Sergeev, V. A., Shukhtina, M. A., Stepanov, N. A., Rogov, D. D., Nikolaev, A. V., Spanswick, E., et al. ( 2020). Toward the reconstruction of substorm‐related dynamical pattern of the radiowave auroral absorption. Space Weather, 18, e2019SW002385. https://doi.org/10.1029/2019SW002385
Toward the reconstruction of substorm‐related dynamical pattern of the radiowave auroral absorption
|Author:||Sergeev, V. A.1; Shukhtina, M. A.1; Stepanov, N. A.1,2;|
1Department of Earth Physics, St. Petersburg State University, St. Petersburg, Russia
2Department of Geophysics, Arctic and Antarctic Research Institute, St. Petersburg, Russia
3Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada
4Sodankyla Geophysical Observatory, University of Oulu, Sodankyla, Finland
|Online Access:||PDF Full Text (PDF, 5.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020050424742
American Geophysical Union,
|Publish Date:|| 2020-05-04
In addition to existing empirical models describing the average distributions of energetic electron precipitation into the auroral ionosphere at different activity levels, we develop and test a semiempirical approach to construct dynamical models describing the recurrent features of spatiotemporal development of auroral absorption in the ionosphere during individual substorms. Its key ingredients are (a) usage of linear prediction filter technique to extract from riometer data the response function to the injection of unit magnitude and (b) characterization of injection parameters by midlatitude magnetic variations caused by the substorm current wedge. Using global riometer network we test the method performance for stations in the middle of auroral zone (at corrected geomagnetic latitudes of 65–67°) where generally the absorption amplitude is largest. In this paper we use the midlatitude positive bay index, recently developed by X. Chu and R. McPherron, to drive the model. We evaluate the model performance, discuss the dynamical properties of energetic electron precipitation as revealed by the linear prediction filter response function analyses, and finally, we discuss possible future improvements of this method intended for both science and applications.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
115 Astronomy and space science
114 Physical sciences
This research was supported by Russian Fund for Basic Research grant 19‐05‐00072.
© 2020. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.