Numerical prediction of sporadic E layer occurrence using GAIA

Hiroyuki Shinagawa, Chihiro Tao, Hidekatsu Jin, Yasunobu Miyoshi, Hitoshi Fujiwara

Research output: Contribution to journalArticlepeer-review

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.[Figure not available: see fulltext.].

Original languageEnglish
Article number28
Journalearth, planets and space
Volume73
Issue number1
DOIs
Publication statusPublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Geology
  • Space and Planetary Science

Fingerprint Dive into the research topics of 'Numerical prediction of sporadic E layer occurrence using GAIA'. Together they form a unique fingerprint.

Cite this