Empirical Modeling of the Geomagnetosphere for SIR and CME‐Driven Magnetic Storms

Abstract

During geomagnetic disturbances, the solar wind arrives in the form of characteristic sequences lasting from tens of hours to days. The most important magnetic storm drivers are the coronal mass ejections (CMEs) and the slow‐fast stream interaction regions (SIRs). Previous data‐based magnetic field models did not distinguish between these types of the solar wind driving. In the present work we retained the basic structure of the Tsyganenko and Andreeva (2015) model but fitted it to data samples corresponding to (1) SIR‐driven storms, (2) CME‐driven storms preceded with a shock ahead of the CME, and (3) CME‐driven storms without such shocks. The storm time dynamics of the model current systems has been represented using the parametrization method developed by Tsyganenko and Sitnov (2005), based on dynamical variables Wi, calculated from concurrent solar wind characteristics and their previous history. The database included observations of THEMIS, Polar, Cluster, Geotail, and Van Allen Probes missions during 155 storms in 1997–2016. The model current systems drastically differ from each other with respect to decay rate and total current magnitudes. During SIR‐induced storms, all current systems saturate, while during CME‐induced disturbances, the saturation occurs only for the symmetric ring current and the tail current. The partial ring current parameters are drastically different between SIR‐ and CME‐induced storm sets. In the case of SIR‐driven storms, the total partial ring current is comparable with symmetric ring current, whereas for all CME‐induced events it is nearly twice higher. The results are compared with GOES 15 magnetometer observations.

Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach

“…That explains the usefulness of combining the two models. The normalization layer in the models plays an important role to keep the scale of the feature quantities as represented in the input time series to get a successful training and a small regularization. Batch normalization layer performs not well and that is because it normalizes the data within the input batch intervals and sometimes it vanishes the IRs' quantities of some intervals when there is high variance in the quantities of other intervals (Andreeva & Tsyganenko, 2019; Heinemann et al., 2019). That mostly happens when the input batch contains a mix of IR intervals during solar minimum and solar maximum.…”

On the Role of Whistler‐Mode Waves in Electron Interaction With Dipolarizing Flux Bundles

“…Electron scattering by whistlers may significantly decrease electron pitch‐angles and move particles to the loss‐cone. An accurate estimate of the loss‐cone size for the near‐Earth magnetotail is almost impossible, because empirical magnetic field models (Andreeva & Tsyganenko, 2019; Tsyganenko, 1995; Tsyganenko & Sitnov, 2007), with only few exceptions (Sitnov et al., 2021; Stephens et al., 2019; Tsyganenko et al., 2021), do not include the effects of dipolarization associated with plasma injection and strong variations of the equatorial magnetic field (such variations may significantly change the loss‐cone size and precipitating electron fluxes, see, e.g., Eshetu et al., 2018). Typical loss‐cone angle α LC estimates give ≤2° in the magnetotail (see Figure 3d in Zhang et al., 2015), and this value can be larger closer to the Earth.…”

Parametrization of Energetic Ion and Electron Fluxes in the Near‐Earth Magnetotail

“…This approach incorporates the magnetic field dynamics (and corresponding L ‐shell variations) caused by plasma sheet motion and reconfigurations. Moreover, this approach allows us to merge the energetic particle flux parametrization with empirical magnetic field models, parametrized by the geomagnetic indices and their time derivatives (Andreeva & Tsyganenko, 2018, 2019; Sitnov et al., 2019; Stephens et al., 2019). The accuracy of the empirical models has been rapidly improved during the last decade, and thus, our parametrization may highly benefit from such merging.…”