The coupling between the solar wind and proton fluxes at GEO

The relationship between the solar wind and the proton flux at geosynchronous Earth orbit (GEO) is investigated using the error reduction ratio (ERR) analysis. The ERR analysis is able to search for the most appropriate inputs that control the evolution of the system. This approach is a black box method and is able to derive a mathematical model of a system from input-output data. This method is used to analyse eight energy ranges of the proton flux at GEO from 80 keV to 14.5 MeV. The inputs to the algorithm were solar wind velocity, density and pressure; the Dst index; the solar energetic proton (SEP) flux; and a function of the interplanetary magnetic field (IMF) tangential magnitude and clock angle. The results show that for lowest five energy channels (80 to 800 keV) the GEO proton fluxes are controlled by the solar wind velocity with a lag of two to three days. However, above 350 keV, the SEP fluxes, accounts for a significant portion of the GEO proton flux variance. For the highest three energy channels (0.74 to 14.5 MeV), the SEPs account for the majority of the ERR. The results also show an anisotropy of protons with gyrocenters inside GEO and outside GEO, where the protons inside GEO are controlled partly by the Dst index and also an IMF-clock angle function. © 2013 Author(s)

Electron Flux Dropouts at L ∼ 4.2 From Global Positioning System Satellites: Occurrences, Magnitudes, and Main Driving Factors

Dropouts in electron fluxes at L ∼ 4.2 were investigated for a broad range of energies from 120 keV to 10 MeV, using 16 years of electron flux data from Combined X-ray Dosimeter on board Global Positioning System (GPS) satellites. Dropouts were defined as flux decreases by at least a factor 4 in 12 h, or 24 h during which a decrease by at least a factor of 1.5 must occur during each 12 h time bin. Such fast and strong dropouts were automatically identified from the GPS electron flux data and statistics of dropout magnitudes, and occurrences were compiled as a function of electron energy. Moreover, the Error Reduction Ratio analysis was employed to search for nonlinear relationships between electron flux dropouts and various solar wind and geomagnetic activity indices, in order to identify potential external causes of dropouts. At L ∼ 4.2, the main driving factor for the more numerous and stronger 1-10 MeV electron dropouts turns out to be the southward interplanetary magnetic field B s , suggesting an important effect from precipitation loss due to combined electromagnetic ion cyclotron and whistler mode waves in a significant fraction of these events, supplementing magnetopause shadowing and outward radial diffusion which are also effective at lower energies

Electron flux dropouts at GEO: occurrences, magnitudes, and main driving factors

Large decreases of daily average electron flux, or dropouts, were investigated for a range of energies from 24.1 keV to 2.7 MeV, on the basis of a large database of 20 years of measurements from Los Alamos National Laboratory (LANL) geosynchronous satellites. Dropouts were defined as flux decreases by at least a factor 4 in 1 day, or a factor 9 in 2 days during which a decrease by at least a factor of 2.5 must occur each day. Such decreases were automatically identified. As a first result, a comprehensive statistics of the mean waiting time between dropouts and of their mean magnitude has been provided as a function of electron energy. Moreover, the Error Reduction Ratio analysis was applied to explore the possible nonlinear relationships between electron dropouts and various exogenous factors, such as solar wind and geomagnetic indices. Different dropout occurrences and magnitudes were found in three distinct energy ranges, lower than 100 keV, 100–600 keV, and larger than 600 keV, corresponding to different groups of drivers and loss processes. Potential explanations have been outlined on the basis of the statistical results

Modelling and Prediction of Global Magnetic Disturbance in Near-Earth Space: a Case Study for Kp Index using NARX Models

Severe geomagnetic disturbances can be hazardous for mod-ern technological systems. The reliable forecast of parameters related to thestate of the magnetosphere can facilitate the mitigation of adverse effects ofspace weather. This study is devoted to the modeling and forecasting of theevolution of the Kp index related to global geomagnetic disturbances. Through-out this work the Nonlinear AutoRegressive with eXogenous inputs (NARX)methodology is applied. Two approaches are presented: i) a recursive slid-ing window approach, and ii) a direct approach. These two approaches arestudied separately and are then compared to evaluate their performances.It is shown that the direct approach outperforms the recursive approach, butboth tend to produce predictions slightly biased from the true values for lowand high disturbances

Prediction of Kp Index Using NARMAX Models with A Robust Model Structure Selection Method

The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar wind. This study presents 3 hours ahead prediction for Kp index using the NARMAX model identified by a novel robust model structure detection method. The identified models are evaluated using 4 years of Kp data. Overall, the models with robust structure can produce very good Kp forecast results and provide transparent and compact representations of the relationship between Kp index and solar wind variables. The robustness and conciseness of the models can highly benefit the space weather forecast tasks

On the regional variability of dB/dt and its significance to GIC

Faraday's law of induction is responsible for setting up a geoelectric field due to the variations in the geomagnetic field caused by ionospheric currents. This drives geomagnetically induced currents (GICs) which flow in large ground‐based technological infrastructure such as high‐voltage power lines. The geoelectric field is often a localized phenomenon exhibiting significant variations over spatial scales of only hundreds of kilometers. This is due to the complex spatiotemporal behavior of electrical currents flowing in the ionosphere and/or large gradients in the ground conductivity due to highly structured local geological properties. Over some regions, and during large storms, both of these effects become significant. In this study, we quantify the regional variability of dB/dt using closely placed IMAGE stations in northern Fennoscandia. The dependency between regional variability, solar wind conditions, and geomagnetic indices are also investigated. Finally, we assess the significance of spatial geomagnetic variations to modeling GICs across a transmission line. Key results from this study are as follows: (1) Regional geomagnetic disturbances are important in modeling GIC during strong storms; (2) dB/dt can vary by several times up to a factor of three compared to the spatial average; (3) dB/dt and its regional variation is coupled to the energy deposited into the magnetosphere; and (4) regional variability can be more accurately captured and predicted from a local index as opposed to a global one. These results demonstrate the need for denser magnetometer networks at high latitudes where transmission lines extending hundreds of kilometers are present

System identification of local time electron fluencies at geostationary orbit

The electron fluxes at geostationary orbit measured by Geostationary Operational Environmental Satellite (GOES) 13, 14, and 15 spacecraft are modeled using system identification techniques. System identification, similar to machine learning, uses input‐output data to train a model, which can then be used to provide forecasts. This study employs the nonlinear autoregressive moving average exogenous technique to deduce the electron flux models. The electron fluxes at geostationary orbit are known to vary in space and time, making it a spatiotemporal system, which complicates the modeling using system identification/machine learning approach. Therefore, the electron flux data are binned into 24 magnetic local time (MLT), and a separate model is developed for each of the 24 MLT bins. MLT models are developed for six of the GOES 13, 14, and 15 electron flux energy channels (75 keV, 150 keV, 275 keV, 475 keV, >800 keV, and >2 MeV). The models are assessed on separate test data by prediction efficiency (PE) and correlation coefficient (CC) and found these to vary by MLT and electron energy. The lowest energy of 75 keV at the midnight sector had a PE of 36.0 and CC of 59.3, which increased on the dayside to a PE of 66.9 and CC of 81.6. These metrics increased to the >2 MeV model, which had a low PE and CC of 63.0 and 81.8 on the nightside to a high of 80.3 and 90.8 on the dayside

The system science development of local time dependent 40 keV electron flux models for geostationary orbit

At Geosynchronous Earth Orbit (GEO), the radiation belt/ring current electron fluxes with energies up to several hundred keV, can vary widely in Magnetic Local Time (MLT). This study aims to develop Nonlinear AutoRegressive eXogenous (NARX) models using system science techniques, which account for the spatial variation in MLT. This is difficult for system science techniques, since there is sparse data availability of the electron fluxes at different MLT. To solve this problem the data are binned from GOES 13, 14, and 15 by MLT, and a separate NARX model is deduced for each bin using solar wind variables as the inputs to the model. These models are then conjugated into one spatiotemporal forecast. The model performance statistics for each model varies in MLT with a Prediction Efficiency (PE) between 47% and 75% and a correlation coefficient (CC) between 51.3% and 78.9% for the period from 1 March 2013 to 31 December 2017

Forecast of the energetic electron environment of the radiation belts

Different modeling methodologies possess different strengths and weakness. For instance, data based models may provide superior accuracy but have a limited spatial coverage while physics based models may provide lower accuracy but provide greater spatial coverage. This study investigates the coupling of a data based model of the electron fluxes at geostationary orbit (GEO) with a numerical model of the radiation belt region to improve the resulting forecasts/pastcasts of electron fluxes over the whole radiation belt region. In particular, two coupling methods are investigated. The first assumes an average value for L* for GEO, namely urn:x-wiley:15427390:media:swe21428:swe21428-math-0001 = 6.2. The second uses a value of L* that varies with geomagnetic activity, quantified using the Kp index. As the terrestrial magnetic field responds to variations in geomagnetic activity, the value of L* will vary for a specific location. In this coupling method, the value of L* is calculated using the Kp driven Tsyganenko 89c magnetic field model for field line tracing. It is shown that this addition can result in changes in the initialization of the parameters at the Versatile Electron Radiation Belt model outer boundary. Model outputs are compared to Van Allen Probes MagEIS measurements of the electron fluxes in the inner magnetosphere for the March 2015 geomagnetic storm. It is found that the fixed urn:x-wiley:15427390:media:swe21428:swe21428-math-0002 coupling method produces a more realistic forecast

The influence of solar wind and geomagnetic indices on lower band chorus emissions in the inner magnetosphere

Statistical wave models, describing the distribution of wave amplitudes as a function of location, geomagnetic activity, and other parameters, are needed as the basis to describe the wave-particle interactions within numerical models of the radiation belts. In this study, we widen the scope of the statistical wave models by investigating which of the solar wind parameters or geomagnetic indices and their time lags have the greatest influence on the amplitudes of lower band chorus (LBC) waves in the inner magnetosphere. The solar wind parameters or geomagnetic indices with the greatest control over the waves were found using the error reduction ratio (ERR) analysis, which plays a key role in system identification modeling techniques. In this application, the LBC magnitudes at different locations are considered as the output data, while the lagged solar wind parameters are the input data. The ERR analysis automatically determines a set of the most influential parameters that explain the variations in the emissions. Both linear and nonlinear applications of the ERR analysis are compared using solar wind inputs and show that the linear ERR analysis can be misleading. The linear results show that the interplanetary magnetic field (IMF) factor has the most influence on at each magnetic local time (MLT) sector. However, the nonlinear ERR analysis shows that the IMF factor coupled with the solar wind velocity has the main contribution to the LBC wave magnitudes. When geomagnetic indices are included as inputs with the solar wind parameters to the nonlinear ERR analysis, the results show that the majority of the variation in emissions may be attributed to the Auroral Electrojet (AE) index. In the dawn sectors between 00 and 12 MLT and 5 < L < 7, the AE index multiplied by the solar wind velocity with zero time lag has the most influence on the amplitudes of LBC. For 5 < L < 7, the parameters with the highest ERR are the AE index multiplied by the solar wind velocity with a 2-hr time lag at 12–16 MLT, the linear AE index with a 2-hr time lag at 16–20 MLT, and AE index multiplied by the IMF factor with zero lag at 20–00 MLT. For 4 < L < 5, the parameters with the highest ERR are the AE index multiplied by the solar wind dynamic pressure with zero time lag at 00–04 MLT, the AE index multiplied by the solar wind velocity with zero time lag between 14 and 12 MLT, the AE index multiplied by the solar wind velocity with a 2-hr time lag at 12–16 MLT, the Dst index with a 6-hr time lag at 12–16 MLT, and the AE index multiplied by the IMF factor with zero lag at 20–00 MLT