Search for: All records

A new version of the US National Science Foundation National Center forAtmospheric Research (NSF NCAR) thermosphere-ionosphere-electrodynamicsgeneral circulation model (TIEGCM) has been developed and released. Thispaper describes the changes and improvements of the new version 3.0since its last major release (2.0) in 2016. These include: 1) increasingthe model resolution in both the horizontal and vertical dimensions, aswell as the ionospheric dynamo solver; 2) upward extension of the modelupper boundary to enable more accurate simulations of the topsideionosphere and neutral density in the lower exosphere; 3) improvedparameterization for thermal electron heating rate; 4) resolvingtransport of minor species N(2D); 5) treating helium as a major species;6) parameterization for additional physical processes, such as SAPS andelectrojet turbulent heating; 7) including parallel ion drag in theneutral momentum equation; 8) nudging of prognostic fields near thelower boundary from external data; 9) modification to the NO reactionrate and auroral heating rate; 10) outputs of diagnostic analysis termsof the equations; 11) new functionalities enabling model simulations ofcertain recurrent phenomena, such as solar flares and eclipse. Wepresent examples of the model validation during a moderate storm andcompare simulation results by turning on/off new functionalities todemonstrate the related new model capabilities. Furthermore, the modelis upgraded to comply with the new computer software environment at NSFNCAR for easy installation and run setup and with new visualizationtools. Finally, the model limitations and future development plans arediscussed. 

Abstract This study presents a data‐driven approach to quantify uncertainties in the ionosphere‐thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low‐density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance‐based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system. 

The specification and prediction of density fluctuations in the thermosphere, especially during geomagnetic storms, is a key challenge for space weather observations and modeling. It is of great operational importance for tracking objects orbiting in near-Earth space. For low-Earth orbit, variations in neutral density represent the most important uncertainty for propagation and prediction of satellite orbits. An international conference in 2018 conducted under the auspices of the NASA Community Coordinated Modeling Center (CCMC) included a workshop on neutral density modeling, using both empirical and numerical methods, and resulted in the organization of an initial effort of model comparison and evaluation. Here, we present an updated metric for model assessment under geomagnetic storm conditions by dividing a storm in four phases with respect to the time of minimum Dst and then calculating the mean density ratios and standard deviations and correlations. Comparisons between three empirical (NRLMSISE-00, JB2008 and DTM2013) and two first-principles models (TIE-GCM and CTIPe) and neutral density data sets that include measurements by the CHAMP, GRACE, and GOCE satellites for 13 storms are presented. The models all show reduced performance during storms, notably much increased standard deviations, but DTM2013, JB2008 and CTIPe did not on average reveal a significant bias in the four phases of our metric. DTM2013 and TIE-GCM driven with the Weimer model achieved the best results taking the entire storm event into account, while NRLMSISE-00 systematically and significantly underestimates the storm densities. Numerical models are still catching up to empirical methods on a statistical basis, but as their drivers become more accurate and they become available at higher resolutions, they will surpass them in the foreseeable future.