Refine
Language
- English (59)
Is part of the Bibliography
- yes (59)
Keywords
- magnetosphere (8)
- radiation belts (8)
- electrons (6)
- Radiation belts (4)
- Van Allen Probes (4)
- inner magnetosphere (3)
- model (3)
- pitch angle (3)
- wave-particle interactions (3)
- EMIC (2)
Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near-Earth space into a single parameter. Most of the best-known indices are calculated from ground-based magnetometer data sets, such as Dst, SYM-H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root-mean-square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices. Plain Language Summary One aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.
We perform a statistical study calculating electromagnetic ion cyclotron (EMIC) wave amplitudes based off in situ plasma measurements taken by the Van Allen Probes’ (1.1–5.8 Re) Helium, Oxygen, Proton, Electron (HOPE) instrument. Calculated wave amplitudes are compared to EMIC waves observed by the Electric and Magnetic Field Instrument Suite and Integrated Science on board the Van Allen Probes during the same period. The survey covers a 22-month period (1 November 2012 to 31 August 2014), a full Van Allen Probe magnetic local time (MLT) precession. The linear theory proxy was used to identify EMIC wave events with plasma conditions favorable for EMIC wave excitation. Two hundred and thirty-two EMIC wave events (103 H+-band and 129 He+-band) were selected for this comparison. Nearly all events selected are observed beyond L = 4. Results show that calculated wave amplitudes exclusively using the in situ HOPE measurements produce amplitudes too low compared to the observed EMIC wave amplitudes. Hot proton anisotropy (Ahp) distributions are asymmetric in MLT within the inner (L < 7) magnetosphere with peak (minimum) Ahp, ∼0.81 to 1.00 (∼0.62), observed in the dawn (dusk), 0000 < MLT ≤ 1200 (1200 < MLT ≤ 2400), sectors. Measurements of Ahp are found to decrease in the presence of EMIC wave activity. Ahp amplification factors are determined and vary with respect to EMIC wave-band and MLT. He+-band events generally require double (quadruple) the measured Ahp for the dawn (dusk) sector to reproduce the observed EMIC wave amplitudes.
Electromagnetic ion cyclotron waves have long been recognized to play a crucial role in the dynamic loss of ring current protons. While the field-aligned propagation approximation of electromagnetic ion cyclotron waves was widely used to quantify the scattering loss of ring current protons, in this study, we find that the wave normal distribution strongly affects the pitch angle scattering efficiency of protons. Increase of peak normal angle or angular width can considerably reduce the scattering rates of <= 10 keV protons. For >10 keV protons, the field-aligned propagation approximation results in a pronounced underestimate of the scattering of intermediate equatorial pitch angle protons and overestimates the scattering of high equatorial pitch angle protons by orders of magnitude. Our results suggest that the wave normal distribution of electromagnetic ion cyclotron waves plays an important role in the pitch angle evolution and scattering loss of ring current protons and should be incorporated in future global modeling of ring current dynamics.
At Saturn electrons are trapped in the planet’s magnetic field and accelerated to relativistic energies to form the radiation belts, but how this dramatic increase in electron energy occurs is still unknown. Until now the mechanism of radial diffusion has been assumed but we show here that in-situ acceleration through wave particle interactions, which initial studies dismissed as ineffectual at Saturn, is in fact a vital part of the energetic particle dynamics there. We present evidence from numerical simulations based on Cassini spacecraft data that a particular plasma wave, known as Z-mode, accelerates electrons to MeV energies inside 4 RS (1 RS = 60,330 km) through a Doppler shifted cyclotron resonant interaction. Our results show that the Z-mode waves observed are not oblique as previously assumed and are much better accelerators than O-mode waves, resulting in an electron energy spectrum that closely approaches observed values without any transport effects included.
Current algorithms for the real-time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind-based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short-term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer-term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions.
We present the PINE (Plasma density in the Inner magnetosphere Neural network‐based Empirical) model ‐ a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural‐network‐based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model‐predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM‐H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.
New wave frequency and amplitude models for the nightside and dayside chorus waves are built based on measurements from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrument onboard the Van Allen Probes. The corresponding 3D diffusion coefficients are systematically obtained. Compared with previous commonly-used (typical) parameterizations, the new parameterizations result in differences in diffusion rates that depend on the energy and pitch angle. Furthermore, one-year 3D diffusive simulations are performed using the Versatile Electron Radiation Belt (VERB) code. Both typical and new wave parameterizations simulation results are in a good agreement with observations at 0.9 MeV. However, the new parameterizations for nightside chorus better reproduce the observed electron fluxes. These parameterizations will be incorporated into future modeling efforts.
Simulations of the inner magnetospheric energetic electrons using the IMPTAM-VERB coupled model
(2019)
In this study, we present initial results of the coupling between the Inner Magnetospheric Particle Transport and Acceleration Model (IMPTAM) and the Versatile Electron Radiation Belt (VERB-3D) code. IMPTAM traces electrons of 10-100 keV energies from the plasma sheet (L = 9 Re) to inner L-shell regions. The flux evolution modeled by IMPTAM is used at the low energy and outer L* computational boundaries of the VERB code (assuming a dipole approximation) to perform radiation belt simulations of energetic electrons. The model was tested on the March 17th, 2013 storm, for a six-day period. Four different simulations were performed and their results compared to satellites observations from Van Allen probes and GOES. The coupled IMPTAM-VERB model reproduces evolution and storm-time features of electron fluxes throughout the studied storm in agreement with the satellite data (within similar to 0.5 orders of magnitude). Including dynamics of the low energy population at L* = 6.6 increases fluxes closer to the heart of the belt and has a strong impact in the VERB simulations at all energies. However, inclusion of magnetopause losses leads to drastic flux decreases even below L* = 3. The dynamics of low energy electrons (max. 10s of keV) do not affect electron fluxes at energies >= 900 keV. Since the IMPTAM-VERB coupled model is only driven by solar wind parameters and the Dst and Kp indexes, it is suitable as a forecasting tool. In this study, we demonstrate that the estimation of electron dynamics with satellite-data-independent models is possible and very accurate.
The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.
We present a statistical analysis of phase space density data from the first 26 months of the Van Allen Probes mission. In particular, we investigate the relationship between the tens and hundreds of keV seed electrons and >1 MeV core radiation belt electron population. Using a cross-correlation analysis, we find that the seed and core populations are well correlated with a coefficient of approximate to 0.73 with a time lag of 10-15 h. We present evidence of a seed population threshold that is necessary for subsequent acceleration. The depth of penetration of the seed population determines the inner boundary of the acceleration process. However, we show that an enhanced seed population alone is not enough to produce acceleration in the higher energies, implying that the seed population of hundreds of keV electrons is only one of several conditions required for MeV electron radiation belt acceleration.