF amplitude, if k is given. More details of the procedure are given by Neal et al [] and Rodger et al [].…”

Section: Poes/sem Electron Flux Measurementsupporting

confidence: 61%

A model providing long‐term data sets of energetic electron precipitation during geomagnetic storms

et al. 2016

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“…The significance of this level of the noise floor of MEPED/POES causing unreal EEP fluxes was earlier considered by Neal et al (, Section 6). They reported that the EEP fluxes at this noise floor level are sufficiently high to produce a four‐time increase in the noontime electron number density at around 75‐km altitude.…”

Section: Introductionmentioning

confidence: 80%

An Updated Model Providing Long‐Term Data Sets of Energetic Electron Precipitation, Including Zonal Dependence

et al. 2018

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In this study 30‐ to 1,000‐keV energetic electron precipitation (EEP) data from low Earth orbiting National Oceanic and Atmospheric Administration and MetOp Polar Orbiting Environmental Satellites were processed in two improved ways, compared to previous studies. First, all noise‐affected data were more carefully removed, to provide more realistic representations of low fluxes during geomagnetically quiet times. Second, the data were analyzed dependent on magnetic local time (MLT), which is an important factor affecting precipitation flux characteristics. We developed a refined zonally averaged EEP model, and a new model dependent on MLT, which both provide better modeling of low fluxes during quiet times. The models provide the EEP spectrum assuming a power law gradient. Using the geomagnetic index Ap with a time resolution of 1 day, the spectral parameters are provided as functions of the L shell value relative to the plasmapause. Results from the models compare well with EEP observations over the period 1998–2012. Analysis of the MLT‐dependent data finds that during magnetically quiet times, the EEP flux concentrates around local midnight. As disturbance levels increase, the flux increases at all MLT. During disturbed times, the flux is strongest in the dawn sector and weakest in the late afternoon sector. The MLT‐dependent model emulates this behavior. The results of the models can be used to produce ionization rate data sets over any time period for which the geomagnetic Ap index is available (recorded or predicted). This ionization rate data set will enable simulations of EEP impacts on the atmosphere and climate with realistic EEP variability.

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“…Furthermore, the VLF instrument at SGO is specifically maintained to operate at constant gain. Comparison against a second, independent instrument indicated negligible gain change over 3 years of operation (Neal et al, ). Additionally, logged system changes (e.g., wiring repairs) from 2004 onward had no influence on system gain.…”

Section: Methodsmentioning

confidence: 99%

D‐Region High‐Latitude Forcing Factors

et al. 2019

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The subionospheric very low frequency (VLF) radio wave technique provides the possibility of investigating the response of the ionospheric D‐region to a diversity of transient and long‐term physical phenomena originating from above (e.g., energetic particle precipitation) and from below (e.g., atmospheric waves). In this study, we identify the periodicities that appear in VLF measurements and investigate how they may be related to changes in space weather and atmospheric activity. The powerful VLF signal transmitted from NAA (24 kHz) on the east coast of the United States, and received at Sodankylä, Finland, was analyzed. Wavelet transform, wavelet power spectrum, wavelet coherence, and cross‐wavelet spectrum were computed for daily averages of selected ionospheric, space weather, and atmospheric parameters from November 2008 until June 2018. Our results show that the significant VLF periods that appear during solar cycle 24 are the annual oscillation, semiannual oscillation, 121‐day, 86‐day, 61‐day, and solar rotation oscillations. We found that the annual oscillation corresponds to variability in mesospheric temperature and solar Lyman‐α (Ly‐α) flux and the semiannual oscillation to variability in space weather‐related parameters. The solar rotation oscillation observed in the VLF variability is mainly related to the Ly‐α flux variation at solar maximum and to geomagnetic activity variation during the declining phase of the solar cycle. Our results are important since they strengthen our understanding of the Earth's D‐region response to solar and atmospheric forcing.

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Long‐term determination of energetic electron precipitation into the atmosphere from AARDDVARK subionospheric VLF observations

Cited by 30 publications

References 73 publications

A model providing long‐term data sets of energetic electron precipitation during geomagnetic storms

A model providing long‐term data sets of energetic electron precipitation during geomagnetic storms

An Updated Model Providing Long‐Term Data Sets of Energetic Electron Precipitation, Including Zonal Dependence

D‐Region High‐Latitude Forcing Factors

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