TY - JOUR A1 - Shprits, Yuri Y. A1 - Vasile, Ruggero A1 - Zhelayskaya, Irina S. T1 - Nowcasting and Predicting the Kp Index Using Historical Values and Real-Time Observations JF - Space Weather: The International Journal of Research and Applications N2 - 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. KW - Kp index KW - geomagnetic activity KW - empirical prediction KW - solar wind KW - forecast KW - AI Y1 - 2019 U6 - https://doi.org/10.1029/2018SW002141 SN - 1542-7390 VL - 17 IS - 8 SP - 1219 EP - 1229 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Wang, Dedong A1 - Shprits, Yuri Y. A1 - Zhelayskaya, Irina S. A1 - Agapitov, Oleksiy A1 - Drozdov, Alexander A1 - Aseev, Nikita T1 - Analytical chorus wave model derived from van Allen Probe Observations JF - Journal of geophysical research : Space physics N2 - Chorus waves play an important role in the dynamic evolution of energetic electrons in the Earth's radiation belts and ring current. Using more than 5 years of Van Allen Probe data, we developed a new analytical model for upper‐band chorus (UBC; 0.5fce < f < fce) and lower‐band chorus (LBC; 0.05fce < f < 0.5fce) waves, where fce is the equatorial electron gyrofrequency. By applying polynomial fits to chorus wave root mean square amplitudes, we developed regression models for LBC and UBC as a function of geomagnetic activity (Kp), L, magnetic latitude (λ), and magnetic local time (MLT). Dependence on Kp is separated from the dependence on λ, L, and MLT as Kp‐scaling law to simplify the calculation of diffusion coefficients and inclusion into particle tracing codes. Frequency models for UBC and LBC are also developed, which depends on MLT and magnetic latitude. This empirical model is valid in all MLTs, magnetic latitude up to 20°, Kp ≤ 6, L‐shell range from 3.5 to 6 for LBC and from 4 to 6 for UBC. The dependence of root mean square amplitudes on L are different for different bands, which implies different energy sources for different wave bands. This analytical chorus wave model is convenient for inclusion in quasi‐linear diffusion calculations of electron scattering rates and particle simulations in the inner magnetosphere, especially for the newly developed four‐dimensional codes, which require significantly improved wave parameterizations. KW - chorus waves KW - radiation belt electrons KW - ring current electrons KW - analytical model KW - wave-particle interactions KW - diffusion coefficients Y1 - 2019 U6 - https://doi.org/10.1029/2018JA026183 SN - 2169-9380 SN - 2169-9402 VL - 124 IS - 2 SP - 1063 EP - 1084 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Zhelayskaya, Irina S. A1 - Vasile, Ruggero A1 - Shprits, Yuri Y. A1 - Stolle, Claudia A1 - Matzka, Jürgen T1 - Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index JF - Space Weather: The International Journal of Research and Applications N2 - 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. KW - Kp index KW - Predictive models KW - Feature selection KW - Machine learning KW - Validation Y1 - 2019 U6 - https://doi.org/10.1029/2019SW002271 SN - 1542-7390 VL - 17 IS - 10 SP - 1461 EP - 1486 PB - American Geophysical Union CY - Washington ER -