Nature's Grand Experiment: Linkage between magnetospheric convection and the radiation belts

The solar minimum of 2007–2010 was unusually deep and long lived. In the later stages of this period the electron fluxes in the radiation belts dropped to extremely low levels. The flux of relativistic electrons (>1 MeV) was significantly diminished and at times was below instrument thresholds both for spacecraft located in geostationary orbits and also those in low-Earth orbit. This period has been described as a natural “Grand Experiment” allowing us to test our understanding of basic radiation belt physics and in particular the acceleration mechanisms which lead to enhancements in outer belt relativistic electron fluxes. Here we test the hypothesis that processes which initiate repetitive substorm onsets drive magnetospheric convection, which in turn triggers enhancement in whistler mode chorus that accelerates radiation belt electrons to relativistic energies. Conversely, individual substorms would not be associated with radiation belt acceleration. Contrasting observations from multiple satellites of energetic and relativistic electrons with substorm event lists, as well as chorus measurements, show that the data are consistent with the hypothesis. We show that repetitive substorms are associated with enhancements in the flux of energetic and relativistic electrons and enhanced whistler mode wave intensities. The enhancement in chorus wave power starts slightly before the repetitive substorm epoch onset. During the 2009/2010 period the only relativistic electron flux enhancements that occurred were preceded by repeated substorm onsets, consistent with enhanced magnetospheric convection as a trigger

Techniques to determine the quiet day curve for a long period of subionospheric VLF observations

Very low frequency (VLF) transmissions propagating between the conducting Earth's surface and lower edge of the ionosphere have been used for decades to study the effect of space weather events on the upper atmosphere. The VLF response to these events can only be quantified by comparison of the observed signal to the estimated quiet time or undisturbed signal levels, known as the quiet day curve (QDC). A common QDC calculation approach for periods of investigation of up to several weeks is to use observations made on quiet days close to the days of interest. This approach is invalid when conditions are not quiet around the days of interest. Longer-term QDCs have also been created from specifically identified quiet days within the period and knowledge of propagation characteristics. This approach is time consuming and can be subjective. We present three algorithmic techniques, which are based on either (1) a mean of previous days' observations, (2) principal component analysis, or (3) the fast Fourier transform (FFT), to calculate the QDC for a long-period VLF data set without identification of specific quiet days as a basis. We demonstrate the effectiveness of the techniques at identifying the true QDCs of synthetic data sets created to mimic patterns seen in actual VLF data including responses to space weather events. We find that the most successful technique is to use a smoothing method, developed within the study, on the data set and then use the developed FFT algorithm. This technique is then applied to multiyear data sets of actual VLF observations

Techniques to Determine Quiet Day Curves for Subionospheric VLF Observations

The ionization rate of the upper atmosphere can be significantly increased by space weather events, examples being solar proton events (SPE), solar flares, and energetic electron precipitation from the radiation belts. An increase in the ionization rate leads to a lowering of the lower edge of the ionospheric D-region. To study the effect of space weather events on our atmosphere it is important 1) to be able to detect the events and also 2) to have some way of determining changes in the height of the D-region. Very low frequency (VLF) radio waves propagate in the waveguide between the surface of the Earth and the lower edge of the ionosphere (D-region). Changes in the height of the D-region lead to changes in the amplitude and phase of the VLF signal received at an antenna. To gain an accurate indication of the size of these changes we need to know what the undisturbed signal, known as a Quiet Day Curve (QDC), would have been if no space weather event had taken place. High power narrow-band communications transmitters operated by multiple nations provide the VLF radio signals used in this technique. In this study we use VLF radio wave observations from the Antarctic-Arctic Radiation- belt Dynamic Deposition VLF Atmospheric Research Konsortia (AARDDVARK) receivers located at Edmonton, Canada and Scott Base, Antarctica. The purpose of this study is to develop a technique for the automatic calculation of QDCs for long-period experimental subionospheric VLF data sets. To enable the quantitative evaluation of how well our QDC finding techniques identify the true QDC of a data set, we have created a suite of synthetic data layers with a known QDC and imposed perturbations similar to those seen in real VLF data. We present this evaluation and comparison between the techniques to allow determination of the best QDC finding technique from those developed. We evaluate two techniques for determining a long-period QDC by algorithm. These are Principal Component Analysis (PCA) and 2-dimensional Discrete Fourier Transforms (DFT). We also evaluate an averaging technique that finds a combined daily curve as a baseline comparison to our techniques. We further evaluate several adjustments to these techniques, endeavouring to improve the resulting QDC. We determine that the best QDC technique for data sets longer than two years is an adjustment to the DFT technique, while, for data sets shorter than two years, the best technique is PCA applied to a smoothed data set. We judge the success of our adjusted DFT technique from the finding that the typical difference between the QDC and the synthetic data background is 0.13~dB for day and 0.17~dB for night. These values are smaller than typical experimentally observed noise levels. We therefore conclude that this QDC finding technique is successful. The pre-smoothed PCA technique gives a typical difference between the QDC and the synthetic data background of 0.38~dB for day, 0.47~dB for night. We therefore conclude that this QDC finding technique is fairly successful, although not as conclusively as the DFT based technique is. We then apply our chosen QDC finding techniques, according to the length of the data sets, to the VLF observations. We apply the adjusted DFT technique to our Scott Base data sets, which span 4 years of observations, i.e., 2,103,840 minutes. We find that the QDC finding technique appears to qualitatively extract the QDC from these real data sets. In particular during solar flares, the extraction looks sensible. We provide examples of the difference between the received VLF signal and the QDC for an example day during which 10 solar flares occurred. We apply the pre-smoothed PCA technique to our Edmonton data sets, which span 20 months of usable data, i.e., 915,840 minutes. We find that this technique appears to be working, but have less confidence in its ability to extract perturbations lasting longer than a few hours

Techniques to Determine Quiet Day Curves for Subionospheric VLF Observations

The ionization rate of the upper atmosphere can be significantly increased by space weather events, examples being solar proton events (SPE), solar flares, and energetic electron precipitation from the radiation belts. An increase in the ionization rate leads to a lowering of the lower edge of the ionospheric D-region. To study the effect of space weather events on our atmosphere it is important 1) to be able to detect the events and also 2) to have some way of determining changes in the height of the D-region. Very low frequency (VLF) radio waves propagate in the waveguide between the surface of the Earth and the lower edge of the ionosphere (D-region). Changes in the height of the D-region lead to changes in the amplitude and phase of the VLF signal received at an antenna. To gain an accurate indication of the size of these changes we need to know what the undisturbed signal, known as a Quiet Day Curve (QDC), would have been if no space weather event had taken place. High power narrow-band communications transmitters operated by multiple nations provide the VLF radio signals used in this technique. In this study we use VLF radio wave observations from the Antarctic-Arctic Radiation- belt Dynamic Deposition VLF Atmospheric Research Konsortia (AARDDVARK) receivers located at Edmonton, Canada and Scott Base, Antarctica. The purpose of this study is to develop a technique for the automatic calculation of QDCs for long-period experimental subionospheric VLF data sets. To enable the quantitative evaluation of how well our QDC finding techniques identify the true QDC of a data set, we have created a suite of synthetic data layers with a known QDC and imposed perturbations similar to those seen in real VLF data. We present this evaluation and comparison between the techniques to allow determination of the best QDC finding technique from those developed. We evaluate two techniques for determining a long-period QDC by algorithm. These are Principal Component Analysis (PCA) and 2-dimensional Discrete Fourier Transforms (DFT). We also evaluate an averaging technique that finds a combined daily curve as a baseline comparison to our techniques. We further evaluate several adjustments to these techniques, endeavouring to improve the resulting QDC. We determine that the best QDC technique for data sets longer than two years is an adjustment to the DFT technique, while, for data sets shorter than two years, the best technique is PCA applied to a smoothed data set. We judge the success of our adjusted DFT technique from the finding that the typical difference between the QDC and the synthetic data background is 0.13~dB for day and 0.17~dB for night. These values are smaller than typical experimentally observed noise levels. We therefore conclude that this QDC finding technique is successful. The pre-smoothed PCA technique gives a typical difference between the QDC and the synthetic data background of 0.38~dB for day, 0.47~dB for night. We therefore conclude that this QDC finding technique is fairly successful, although not as conclusively as the DFT based technique is. We then apply our chosen QDC finding techniques, according to the length of the data sets, to the VLF observations. We apply the adjusted DFT technique to our Scott Base data sets, which span 4 years of observations, i.e., 2,103,840 minutes. We find that the QDC finding technique appears to qualitatively extract the QDC from these real data sets. In particular during solar flares, the extraction looks sensible. We provide examples of the difference between the received VLF signal and the QDC for an example day during which 10 solar flares occurred. We apply the pre-smoothed PCA technique to our Edmonton data sets, which span 20 months of usable data, i.e., 915,840 minutes. We find that this technique appears to be working, but have less confidence in its ability to extract perturbations lasting longer than a few hours

Developing a Nowcasting Capability for X-Class Solar Flares Using VLF Radiowave Propagation Changes

A technique for analyzing very low frequency (VLF) radiowave signals is investigated in order to achieve rapid, real-time detection of large solar flares, through the monitoring of changes in VLF radio signal propagation conditions. The reliability of the use of VLF phase and amplitude perturbations to determine the X-ray fluxes involved during 10 large solar flare events (>X1) is examined. Linear regression analysis of signals from the NPM transmitter in Hawaii, received at Arrival Heights, Scott Base, Antarctica, over the years 2011-2015 shows that VLF phase perturbations during large solar flares have a 1.5-3 times lower mean square error when modeling the long wavelength X-ray fluxes than the equivalent short wavelength fluxes. The use of VLF amplitude observations to determine long or short wavelength X-ray flux levels have a 4-10 times higher mean square error than when using VLF phase. Normalized linear regression analysis identifies VLF phase as the most important parameter in the regression, followed by solar zenith angle at the midpoint of the propagation path, then the initial solar X-ray flux level (from 5 min before the impact of the solar flare), with F10.7 cm flux from the day beforehand providing the least important contribution. Transmitter phase measurements are more difficult to undertake than amplitude. However, networks of VLF receivers already exist which include the high quality phase capability required for such a nowcasting product. Such narrowband VLF data can be a redundant source of flare monitoring if satellite data is not available.Peer reviewe