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 - Zhelavskaya, Irina A1 - Aseev, Nikita A1 - Shprits, Yuri T1 - A combined neural network‐ and physics‐based approach for modeling plasmasphere dynamics JF - JGR / AGU, American Geographical Union. Space Physics N2 - Abstract In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network‐based models capture the large‐scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non‐existent, as occurs during geomagnetic storms, the performance of NNs significantly decreases, as networks inherently cannot learn from the limited number of examples. This limitation can be overcome by employing physics‐based modeling during strong geomagnetic storms. Physics‐based models show a stable performance during periods of disturbed geomagnetic activity if they are correctly initialized and configured. In this study, we illustrate how to combine the neural network‐ and physics‐based models of the plasmasphere in an optimal way by using data assimilation. The proposed approach utilizes advantages of both neural network‐ and physics‐based modeling and produces global plasma density reconstructions for both quiet and disturbed geomagnetic activity, including extreme geomagnetic storms. We validate the models quantitatively by comparing their output to the in‐situ density measurements from RBSP‐A for an 18‐month out‐of‐sample period from June 30, 2016 to January 01, 2018 and computing performance metrics. To validate the global density reconstructions qualitatively, we compare them to the IMAGE EUV images of the He+ particle distribution in the Earth's plasmasphere for a number of events in the past, including the Halloween storm in 2003. KW - data assimilation KW - Kalman filter KW - machine learning KW - neural networks KW - plasmasphere KW - plasma density Y1 - 2021 U6 - https://doi.org/10.1029/2020JA028077 SN - 2169-9380 SN - 2169-9402 VL - 126 IS - 3 PB - Wiley CY - Hoboken, NJ ER - TY - JOUR A1 - Saikin, Anthony A1 - Shprits, Yuri 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 - Guo, Yingjie A1 - Ni, Binbin A1 - Fu, Song A1 - Wang, Dedong A1 - Shprits, Yuri 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 - Del Corpo, Alfredo A1 - Vellante, Massimo A1 - Zhelavskaya, Irina A1 - Shprits, Yuri 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 - Shprits, Yuri 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 - GEN A1 - Shprits, Yuri 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 - Smirnov, Artem A1 - Berrendorf, Max A1 - Shprits, Yuri 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 - Landis, Daji August A1 - Saikin, Anthony A1 - Zhelavskaya, Irina A1 - Drozdov, Alexander A1 - Aseev, Nikita A1 - Shprits, Yuri 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 -