Numerical forecast of the upper atmosphere and ionosphere using GAIA

Upper atmospheric conditions are crucial for the safe operation of spacecraft orbiting near Earth and for communication and positioning systems using radio signals. To understand and predict the upper atmospheric conditions, which include complex variations affected by both low altitude and upper su...

Full description

Saved in:
Bibliographic Details
Published in:Earth, planets, and space planets, and space, 2020-11, Vol.72 (1), Article 178
Main Authors: Tao, Chihiro, Jin, Hidekatsu, Miyoshi, Yasunobu, Shinagawa, Hiroyuki, Fujiwara, Hitoshi, Nishioka, Michi, Ishii, Mamoru
Format: Article
Language:English
Subjects:
3. Space science
Aeronomy
Atmosphere
Atmosphere, Upper
Earth and Environmental Science
Earth Sciences
Forecasts and trends
Geology
Geophysics/Geodesy
Ionosphere
Ionospheric electron content
Ionospheric models
Ionospheric storms
Lead time
Low altitude
Mathematical models
Meteorological data
Natural history
Numerical forecasting
Radio signals
Simulation
Solar-Terrestrial Environment Prediction: Toward the Synergy of Science and Forecasting Operation of Space Weather and Space Climate
Space weather
Spacecraft
Stratospheric warming
Total Electron Content
Upper atmosphere
Weather forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Upper atmospheric conditions are crucial for the safe operation of spacecraft orbiting near Earth and for communication and positioning systems using radio signals. To understand and predict the upper atmospheric conditions, which include complex variations affected by both low altitude and upper surrounding environments, we have developed a quasi-real-time and forecast simulations using a physical global model, the Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy (GAIA). The GAIA simulation system provides a global distribution of ionospheric total electron content (TEC) with background atmospheric and electric distributions including a few-days prediction. The prediction accuracy for the detection of significant ionospheric storms decreases with increasing lead time, i.e., the duration of the model simulation which is not constrained by realistic input parameters. Similar characteristic variations associated with sudden stratospheric warmings (SSWs) are reproduced with the full or limited input of meteorological data at least the prior 3 days. This is a first step toward the usage of GAIA for space weather forecasting.
ISSN:1880-5981
1343-8832
1880-5981
DOI:10.1186/s40623-020-01307-x