4.2 Article

Numerical prediction of sporadic E layer occurrence using GAIA

Journal

EARTH PLANETS AND SPACE
Volume 73, Issue 1, Pages -

Publisher

SPRINGER
DOI: 10.1186/s40623-020-01330-y

Keywords

Sporadic E layer; Wind shear; Occurrence; Prediction; GAIA; Model; Vertical ion convergence

Funding

  1. JSPS KAKENHI [JP15H03733, JP15H05815]
  2. Ministry of Internal Affairs and Communications, Japan [0155-0133]

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The sporadic E layer plays a significant role in radio communications and broadcasting, making it crucial to predict its occurrence in space weather forecasts. Despite clear seasonal variations, day-to-day and regional/temporal variations in sporadic E layer occurrence pose challenges. Researchers are exploring the use of the GAIA model to numerically predict sporadic E layer occurrence, indicating a potential for improved forecasting capabilities.
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.

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