TY - JOUR A1 - Shprits, Yuri Y. A1 - Drozdov, Alexander A1 - Spasojevic, Maria A1 - Kellerman, Adam C. A1 - Usanova, Maria E. A1 - Engebretson, Mark J. A1 - Agapitov, Oleksiy V. A1 - Zhelavskaya, Irina A1 - Raita, Tero J. A1 - Spence, Harlan E. A1 - Baker, Daniel N. A1 - Zhu, Hui A1 - Aseev, Nikita T1 - Wave-induced loss of ultra-relativistic electrons in the Van Allen radiation belts JF - Nature Communications Y1 - 2016 U6 - https://doi.org/10.1038/ncomms12883 SN - 2041-1723 VL - 7 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Del Corpo, Alfredo A1 - Vellante, Massimo A1 - Zhelavskaya, Irina A1 - Shprits, Yuri Y. A1 - Heilig, Balazs A1 - Reda, Jan A1 - Pietropaolo, Ermanno A1 - Lichtenberger, Janos T1 - Study of the average ion mass of the dayside magnetospheric plasma JF - Journal of geophysical research : Space physics N2 - The investigation of heavy ions dynamics and properties in the Earth's magnetosphere is still an important field of research as they play an important role in several space weather aspects. We present a statistical survey of the average ion mass in the dayside magnetosphere made comparing plasma mass density with electron number density measurements and focusing on both spatial and geomagnetic activity dependence. Field line resonance frequency observations across the European quasi-Meridional Magnetometer Array, are used to infer the equatorial plasma mass density in the range of magnetic L-shells 1.6-6.2. The electron number density is derived from local electric field measurements made on Van Allen Probes using the Neural-network-based Upper-hybrid Resonance Determination algorithm. The analysis is conducted separately for the plasmasphere and the plasmatrough during favorable periods for which both the plasma parameters are observed simultaneously. We found that throughout the plasmasphere the average ion mass is similar or equal to 1 amu for a wide range of geomagnetic activity conditions, suggesting that the plasma mainly consist of hydrogen ions, without regard to the level of geomagnetic activity. Conversely, the plasmatrough is characterized by a variable composition, highlighting a heavy ion mass loading that increases with increasing levels of geomagnetic disturbance. During the most disturbed conditions, the average radial structure shows a broad maximum around 3-4 Earth radii, probably correlated with the accumulation of oxygen ions near the plasmapause. Those ions are mostly observed in the post-dawn and pre-dusk longitudinal sectors. KW - magnetospheric average ion mass KW - magnetospheric plasma spatial KW - distribution KW - oxygen torus KW - geomagnetic activity dependence KW - field line KW - resonances Y1 - 2022 U6 - https://doi.org/10.1029/2022JA030605 SN - 2169-9380 VL - 127 IS - 10 PB - American Geophysical Union CY - Washington, DC ER - TY - JOUR A1 - Aseev, Nikita A1 - Shprits, Yuri Y. A1 - Drozdov, Alexander A1 - Kellerman, Adam C. A1 - Usanova, Maria E. A1 - Wang, D. A1 - Zhelavskaya, Irina T1 - Signatures of Ultrarelativistic Electron Loss in the Heart of the Outer Radiation Belt Measured by Van Allen Probes JF - Journal of geophysical research : Space physics N2 - Up until recently, signatures of the ultrarelativistic electron loss driven by electromagnetic ion cyclotron (EMIC) waves in the Earth's outer radiation belt have been limited to direct or indirect measurements of electron precipitation or the narrowing of normalized pitch angle distributions in the heart of the belt. In this study, we demonstrate additional observational evidence of ultrarelativistic electron loss that can be driven by resonant interaction with EMIC waves. We analyzed the profiles derived from Van Allen Probe particle data as a function of time and three adiabatic invariants between 9 October and 29 November 2012. New local minimums in the profiles are accompanied by the narrowing of normalized pitch angle distributions and ground‐based detection of EMIC waves. Such a correlation may be indicative of ultrarelativistic electron precipitation into the Earth's atmosphere caused by resonance with EMIC waves. Y1 - 2017 U6 - https://doi.org/10.1002/2017JA024485 SN - 2169-9380 SN - 2169-9402 VL - 122 SP - 10102 EP - 10111 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Cao, Xing A1 - Shprits, Yuri Y. A1 - Ni, Binbin A1 - Zhelavskaya, Irina T1 - Scattering of Ultra-relativistic Electrons in the Van Allen Radiation Belts Accounting for Hot Plasma Effects JF - Scientific reports N2 - Electron flux in the Earth’s outer radiation belt is highly variable due to a delicate balance between competing acceleration and loss processes. It has been long recognized that Electromagnetic Ion Cyclotron (EMIC) waves may play a crucial role in the loss of radiation belt electrons. Previous theoretical studies proposed that EMIC waves may account for the loss of the relativistic electron population. However, recent observations showed that while EMIC waves are responsible for the significant loss of ultra-relativistic electrons, the relativistic electron population is almost unaffected. In this study, we provide a theoretical explanation for this discrepancy between previous theoretical studies and recent observations. We demonstrate that EMIC waves mainly contribute to the loss of ultra-relativistic electrons. This study significantly improves the current understanding of the electron dynamics in the Earth’s radiation belt and also can help us understand the radiation environments of the exoplanets and outer planets. Y1 - 2017 U6 - https://doi.org/10.1038/s41598-017-17739-7 SN - 2045-2322 VL - 7 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Saikin, Anthony A1 - Shprits, Yuri Y. A1 - Drozdov, Alexander A1 - Landis, Daji August A1 - Zhelavskaya, Irina A1 - Cervantes Villa, Juan Sebastian T1 - Reconstruction of the radiation belts for solar cycles 17-24 (1933-2017) JF - Space weather : the international journal of research and applications N2 - We present a reconstruction of the dynamics of the radiation belts from solar cycles 17 to 24 which allows us to study how radiation belt activity has varied between the different solar cycles. The radiation belt simulations are produced using the Versatile Electron Radiation Belt (VERB)-3D code. The VERB-3D code simulations incorporate radial, energy, and pitch angle diffusion to reproduce the radiation belts. Our simulations use the historical measurements of Kp (available since solar cycle 17, i.e., 1933) to model the evolution radiation belt dynamics between L* = 1-6.6. A nonlinear auto regressive network with exogenous inputs (NARX) neural network was trained off GOES 15 measurements (January 2011-March 2014) and used to supply the upper boundary condition (L* = 6.6) over the course of solar cycles 17-24 (i.e., 1933-2017). Comparison of the model with long term observations of the Van Allen Probes and CRRES demonstrates that our model, driven by the NARX boundary, can reconstruct the general evolution of the radiation belt fluxes. Solar cycle 24 (January 2008-2017) has been the least active of the considered solar cycles which resulted in unusually low electron fluxes. Our results show that solar cycle 24 should not be used as a representative solar cycle for developing long term environment models. The developed reconstruction of fluxes can be used to develop or improve empirical models of the radiation belts. Y1 - 2021 U6 - https://doi.org/10.1029/2020SW002524 SN - 1542-7390 VL - 19 IS - 3 PB - Wiley CY - New York ER - TY - JOUR A1 - Landis, Daji August A1 - Saikin, Anthony A1 - Zhelavskaya, Irina A1 - Drozdov, Alexander A1 - Aseev, Nikita A1 - Shprits, Yuri Y. A1 - Pfitzer, Maximilian F. A1 - Smirnov, Artem G. T1 - NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux JF - Space Weather: the international journal of research and applications N2 - We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES-15 measurements of 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling GOES-15 flux values and an upper boundary condition scaling factor (BF). The GOES-15 flux model utilizes an input and feedback delay of 2 and 2 time steps (i.e., 5 min time steps) with the most efficient number of hidden layers set to 10. Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, and solar wind velocity were found to perform as predicative indicators of GOES-15 flux and therefore were used as the exogenous inputs. The NARX-derived upper boundary condition scaling factor was used in conjunction with the Versatile Electron Radiation Belt (VERB) code to produce reconstructions of the radiation belts during the period of July-November 1990, independent of in-situ observations. Here, Kp was chosen as the sole exogenous input to be more compatible with the VERB code. This Combined Release and Radiation Effects Satellite-era reconstruction showcases the potential to use these neural network-derived boundary conditions as a method of hindcasting the historical radiation belts. This study serves as a companion paper to another recently published study on reconstructing the radiation belts during Solar Cycles 17-24 (Saikin et al., 2021, ), for which the results featured in this paper were used. KW - radiation belts KW - forecasting (1922, 4315, 7924, 7964) KW - machine learning (0555) Y1 - 2022 U6 - https://doi.org/10.1029/2021SW002774 SN - 1542-7390 VL - 20 IS - 5 PB - American Geophysical Union CY - Washington ER - TY - THES A1 - Zhelavskaya, Irina T1 - Modeling of the Plasmasphere Dynamics T1 - Modellierung der Plasmasphärendynamik N2 - The plasmasphere is a dynamic region of cold, dense plasma surrounding the Earth. Its shape and size are highly susceptible to variations in solar and geomagnetic conditions. Having an accurate model of plasma density in the plasmasphere is important for GNSS navigation and for predicting hazardous effects of radiation in space on spacecraft. The distribution of cold plasma and its dynamic dependence on solar wind and geomagnetic conditions remain, however, poorly quantified. Existing empirical models of plasma density tend to be oversimplified as they are based on statistical averages over static parameters. Understanding the global dynamics of the plasmasphere using observations from space remains a challenge, as existing density measurements are sparse and limited to locations where satellites can provide in-situ observations. In this dissertation, we demonstrate how such sparse electron density measurements can be used to reconstruct the global electron density distribution in the plasmasphere and capture its dynamic dependence on solar wind and geomagnetic conditions. First, we develop an automated algorithm to determine the electron density from in-situ measurements of the electric field on the Van Allen Probes spacecraft. In particular, we design a neural network to infer the upper hybrid resonance frequency from the dynamic spectrograms obtained with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite, which is then used to calculate the electron number density. The developed Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm is applied to more than four years of EMFISIS measurements to produce the publicly available electron density data set. We utilize the obtained electron density data set to develop a new global model of plasma density by employing a neural network-based modeling approach. In addition to the location, the model takes the time history of geomagnetic indices and location as inputs, and produces electron density in the equatorial plane as an output. It is extensively validated using in-situ density measurements from the Van Allen Probes mission, and also by comparing the predicted global evolution of the plasmasphere with the global IMAGE EUV images of He+ distribution. The model successfully reproduces erosion of the plasmasphere on the night side as well as plume formation and evolution, and agrees well with data. The performance of neural networks strongly depends on the availability of training data, which is limited during intervals of high geomagnetic activity. In order to provide reliable density predictions during such intervals, we can employ physics-based modeling. We develop a new approach for optimally combining the neural network- and physics-based models of the plasmasphere by means of data assimilation. The developed approach utilizes advantages of both neural network- and physics-based modeling and produces reliable global plasma density reconstructions for quiet, disturbed, and extreme geomagnetic conditions. Finally, we extend the developed machine learning-based tools and apply them to another important problem in the field of space weather, the prediction of the geomagnetic index Kp. 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, the radiation belts and the plasmasphere. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast and make short-term predictions of Kp, basing their inferences on the recent history of Kp and solar wind measurements at L1. We analyze how the performance of neural networks compares to other machine learning algorithms for nowcasting and forecasting Kp for up to 12 hours ahead. Additionally, we investigate several machine learning and information theory methods for selecting the optimal inputs to a predictive model of Kp. The developed tools for feature selection can also be applied to other problems in space physics in order to reduce the input dimensionality and identify the most important drivers. Research outlined in this dissertation clearly demonstrates that machine learning tools can be used to develop empirical models from sparse data and also can be used to understand the underlying physical processes. Combining machine learning, physics-based modeling and data assimilation allows us to develop novel methods benefiting from these different approaches. N2 - Die Plasmasphäre ist eine die Erde umgebende dynamische Region aus kaltem, dichtem Plasma. Ihre Form und Größe sind sehr anfällig für Schwankungen der solaren und geomagnetischen Bedingungen. Ein präzises Modell der Plasmadichte in der Plasmasphäre ist wichtig für die GNSS-Navigation und für die Vorhersage gefährlicher Auswirkungen der kosmischen Strahlung auf Raumfahrzeuge. Die Verteilung des kalten Plasmas und seine dynamische Abhängigkeit vom Sonnenwind und den geomagnetischen Bedingungen sind jedoch nach wie vor nur unzureichend quantifiziert. Bestehende empirische Modelle der Plasmadichte sind in der Regel zu stark vereinfacht, da sie auf statistischen Durchschnittswerten statischer Parameter basieren. Das Verständnis der globalen Dynamik der Plasmasphäre anhand von Beobachtungen aus dem Weltraum bleibt eine Herausforderung, da vorhandene Dichtemessungen spärlich sind und sich auf Orte beschränken, an denen Satelliten In-situ-Beobachtungen liefern können. In dieser Dissertation zeigen wir, wie solche spärlichen Elektronendichtemessungen verwendet werden können, um die globale Elektronendichteverteilung in der Plasmasphäre zu rekonstruieren und ihre dynamische Abhängigkeit vom Sonnenwind und den geomagnetischen Bedingungen zu erfassen. Zunächst entwickeln wir einen automatisierten Algorithmus zur Bestimmung der Elektronendichte aus In-situ-Messungen des elektrischen Feldes der Van Allen Probes Raumsonden. Insbesondere entwerfen wir ein neuronales Netzwerk, um die obere Hybridresonanzfrequenz aus den dynamischen Spektrogrammen abzuleiten, die wir durch die Instrumentensuite „Electric and Magnetic Field Instrument Suite“ (EMFISIS) erhielten, welche dann zur Berechnung der Elektronenzahldichte verwendet wird. Der entwickelte „Neural-network-based Upper Hybrid Resonance Determination“ (NURD)-Algorithmus wird auf mehr als vier Jahre der EMFISIS-Messungen angewendet, um den öffentlich verfügbaren Elektronendichte-Datensatz zu erstellen. Wir verwenden den erhaltenen Elektronendichte-Datensatz, um ein neues globales Modell der Plasmadichte zu entwickeln, indem wir einen auf einem neuronalen Netzwerk basierenden Modellierungsansatz verwenden. Zusätzlich zum Ort nimmt das Modell den zeitlichen Verlauf der geomagnetischen Indizes und des Ortes als Eingabe und erzeugt als Ausgabe die Elektronendichte in der äquatorialebene. Dies wird ausführlich anhand von In-situ-Dichtemessungen der Van Allen Probes-Mission und durch den Vergleich der vom Modell vorhergesagten globalen Entwicklung der Plasmasphäre mit den globalen IMAGE EUV-Bildern der He+ -Verteilung validiert. Das Modell reproduziert erfolgreich die Erosion der Plasmasphäre auf der Nachtseite sowie die Bildung und Entwicklung von Fahnen und stimmt gut mit den Daten überein. Die Leistung neuronaler Netze hängt stark von der Verfügbarkeit von Trainingsdaten ab, die für Intervalle hoher geomagnetischer Aktivität nur spärlich vorhanden sind. Um zuverlässige Dichtevorhersagen während solcher Intervalle zu liefern, können wir eine physikalische Modellierung verwenden. Wir entwickeln einen neuen Ansatz zur optimalen Kombination der neuronalen Netzwerk- und physikbasierenden Modelle der Plasmasphäre mittels Datenassimilation. Der entwickelte Ansatz nutzt sowohl die Vorteile neuronaler Netze als auch die physikalischen Modellierung und liefert zuverlässige Rekonstruktionen der globalen Plasmadichte für ruhige, gestörte und extreme geomagnetische Bedingungen. Schließlich erweitern wir die entwickelten auf maschinellem Lernen basierten Werkzeuge und wenden sie auf ein weiteres wichtiges Problem im Bereich des Weltraumwetters an, die Vorhersage des geomagnetischen Index Kp. Der Kp-Index ist einer der am häufigsten verwendeten Indikatoren für Weltraumwetterwarnungen und dient als Eingabe für verschiedene Modelle, z.B. für die Thermosphäre, die Strahlungsgürtel und die Plasmasphäre. Es ist daher wichtig, den Kp-Index genau vorherzusagen. Frühere Arbeiten in diesem Bereich verwendeten hauptsächlich künstliche neuronale Netze, um Kurzzeit-Kp-Vorhersagen zu treffen, wobei deren Schlussfolgerungen auf der jüngsten Vergangenheit von Kp- und Sonnenwindmessungen am L1-Punkt beruhten. Wir analysieren, wie sich die Leistung neuronaler Netze im Vergleich zu anderen Algorithmen für maschinelles Lernen verhält, um kurz- und längerfristige Kp-Voraussagen von bis zu 12 Stunden treffen zu können. Zusätzlich untersuchen wir verschiedene Methoden des maschinellen Lernens und der Informationstheorie zur Auswahl der optimalen Eingaben für ein Vorhersagemodell von Kp. Die entwickelten Werkzeuge zur Merkmalsauswahl können auch auf andere Probleme in der Weltraumphysik angewendet werden, um die Eingabedimensionalität zu reduzieren und die wichtigsten Treiber zu identifizieren. Die in dieser Dissertation skizzierten Untersuchungen zeigen deutlich, dass Werkzeuge für maschinelles Lernen sowohl zur Entwicklung empirischer Modelle aus spärlichen Daten als auch zum Verstehen zugrunde liegender physikalischer Prozesse genutzt werden können. Die Kombination von maschinellem Lernen, physikbasierter Modellierung und Datenassimilation ermöglicht es uns, kombinierte Methoden zu entwickeln, die von unterschiedlichen Ansätzen profitieren. KW - Plasmasphere KW - Inner magnetosphere KW - Neural networks KW - Machine learning KW - Modeling KW - Kp index KW - Geomagnetic activity KW - Data assimilation KW - Validation KW - IMAGE EUV KW - Kalman filter KW - Plasmasphäre KW - Innere Magnetosphäre KW - Neuronale Netze KW - Maschinelles Lernen KW - Modellieren KW - Forecasting KW - Kp-Index KW - Geomagnetische Aktivität KW - Datenassimilation KW - Validierung KW - Kalman Filter KW - Prognose Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-482433 ER - TY - JOUR A1 - Liemohn, Michael W. A1 - McCollough, James P. A1 - Jordanova, Vania K. A1 - Ngwira, Chigomezyo M. A1 - Morley, Steven K. A1 - Cid, Consuelo A1 - Tobiska, W. Kent A1 - Wintoft, Peter A1 - Ganushkina, Natalia Yu A1 - Welling, Daniel T. A1 - Bingham, Suzy A1 - Balikhin, Michael A. A1 - Opgenoorth, Hermann J. A1 - Engel, Miles A. A1 - Weigel, Robert S. A1 - Singer, Howard J. A1 - Buresova, Dalia A1 - Bruinsma, Sean A1 - Zhelavskaya, Irina A1 - Shprits, Yuri Y. A1 - Vasile, Ruggero T1 - Model Evaluation Guidelines for Geomagnetic Index Predictions JF - Space Weather: The International Journal of Research and Applications N2 - 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. Y1 - 2018 U6 - https://doi.org/10.1029/2018SW002067 SN - 1542-7390 VL - 16 IS - 12 SP - 2079 EP - 2102 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Smirnov, Artem A1 - Berrendorf, Max A1 - Shprits, Yuri Y. A1 - Kronberg, Elena A. A1 - Allison, Hayley J. A1 - Aseev, Nikita A1 - Zhelavskaya, Irina A1 - Morley, Steven K. A1 - Reeves, Geoffrey D. A1 - Carver, Matthew R. A1 - Effenberger, Frederic T1 - Medium energy electron flux in earth's outer radiation belt (MERLIN) BT - a Machine learning model JF - Space weather : the international journal of research and applications N2 - The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis. KW - machine learning KW - radiation belts KW - electron flux KW - empirical modeling KW - magnetosphere KW - electrons Y1 - 2020 U6 - https://doi.org/10.1029/2020SW002532 SN - 1542-7390 VL - 18 IS - 11 PB - American geophysical union, AGU CY - Washington ER - TY - JOUR A1 - Guo, Yingjie A1 - Ni, Binbin A1 - Fu, Song A1 - Wang, Dedong A1 - Shprits, Yuri Y. A1 - Zhelavskaya, Irina A1 - Feng, Minghang A1 - Guo, Deyu T1 - Identification of controlling geomagnetic and solar wind factors for magnetospheric chorus intensity using feature selection techniques JF - Journal of geophysical research : A, Space physics N2 - Using over-5-year EMFISIS wave measurements from Van Allen Probes, we present a detailed survey to identify the controlling factors among the geomagnetic indices and solar wind parameters for the 1-min root mean square amplitudes of lower band chorus (LBC) and upper band chorus (UBC). A set of important features are automatically determined by feature selection techniques, namely, Random Forest and Maximum Relevancy Minimum Redundancy. Our analysis results indicate the AE index with zero-time-delay dominates the intensity evolution of LBC and UBC, consistent with the evidence that chorus waves prefer to occur and amplify during enhanced substorm periods. Regarding solar wind parameters, solar wind speed and IMF B-z are identified as the controlling factors for chorus wave intensity. Using the combination of all these important features, a predictive neural network model of chorus wave intensity is established to reconstruct the temporal variations of chorus wave intensity, for which application of Random Forest produces the overall best performance. Plain Language Summary Whistler mode chorus waves are electromagnetic waves observed in the low-density region near the geomagnetic equator outside the plasmapause. The dynamics of Earth's radiation belts are largely influenced by chorus waves owing to their dual contributions to both radiation belt electron acceleration and loss. In this study, we use feature selection techniques to identify the controlling geomagnetic and solar wind factors for magnetospheric chorus waves. Feature selection techniques implement the processes which can select the features most influential to the output. In this study, the inputs are geomagnetic indices and solar wind parameters and the output is the chorus wave intensity. The results indicate that AE index with zerotime delay dominates the chorus wave intensity. Furthermore, solar wind speed and IMF B-z are identified as the most important solar wind drivers for chorus wave intensity. On basis of the combination of all these important geomagnetic and solar wind controlling factors, we develop a neural network model of chorus wave intensity, and find that the model with the inputs identified using the Random Forest method produces the overall best performance. Y1 - 2021 U6 - https://doi.org/10.1029/2021JA029926 SN - 2169-9380 SN - 2169-9402 VL - 127 IS - 1 PB - Wiley CY - Hoboken, NJ ER - TY - JOUR A1 - Zhelavskaya, Irina A1 - Shprits, Yuri Y. A1 - Spasojevic, Maria T1 - Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks JF - Journal of geophysical research : Space physics N2 - 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. Y1 - 2017 U6 - https://doi.org/10.1002/2017JA024406 SN - 2169-9380 SN - 2169-9402 VL - 122 SP - 11227 EP - 11244 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Shprits, Yuri Y. A1 - Zhelavskaya, Irina A1 - Green, Janet C. A1 - Pulkkinen, Antti A. A1 - Horne, Richard B. A1 - Pitchford, David A1 - Glover, Alexi T1 - Discussions on Stakeholder Requirements for Space Weather-Related Models T2 - Space Weather: The International Journal of Research and Applications N2 - Participants of the 2017 European Space Weather Week in Ostend, Belgium, discussed the stakeholder requirements for space weather-related models. It was emphasized that stakeholders show an increased interest in space weather-related models. Participants of the meeting discussed particular prediction indicators that can provide first-order estimates of the impact of space weather on engineering systems. KW - 7924 KW - 7934 KW - 7959 Y1 - 2018 U6 - https://doi.org/10.1002/2018SW001864 SN - 1542-7390 VL - 16 IS - 4 SP - 341 EP - 342 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Zhelavskaya, Irina A1 - Spasojevic, M. A1 - Shprits, Yuri Y. A1 - Kurth, William S. T1 - Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft JF - Journal of geophysical research : Space physics N2 - We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA's Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, fuhr, from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model. KW - Van Allen Probes KW - electron number density KW - neural networks Y1 - 2016 U6 - https://doi.org/10.1002/2015JA022132 SN - 2169-9380 SN - 2169-9402 VL - 121 SP - 4611 EP - 4625 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Pick, Leonie A1 - Effenberger, Frederic A1 - Zhelavskaya, Irina A1 - Korte, Monika T1 - A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements JF - Earth and Space Science N2 - Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003) KW - geomagnetic observatory data KW - geomagnetic storm drivers KW - historical geomagnetic storms KW - supervised machine learning Y1 - 2019 U6 - https://doi.org/10.1029/2019EA000726 SN - 2333-5084 VL - 6 SP - 2000 EP - 2015 PB - American Geophysical Union CY - Malden, Mass. ER - TY - GEN A1 - Pick, Leonie A1 - Effenberger, Frederic A1 - Zhelavskaya, Irina A1 - Korte, Monika T1 - A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003) T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 982 KW - geomagnetic observatory data KW - geomagnetic storm drivers KW - historical geomagnetic storms KW - supervised machine learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-474996 SN - 1866-8372 IS - 982 SP - 2000 EP - 2015 ER - TY - JOUR A1 - Shprits, Yuri Y. A1 - Allison, Hayley J. A1 - Wang, Dedong A1 - Drozdov, Alexander A1 - Szabo-Roberts, Matyas A1 - Zhelavskaya, Irina A1 - Vasile, Ruggero T1 - A new population of ultra-relativistic electrons in the outer radiation zone JF - Journal of geophysical research : Space physics N2 - Van Allen Probes measurements revealed the presence of the most unusual structures in the ultra-relativistic radiation belts. Detailed modeling, analysis of pitch angle distributions, analysis of the difference between relativistic and ultra-realistic electron evolution, along with theoretical studies of the scattering and wave growth, all indicate that electromagnetic ion cyclotron (EMIC) waves can produce a very efficient loss of the ultra-relativistic electrons in the heart of the radiation belts. Moreover, a detailed analysis of the profiles of phase space densities provides direct evidence for localized loss by EMIC waves. The evolution of multi-MeV fluxes shows dramatic and very sudden enhancements of electrons for selected storms. Analysis of phase space density profiles reveals that growing peaks at different values of the first invariant are formed at approximately the same radial distance from the Earth and show the sequential formation of the peaks from lower to higher energies, indicating that local energy diffusion is the dominant source of the acceleration from MeV to multi-MeV energies. Further simultaneous analysis of the background density and ultra-relativistic electron fluxes shows that the acceleration to multi-MeV energies only occurs when plasma density is significantly depleted outside of the plasmasphere, which is consistent with the modeling of acceleration due to chorus waves. KW - radiation belts KW - ultra-relativistic electrons KW - EMIC waves KW - modeling; KW - plasma density KW - chorus waves Y1 - 2022 U6 - https://doi.org/10.1029/2021JA030214 SN - 2169-9380 SN - 2169-9402 VL - 127 IS - 5 PB - American Geophysical Union CY - Washington ER -