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We analyze an episode of strong mountain wave (MW) activity over the western US from 9 to 12 January 2017 using the HIgh Altitude mechanistic General Circulation Model. We find that medium‐scale MWs were generated by strong eastward flow over the Sierra Nevada and the Rocky Mountains. During this time, part of the stratospheric polar vortex jet extended from the western US to eastern Canada such that the MWs propagated into the lower mesosphere where they dissipated from westward vertical wind shear. This resulted in secondary gravity waves (GWs) that propagated into the lower thermosphere where tertiary GWs having concentric ring structures were created. With increasing altitude in the thermosphere, certain propagation directions were highlighted as a result of the dissipation induced by the tidal winds. At 260 km, we find eastward propagation during local morning over the northeastern US, equatorward propagation around local noon over the southern US, westward propagation during local afternoon over the northwestern US, and poleward propagation over Canada after local midnight. In addition, the model shows equatorward propagating larger‐scale GWs over Canada from remote sources around local noon. The simulated regional GW‐mean flow interaction patterns are consistent with multi‐step vertical coupling triggered by the MWs. The traveling ionospheric disturbances (TIDs) during the MW event are simulated with the ionospheric model SAMI3. The simulated GWs and TIDs are consistent with the medium‐to‐large‐scale TIDs observed over the continental US in GPS TEC data. 

Abstract New, open access tools have been developed to validate ionospheric models in terms of technologically relevant metrics. These are ionospheric errors on GPS 3D position, HF ham radio communications, and peak F‐region density. To demonstrate these tools, we have used output from Sami is Another Model of the Ionosphere (SAMI3) driven by high‐latitude electric potentials derived from Active Magnetosphere and Planetary Electrodynamics Response Experiment, covering the first available month of operation using Iridium‐NEXT data (March 2019). Output of this model is now available for visualization and download viahttps://sami3.jhuapl.edu. The GPS test indicates SAMI3 reduces ionospheric errors on 3D position solutions from 1.9 m with no model to 1.6 m on average (maximum error: 14.2 m without correction, 13.9 m with correction). SAMI3 predicts 55.5% of reported amateur radio links between 2–30 MHz and 500–2,000 km. Autoscaled and then machine learning “cleaned” Digisonde NmF2 data indicate a 1.0 × 10¹¹ el. m³median positive bias in SAMI3 (equivalent to a 27% overestimation). The positive NmF2 bias is largest during the daytime, which may explain the relatively good performance in predicting HF links then. The underlying data sources and software used here are publicly available, so that interested groups may apply these tests to other models and time intervals. 

Abstract A linear theory of the generalized Rayleigh‐Taylor instability (GRTI) is derived which includes ion inertia and acceleration forces, as well asEregion drivers: the zonal neutral wind and plasma drift. This is in contrast to theFregion drivers (aside from gravity): the meridional neutral wind and the meridional/vertical plasma drifts. Both a local theory and a flux‐tube integrated theory are presented with application to the onset of ionosphere irregularities associated with equatorial spreadF. Inertia and acceleration forces do not affect the growth rate of the GRTI for nominal ionospheric conditions, but theEregion zonal drifts can significantly increase or decrease the growth rate of the GRTI in the equatorial and mid‐latitude ionosphere depending on their direction. 

Key Points Validation of ionospheric total electron content (TEC) by the state‐of‐the‐art ionospheric models hosted by NASA Community Coordinated Modeling Center, National Oceanic and Atmospheric Administration Space Weather Prediction Center, and NASA Jet Propulsion Laboratory (JPL) Multiple metrics and skill scores are used to assess the performance of ionospheric models in capturing storm time TEC anomaly GLObal Total Electron Content and JPL Global Ionospheric Map perform best, and physics‐based models perform better than the empirical model in capturing storm TEC variations 

Abstract We report the first results of a global ionosphere/thermosphere simulation study that self‐consistently generates large‐scale equatorial spreadF(ESF) plasma bubbles in the postsunset ionosphere. The coupled model comprises the ionospheric code SAMI3 and the atmosphere/thermosphere code WACCM‐X. Two cases are modeled for different seasons and geophysical conditions: the March case (low solar activity: F10.7 = 70) and the July case (high solar activity: F10.7 = 170). We find that equatorial plasma bubbles formed and penetrated into the topsideFlayer for the March case but not the July case. For the March case, a series of bubbles formed in the Atlantic sector with irregularity spacings in the range 400–1,200 km, rose to over 800 km, and persisted until after midnight. These results are consistent with recent GOLD observations. Calculation of the generalized Rayleigh‐Taylor instability (GRTI) growth rate shows that the e‐folding time was shorter for the March case than the July case. 

Abstract A new technique has been developed to determine the high‐latitude electric potential from observed field‐aligned currents (FACs) and modeled ionospheric conductances. FACs are observed by the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), while the conductances are modeled by Sami3 is Also a Model of the Ionosphere (SAMI3). This is a development of the Magnetosphere‐Ionosphere Coupling approach first demonstrated by Merkin and Lyon (2010),https://doi.org/10.1029/2010ja015461. An advantage of using SAMI3 is that the model can be used to predict total electron content (TEC), based on the AMPERE‐derived potential solutions. 23 May 2014 is chosen as a case study to assess the new technique for a moderately disturbed case (min Dst: −36 nT, max AE: 909 nT) with good GPS data coverage. The new AMPERE/SAMI3 solutions are compared against independent GPS‐based TEC observations from the Multi‐Instrument Data Analysis Software (MIDAS) by Mitchell and Spencer (2003), and against Defense Meteorological Satellite Program (DMSP) ion drift data. The comparison shows excellent agreement between the location of the tongue of ionization in the MIDAS GPS data and the AMPERE/SAMI3 potential pattern, and good overall agreement with DMSP drifts. SAMI3 predictions of high‐latitude TEC are much improved when using the AMPERE‐derived potential as compared to Weimer's (2005),https://doi.org/10.1029/2005ja011270model. The two potential models have substantial differences, with Weimer producing an average 77 kV cross‐cap potential versus 60 kV for the AMPERE‐derived potential. The results indicate that the 66‐satellite Iridium constellation provides sufficient resolution of FACs to estimate large‐scale ionospheric convection as it impacts TEC. 

Abstract The polarFregion ionosphere frequently exhibits sporadic variability (e.g., Meek, 1949,https://doi.org/10.1029/JZ054i004p00339; Hill, 1963,https://doi.org/10.1175/1520‐0469(1963)020<0492:SEOLII>2.0.CO;2). Recent satellite data analysis (Noja et al., 2013,https://doi.org/10.1002/rds.20033; Chartier et al., 2018,https://doi.org/10.1002/2017JA024811) showed that the high‐latitudeFregion ionosphere exhibits sporadic enhancements more frequently in January than in July in both the northern and southern hemispheres. The same pattern has been seen in statistics of the degradation and total loss of GPS service onboard low‐Earth orbit satellites (Xiong et al. 2018,https://doi.org/10.5194/angeo‐36‐679‐2018). Here, we confirm the existence of this annual pattern using ground GPS‐based images of TEC from the MIDAS algorithm. Images covering January and July 2014 confirm that the high‐latitude (>70 MLAT)Fregion exhibits a substantially larger range of values in January than in July in both the northern and southern hemispheres. The range of TEC values observed in the polar caps is 38–57 TECU (north‐south) in January versus 25–37 TECU in July. First‐principle modeling using SAMI3 reproduces this pattern, and indicates that it is caused by an asymmetry in plasma levels (30% higher in January than in July across both polar caps), as well as 17% longer O⁺plasma lifetimes in northern hemisphere winter, compared to southern hemisphere winter. 

Abstract The total solar eclipse over the continental United States on 21 August 2017 offered a unique opportunity to study the dependence of the ionospheric density and morphology on incident solar radiation at different local times. The Super Dual Auroral Radar Network (SuperDARN) radars in Christmas Valley, Oregon, and Fort Hays, Kansas, are located slightly southward of the line of totality; they both made measurements of the eclipsed ionosphere. The received power of backscattered signal decreases during the eclipse, and the slant ranges from the westward looking radar beams initially increase and then decrease after totality. The time scales over which these changes occur at each site differ significantly from one another. For Christmas Valley the propagation changes are fairly symmetric in time, with the largest slant ranges and smallest power return occurring coincident with the closest approach of totality to the radar. The Fort Hays signature is less symmetric. In order to investigate the underlying processes governing the ionospheric eclipse response, we use a ray‐tracing code to simulate SuperDARN data in conjunction with different eclipsed ionosphere models. In particular, we quantify the effect of the neutral wind velocity on the simulated data by testing the effect of adding/removing various neutral wind vector components. The results indicate that variations in meridional winds have a greater impact on the modeled ionospheric eclipse response than do variations in zonal winds. The geomagnetic field geometry and the line‐of‐sight angle from each site to the Sun appear to be important factors that influence the ionospheric eclipse response.