11:00 AM - 1:00 PM
[PEM09-P07] Improvement of GAIA using foEs data obtained by ionosonde observations
Keywords:GAIA, improvement, foEs, mesosphere, thermosphere, neutral wind
The neutral wind in the region between the upper mesosphere and the lower thermosphere has significant influences on the ionosphere through ionospheric dynamo processes. The neutral wind also affects the behavior of sporadic E layers and equatorial plasma bubbles. Therefore, it is one of the most important issues in space weather forecasting to reproduce and predict the dynamics of the neutral wind in the region. Recent development of observational techniques of the upper atmosphere has greatly increased our understanding of the structure and dynamics of the ionosphere and the thermosphere. However, dynamics of the upper mesosphere and the lower thermosphere has not been fully understood because of the difficulty of the observations in those regions.
Although various whole atmosphere models have been constructed to compensate for the lack of observations, the models contain significant uncertainties in the dynamics of the regions because of some uncertain parameters such as the eddy diffusion coefficient and the gravity wave drag. Furthermore, the models need to incorporate meteorological reanalysis data in the lower atmosphere, but the way to include the reanalysis data has also arbitrary factors. Indeed, there are considerable discrepancies between the present whole atmosphere models for the neutral wind in the upper mesosphere and the lower thermosphere.
In this presentation, we will propose a new method to improve a whole atmosphere model using foEs data obtained by ionosonde observations. The method is based on the fact that the vertical ion convergence (VIC) in the E region has a good correlation with foEs, which was shown by Shinagawa et al. (2021). In the analysis, we first calculate VIC using GAIA for various parameters and approximations. Then, we compare the correlations between foEs and VIC for the results to find the best combination of parameters in GAIA. We found that some set of parameters could give significantly better correlations than others. We will also present the best parameters and some simulation results obtained from our study.
Reference
Shinagawa, H., C. Tao, H. Jin, Y. Miyoshi, H. Fujiwara (2021), Numerical prediction of sporadic E layer occurrence using GAIA, Earth Planets Space, 73:28. https://doi.org/10.1186/s40623-020-01330-y
Although various whole atmosphere models have been constructed to compensate for the lack of observations, the models contain significant uncertainties in the dynamics of the regions because of some uncertain parameters such as the eddy diffusion coefficient and the gravity wave drag. Furthermore, the models need to incorporate meteorological reanalysis data in the lower atmosphere, but the way to include the reanalysis data has also arbitrary factors. Indeed, there are considerable discrepancies between the present whole atmosphere models for the neutral wind in the upper mesosphere and the lower thermosphere.
In this presentation, we will propose a new method to improve a whole atmosphere model using foEs data obtained by ionosonde observations. The method is based on the fact that the vertical ion convergence (VIC) in the E region has a good correlation with foEs, which was shown by Shinagawa et al. (2021). In the analysis, we first calculate VIC using GAIA for various parameters and approximations. Then, we compare the correlations between foEs and VIC for the results to find the best combination of parameters in GAIA. We found that some set of parameters could give significantly better correlations than others. We will also present the best parameters and some simulation results obtained from our study.
Reference
Shinagawa, H., C. Tao, H. Jin, Y. Miyoshi, H. Fujiwara (2021), Numerical prediction of sporadic E layer occurrence using GAIA, Earth Planets Space, 73:28. https://doi.org/10.1186/s40623-020-01330-y