Bibliography

Modeling of Ultraviolet Aurora Intensity Associated With Interplanetary and Geomagnetic Parameters Based on Neural Networks

The spatial distribution of aurora intensity is an important manifestation of solar wind-magnetosphere-ionosphere energy coupling process, and it oscillates with the change of space environment parameters and geomagnetic index. It is of great significance to establish an appropriate aurora intensity model for the prediction of space weather and the study of magnetosphere dynamics. Based on Ultraviolet Imager (UVI) data of Polar satellite, we constructed two auroral models by using two different neural networks, that is, the generalized regression neural network (GRNN), and the conditional generation adversarial network (CGAN). Input parameters of the models include interplanetary magnetic field, solar wind velocity and density, and the geomagnetic AE index. Output result is the spatial distribution of auroral intensity in altitude adjusted corrected geomagnetic (AACGM) coordinates. The structural similarity index (SSIM) of image quality is used as an evaluation standard of detail similarity between the prediction results of auroral intensity model and corresponding UVI images (complete similarity is 1, dissimilarity is 0, SSIM is generally considered to have good similarity if it is greater than 0.5). Based on the respective training datasets of GRNN and CGAN models, the evaluating results showed that the mean values (standard deviation) of SSIM were 0.5409 (0.0912) and 0.5876 (0.0712), respectively, so the prediction results from both models can restore the auroral intensity distribution of the original images of UVI. In addition, the value of SSIM can increase with the increase of the number of training data. Therefore, more training data will help improve the effectiveness of these models.

Hu, Ze-Jun; Han, Bing; Zhang, Yisheng; Lian, Huifang; Wang, Ping; Li, Guojun; Li, Bin; Chen, Xiang-Cai; Liu, Jian-Jun;

Published by: Space Weather      Published on:

YEAR: 2021     DOI: 10.1029/2021SW002751

conditional generation adversarial network; generalized regression neural network; interplanetary and geomagnetic parameters; neural networks; ultraviolet auroral intensity model

Photoelectron transport and associated Far Ultraviolet emissions: Model simulation and comparison with observations

Liang, Jun; Sydorenko, Dmytro; Donovan, Eric; Rankin, Robert;

Published by:       Published on:

YEAR: 2021     DOI:

Impacts of SABER CO ₂ -based eddy diffusion coefficients in the lower thermosphere on the ionosphere/thermosphere

This work estimates global-mean K_zz using Sounding of the Atmosphere using Broadband Emission Radiometry/Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics monthly global-mean CO₂ profiles and a one-dimensional transport model. It is then specified as a lower boundary into the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). Results first show that global-mean CO₂ in the mesosphere and lower thermosphere region has annual and semiannual oscillations (AO and SAO) with maxima during solstice seasons along with a primary maximum in boreal summer. Our calculated AO and SAO in global-mean CO₂ are then modeled by AO and SAO in global-mean K_zz. It is then shown that our estimated global-mean K_zz is lower in magnitude than the suggested global-mean K_zz from Qian et al. (2009) that can model the observed AO and SAO in the ionosphere/thermosphere (IT) region. However, our estimated global-mean K_zz is similar in magnitude with recent suggestions of global-mean K_zz in models with explicit gravity wave parameterization. Our work therefore concludes that global-mean K_zz from global-mean CO₂ profiles cannot model the observed AO and SAO in the IT region because our estimated global-mean K_zz may only be representing eddy diffusion due to gravity wave breaking. The difference between our estimated global-mean K_zz and the global-mean K_zz from Qian et al. (2009) thus represents diffusion and mixing from other nongravity wave sources not directly accounted for in the TIE-GCM lower boundary conditions. These other sources may well be the more dominant lower atmospheric forcing behind the AO and SAO in the IT region.

Salinas, Cornelius; Chang, Loren; Liang, Mao-Chang; Yue, Jia; Russell, James; Mlynczak, Martin;

Published by: Journal of Geophysical Research: Space Physics      Published on: 11/2016

YEAR: 2016     DOI: 10.1002/2016JA023161

Impacts of SABER CO2-based eddy diffusion coefficients in the lower thermosphere on the ionosphere/thermosphere

This work estimates global‐mean K zz using Sounding of the Atmosphere using Broadband Emission Radiometry/Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics

Salinas, Cornelius; Chang, Loren; Liang, Mao-Chang; Yue, Jia; , Russell; Mlynczak, Martin;

Published by: Journal of Geophysical Research: Space Physics      Published on:

YEAR: 2016     DOI: 10.1002/2016JA023161

Ionospheric control of auroral occurrence

Kan, Liou; Yongliang, Zhang; Patrick, Newell; Larry, Paxton;

Published by:       Published on:

YEAR: 2009     DOI:

Coordinated GUVI, ISR, and imaging observations of the auroral boundary over Sondrestrom

Doe, RA; Thayer, JP; Semeter, JL; McCready, M; Paxton, L; Liang, Y; Christensen, A;

Published by:       Published on:

YEAR: 2002     DOI: