Abstract

A sporadic E layer has significant influence on radio communications and broadcasting, and predicting the occurrence of sporadic E layers is one of the most important issues in space weather forecast. While sporadic E layer occurrence and the magnitude of the critical sporadic E frequency (foEs) have clear seasonal variations, significant day-to-day variations as well as regional and temporal variations also occur. Because of the highly complex behavior of sporadic E layers, the prediction of sporadic E layer occurrence has been one of the most difficult issues in space weather forecast. To explore the possibility of numerically predicting sporadic E layer occurrence, we employed the whole atmosphere–ionosphere coupled model GAIA, examining parameters related to the formation of sporadic E layer such as vertical ions velocities and vertical ion convergences at different altitudes between 90 and 150 km. Those parameters in GAIA were compared with the observed foEs data obtained by ionosonde observations in Japan. Although the agreement is not very good in the present version of GAIA, the results suggest a possibility that sporadic E layer occurrence can be numerically predicted using the parameters derived from GAIA. We have recently developed a real-time GAIA simulation system that can predict atmosphere–ionosphere conditions for a few days ahead. We present an experimental prediction scheme and a preliminary result for predicting sporadic E layer occurrence.

Details

Title
Numerical prediction of sporadic E layer occurrence using GAIA
Author
Shinagawa Hiroyuki 1   VIAFID ORCID Logo  ; Tao Chihiro 1 ; Jin Hidekatsu 1 ; Miyoshi Yasunobu 2 ; Fujiwara Hitoshi 3 

 National Institute of Information and Communications Technology, Space Environment Laboratory, Applied Electromagnetic Research Institute, Tokyo, Japan (GRID:grid.28312.3a) (ISNI:0000 0001 0590 0962) 
 Kyushu University, Department of Earth and Planetary Sciences, Fukuoka, Japan (GRID:grid.177174.3) (ISNI:0000 0001 2242 4849) 
 Seikei University, Faculty of Science and Technology, Tokyo, Japan (GRID:grid.263319.c) (ISNI:0000 0001 0659 8312) 
Publication year
2021
Publication date
Jan 2021
Publisher
Springer Nature B.V.
e-ISSN
18805981
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2480986988
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.