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The migrating diurnal tide (DW1) is derived by fitting Hough Mode Extensions to the TIMED/TIDI near‐global wind measurements within the mesosphere and lower thermosphere between 85 and 100 km from 2004 to 2014. The tidal amplitude peaks around the equinoxes with a large interannual variability of up to 50%. The correlation coefficients between the tidal amplitude variability and the solar cycle as represented by F10.7, stratospheric Quasi‐Biennial Oscillation (QBO), and El Niño‐Southern Oscillation (ENSO) are calculated every 10 days revealing seasonal dependencies. The interannual variability is positively correlated with QBO from spring to fall, maximizing around the equinoxes; anti‐correlated to the solar cycle in early winter; and anti‐correlated to ENSO in early winter and slightly in March. Multivariate linear regressions are performed to quantitatively analyze the linear relationships between the DW1 amplitude and those factors. The fittings perform best with the QBO at 30 and 50 hPa both being considered. The contribution of QBO peaks around January and October may be related to the polar vortex modulated by QBO in the northern and southern hemispheres, respectively. The correlation between the DW1 amplitude and ENSO is negative with time lags <∼5 months during early winter and spring. Key Points: Correlation coefficient is positive for Quasi‐Biennial Oscillation (QBO) from spring to fall; negative for solar cycle in early winter; and negative for El Niño‐Southern Oscillation in early winter and MarchQBO30 and QBO50 contribution peaks may be related to the modulated polar vortex in the northern and southern hemispheres, respectivelyThe DW1 amplitude and the Oceanic Niño Index are negatively correlated with time lags <∼5 months during early winter and spring [ABSTRACT FROM AUTHOR]

This paper introduces the new Venus global ionosphere‐thermosphere model (V‐GITM) which incorporates the terrestrial GITM framework with Venus‐specific parameters, ion‐neutral chemistry, and radiative processes in order to simulate some of the observable features regarding the temperatures, composition, and dynamical structure of the Venus atmosphere from 70 to 170 km. Atmospheric processes are included based upon formulations used in previous Venus GCMs, several augmentations exist, such as improved horizontal and vertical momentum equations and tracking exothermic chemistry. Explicitly solving the momentum equations allows for the exploration of its dynamical effects on the day‐night structure. In addition, V‐GITM's use of exothermic chemistry instead of a strong heating efficiency accounts for the heating due to the solar EUV while producing comparable temperatures to empirical models. V‐GITM neutral temperatures and neutral‐ion densities are compared to upper atmosphere measurements obtained from Pioneer Venus and Venus Express. V‐GITM demonstrates asymmetric horizontal wind velocities through the cloud tops to the middle thermosphere and explains the mechanisms for sustaining the wind structure. In addition, V‐GITM produces reasonable dayside ion densities and shows that the neutral winds can carry the ions to the nightside via an experiment advecting O2+ ${\mathrm{O}}_{2}^{+}$. Plain Language Summary: We present a new, state‐of‐the‐art Venus global circulation model, the Venus global ionosphere thermosphere model. The new model will be useful for answering unknown questions about Venus' atmosphere, the physics of CO2 rich planets, and Venus missions utilizing the aerobraking maneuver. We performed many model simulations and compared the results to some of the existing Venus data sets to assess the accuracy of the model. The results showed that the dayside extreme ultraviolet heating can be captured using the dominant ion‐neutral chemistry rather than relying on simplified legacy methods. Furthermore, the cloud layer below the thermosphere has a unique wind pattern and its impact on the thermospheric temperatures and winds were explored. This work also revealed that the thin nightside ionosphere forms as a result of the transport of a single ion species. Key Points: A new, non‐hydrostatic Venus ionosphere‐thermosphere model is introduced with new physics not previously included in Venus GCMsSimulations during solar minimum conditions are used for data‐model comparisons of the temperatures, plasma, and neutral densitiesThe influence of the retrograde superrotating zonal flow is explored in relation to how it affects the neutral temperature and velocities [ABSTRACT FROM AUTHOR]

In the context of space weather forecasting, solar EUV irradiance specification is needed on multiple time scales, with associated uncertainty quantification for determining the accuracy of downstream parameters. Empirical models of irradiance often rely on parametric fits between irradiance in several bands and various solar indices. We build upon these empirical models by using Generalized Additive Models (GAMs) to represent solar irradiance. We apply the GAM approach in two steps: (a) A GAM is fitted between FISM2 irradiance and solar indices F10.7, Revised Sunspot Number, and the Lyman‐α solar index. (b) A second GAM is fit to model the residuals of the first GAM with respect to FISM2 irradiance. We evaluate the performance of this approach during Solar Cycle 24 using GAMs driven by known solar indices as well as those forecasted 3 days ahead with an autoregressive modeling approach. We demonstrate negligible dependence of performance on solar cycle and season, and we assess the efficacy of the GAM approach across different wavelengths. Plain Language Summary: Modeling solar irradiance at extreme ultraviolet wavelengths is very important for describing the behavior of the upper atmosphere. Many empirical models represent solar irradiance by describing it as linearly‐dependent on measurements of other quantities that are very strongly correlated with it. These methods have shown great promise, but require building their models from many sources of data. We build upon these methods by showing that using only four sources of data (three solar proxies and irradiance from the FISM2 model), solar extreme ultraviolet irradiance can be modeled in different wavelengths efficiently. We use Generalized Additive Models (GAMs) for our approach, which are used to describe irradiance in terms of a sum of smooth functions of solar proxies. We show how this approach can be used to forecast solar EUV irradiance. Key Points: Solar EUV irradiance is modeled using Generalized Additive Models parameterized using F10.7, revised Sunspot Number, and Lyman‐αRelative irradiance error is evaluated for solar cycle and seasonal dependence and compared to that of TIMED/SEE and SDO/EVESolar EUV irradiance hindcasts during Solar Cycle are generated to demonstrate the suitability of the GAM approach for forecasting [ABSTRACT FROM AUTHOR]

Auroral images of the N2 Lyman‐Birge‐Hopfield emissions from Polar Ultraviolet Imager (UVI) for 1.5 years are used to investigate the auroral characteristics related to the AU and AL indices that represent the directly driven and unloading processes in the solar wind‐magnetosphere‐ionosphere coupling, respectively. Findings include: (a) Growing AL mainly relates to the nightside aurora and tends to keep the general auroral morphology. (b) As AU increases, the aurora brightens and expands more globally, especially in the midnight to postmidnight sector. (c) The regional auroral power (AP), equatorward boundary, poleward boundary, and peak intensity change quasi‐linearly with intensifying AU and AL. (d) The rates of change depend on the MLT and AU/AL levels. The same dataset has been used to construct the empirical Feature Tracking of Auroral Precipitation (FTA) model which specifies the global energy flux and mean energy determined by the AE index (FTA‐AE). As an extension of FTA‐AE, the relationships of the auroral emission with the AU&AL indices were derived to construct the FTA‐AU&AL model which specifies a more consistent aurora during different magnetospheric driving modes compared to FTA‐AE. Comparisons of AP from the Defense Meteorological Satellite Program Special Sensor Ultraviolet Spectrographic Imagers (SSUSI) measurements and empirical auroral models show that the FTA‐AE and ‐AU&AL models predicted larger AP than the Fuller‐Rowell and Evans (1987) model and the OVATION‐prime model did. As the activity level increased, all four models tended to underestimate the AP but the FTA APs increased relatively faster and were therefore more consistent with the data. Key Points: Aurora brightens and tends to keep morphology as AL grows; aurora brightens with a midnight‐to‐dawn enhancement developing as AU growsAuroral integrated power, boundaries, and peak intensity change quasi‐linearly with AU/AL and the rates depend on the MLT and AU/AL levelsEmpirical relationships between the auroral emission and the AU&AL indices are derived to construct the FTA‐AU&AL model [ABSTRACT FROM AUTHOR]

At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier‐Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents (N2, O2, and O) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm. Plain Language Summary: Physics‐based models have a difficult time accurately estimating the upper atmosphere density. These densities are needed to compute satellite orbit trajectories to monitor for potential collisions. Inaccurate density estimation can be due to variety of factors and so methods of correcting the model‐predicted density are needed. We are presenting a method to correct the densities using available satellite measurements from the Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment satellites and the commonly used empirical model NRLMSISE‐00. Upon reducing the model error, we show the improved ability of a physics‐based model to capture a geomagnetic storm. Key Points: Inaccurate approximations to physics terms and incorrect drivers within Global Ionosphere Thermosphere Model (GITM) can be corrected for using data‐driven model refinementDynamic adjustments to the parameterized thermal conductivity coefficients can compensate for errors in model predicted mass densitiesComparative statistics were computed when GITM was configured in a biased version, an out‐of‐the‐box version and the refined version [ABSTRACT FROM AUTHOR]

The Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics (TIMED) satellite has been making observations of the mesosphere and lower thermosphere (MLT) region for two decades. The TIMED Doppler Interferometer (TIDI) measures the neutral winds using four orthogonal telescopes. In this study, the line of sight (LOS) winds from individual telescopes are compared to the measurements from the Ionospheric Connection Explorer's (ICON's) Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument from 90 to 100 km altitude during 2020. With the MIGHTI vector winds projected onto the LOS direction of each TIDI telescope, coincidences of the two data sets are found. The four telescopes perform differently and the performance depends on the satellite configuration and local solar zenith angle. Measurements from the coldside telescopes, Telescope 1 (Tel1) and Telescope 2 (Tel2), are better correlated with the MIGHTI winds in general with Tel2 having higher correlation coefficients across all conditions. The performance of Tel1 is comparable to that of Tel2 during backward flight while showing systematic errors larger than the average wind speeds during forward flight. The warmside LOS winds from Telescope 3 (Tel3) and Telescope 4 (Tel4) vary widely in magnitude, especially on the nightside. Compared with MIGHTI winds, the Tel4 measurements have the weakest correlation, while the Tel3 performance is comparable to that of the coldside telescopes during the ascending phase but deteriorates during the descending phase. Based on the TIDI/MIGHTI comparisons, figures of merit are generated to quantify the quality of measurements from individual telescopes in different configurations. Key Points: Line of sight winds from four TIMED Doppler Interferometer (TIDI) telescopes are compared to Ionospheric Connection Explorer‐Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) measurementsPerformance figures of merit for individual TIDI telescopes in different satellite configurations are presentedColdside TIDI telescopes match MIGHTI better than warmside ones in general, while systematic errors are found in Tel1 during forward flight [ABSTRACT FROM AUTHOR]

Characterization of the thermospheric horizontal wind is an important challenge in atmospheric modeling due to its vital role in the transport of densities and energy, associations with the diurnal tide, and interplay with vertical winds that drive changes in the thermosphere neutral composition. The mechanisms and drivers that underlie the physics of thermospheric horizontal winds remain under investigation and, to date, no comprehensive statistical study between thermospheric winds generated by a physics-based atmospheric model and those retrieved from satellite measurements has been performed. Comparisons between cross-track horizontal winds from a 10-month run of Global Ionosphere-Thermosphere (GITM) and those derived from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite show that GITM did best modeling horizontal winds in the polar zone but overestimated horizontal wind speeds at midlatitudes just outside of the equatorial ionization anomaly region. GITM's wind response to AE was best at polar noon and worst in the midnight auroral zone, its ability to capture seasonality was best in the northern high latitudes and worst in the southern high latitudes, and GITM displayed less wind variability as a function of F10.7 than GOCE, matching it best for F10.7 >~150. Discrepancies in GITM's performance may be explained by inaccurate modeling of ion drift, ion drag, and electron densities. [ABSTRACT FROM AUTHOR]

The latitudinal and temporal variation of atomic oxygen (O) is opposite between the empirical model, NRLMSISE‐00 (MSIS) and the whole atmosphere model, whole atmosphere community climate model with thermosphere and ionosphere extension (WACCM‐X) at 97–100 km. Atomic Oxygen from WACCM‐X has maxima at solstices and summer mid‐high latitudes, similar to [O] from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). We use the densities and dynamics from WACCM‐X to drive the Global Ionosphere Thermosphere Model (GITM) at its lower boundary and compare it with the MSIS driven GITM. We focus on the differences in the modeling of the thermospheric and ionospheric semiannual oscillation (T‐I SAO). Our results reveal that driving GITM with WACCM‐X causes the T‐I SAO to maximize around solstices, opposite to when MSIS is used. This is because the global mixing in GITM during solstices is not strong enough to decrease the solstitial [O] densities below the equinoctial values between mesosphere and lower thermosphere (MLT) and upper thermosphere. Larger summer [O] in the MLT leads to the accumulation of [O] at lower latitudes in the thermosphere due to weaker meridional transport, which further increases the amplitude of the oppositely phased SAO. WACCM‐X itself has the right phase of SAO in the upper thermosphere but wrong at lower altitudes. The exact mechanisms that can correct the phase of T‐I SAO in GITM while using SABER‐like [O] in the MLT are currently unknown and warrant further investigation. We suggest mechanisms that can reduce the solstitial maxima in the lower thermosphere, for example, stronger interhemispheric meridional winds, stronger residual circulation, seasonal variations in eddy diffusion, and momentum from breaking gravity waves. Plain Language Summary: We study the characteristics and drivers of the Thermospheric and Ionospheric Semiannual Oscillation (T‐I SAO) using a numerical model of the Earth's upper atmosphere. It is an oscillation in T‐I densities, temperature, and composition with maxima at equinoxes. We investigate the contribution of lower atmosphere to the T‐I SAO using different assumptions at the lower boundary of the model. We find that using the correct lower boundary conditions changes the phase of T‐I SAO such that it does not match with the satellite observations at higher altitudes. This implies that there are mechanisms missing in the numerical model that can reproduce the correct SAO phase while using the updated boundary conditions. Key Points: Atomic oxygen in mesosphere and lower thermosphere (MLT) from Sounding of the Atmosphere using Broadband Emission Radiometry and whole atmosphere community climate model with thermosphere and ionosphere extension (WACCM‐X) has a semiannual oscillation (SAO) with maxima at solstices and at summer mid‐high latitudes, opposite to that of MSISGlobal Ionosphere Thermosphere Model (GITM) reproduces the T‐I SAO with equinoctial maxima using MSIS [O] at lower boundary and with solstitial maxima using WACCM‐X [O]GITM does not change the SAO phase between MLT and upper thermosphere on a seasonal scale [ABSTRACT FROM AUTHOR]

The Feature Tracking of Aurora (FTA) model was constructed using 1.5 years of Polar Ultraviolet Imager data and is based on tracking a cumulative energy grid in 96 magnetic local time (MLT) sectors. The equatorward boundary, poleward boundary, and 19 cumulative energy bins are tracked with the energy flux and the latitudinal position. With AE increasing, the equatorward boundary moves to lower latitudes everywhere, while the poleward boundary moves poleward in the 2300-0300 MLT region and equatorward in other MLT sectors. This results in the aurora getting wider on the nightside and becoming narrower on the dayside. The peak intensity of the aurora in each MLT sector is almost linearly related to AE, with the global peak moving from pre-midnight to post-midnight as geomagnetic activity increases. Ratios between the Lyman-Birge-Hopfield-long and -short models allow the average energy to be calculated. Predictions from the FTA and two other auroral models were compared to the measurements by the Defense Meteorological Satellite Program Special Sensor Ultraviolet Spectrographic Imagers (SSUSI) on March 17, 2013. Among the three models, the FTA model specified the most confined patterns with the highest energy flux, agreeing with the spatial and temporal evolution of SSUSI measurements better and predicted auroral power (AP) better during higher activity levels (SSUSI AP > 20 GW). The Fuller-Rowell and Evans (1987) and FTA models specified very similar average energy compared with SSUSI measurements, doing slightly better by ~1 keV than the OVATION Prime model. [ABSTRACT FROM AUTHOR]

Empirical models of the thermospheric density are routinely used to perform orbit maintenance, satellite collision avoidance, and estimate time and location of re‐entry for spacecraft. These models have characteristic errors in the thermospheric density below 10% during geomagnetic quiet time but are unable to reproduce the significant increase and subsequent recovery in the density observed during geomagnetic storms. Underestimation of the density during these conditions translates to errors in orbit propagation that reduce the accuracy of any resulting orbit predictions. These drawbacks risk the safety of astronauts and orbiting spacecraft and also limit understanding of the physics of thermospheric density enhancements. Numerous CubeSats with publicly available ephemeris in the form of two‐line element (TLEs) sets orbit in this region. We present the Multifaceted Optimization Algorithm (MOA), a method to estimate the thermospheric density by minimizing the error between a modeled trajectory and a set of TLEs. The algorithm first estimates a representative cross‐sectional area for several reference CubeSats during the quiet time 3 weeks prior to the storm, and then estimates modifications to the inputs of the NRLMSISE‐00 empirical density model in order to minimize the difference between the modeled and TLE‐provided semimajor axis of the CubeSats. For validation, the median value of the modifications across all CubeSats are applied along the Swarm spacecraft orbits. This results in orbit‐averaged empirical densities below 10% error in magnitude during a geomagnetic storm, compared to errors in excess of 25% for uncalibrated NLRMSISE‐00 when compared to Swarm GPS‐derived densities. Plain Language Summary: Empirical atmospheric density models underestimate the increase in thermospheric density observed during times of intense solar and geomagnetic activity. This demonstrates our limited understanding of the physics of the thermosphere during these times and limits our ability to accurately predict the orbits of both operational satellites and space debris. We present a method to correct these density underestimations by using an orbital propagator and correcting the inputs to the NRLMSISE‐00 density model to minimize orbit error. We apply medians of these adjustments along the orbit of the Swarm spacecraft and compare resulting corrected densities to GPS‐derived densities collected by Swarm. Key Points: Empirical atmospheric models exhibit significant density underestimation during geomagnetic stormsA simple algorithm presents a new way of obtaining improved density model estimates from two‐line elements describing satellite orbitsThe technique is validated against Swarm GPS‐derived densities during geomagnetic quiet and active times [ABSTRACT FROM AUTHOR]

Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field‐aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere‐ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first‐principles‐based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere‐ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field‐aligned current values. CMEE has been developed using nonlinear regression over a year's worth of 1‐min resolution output from assimilative maps, specifically including times of extreme driving of the solar wind‐magnetosphere‐ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground‐based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents, which impact the simulated dB/dt and ΔB, resulting in substantial improvements in dB/dt predictive skill. Plain Language Summary: Electric currents generated in the Earth's space environment due to its magnetic interaction with the Sun leads to charged particle deposition and closure of these currents in the terrestrial upper atmosphere, especially in the high‐latitude auroral region. The enhancement in the electrical charge‐carrying capacity as a result of this process in the Earth's upper atmosphere, also known as the ionosphere, is challenging to estimate in most numerical simulations attempting to study the interactive dynamic and chemical processes in the near‐Earth region. The inability to accurately estimate this quantity leads to underprediction of severe space weather events that can have adverse impacts on man‐made technology like electrical power grids, railway, and oil pipelines. In this study, we present a novel modeling approach to address this problem and provide global simulations with a more accurate estimate on the electrical conductivity of the ionosphere. Through this investigation, we show that the accurate measurement of the charge carriers in the ionosphere using the new model causes substantial improvements in the prediction of space weather on the ground, and significantly advances our understanding of global dynamics causing ground‐based space weather. Key Points: An updated auroral conductance module is built for global models using nonlinear regression and empirical adjustments spanning extreme eventsExpanded data set raises the ceiling of conductance values, impacting the polar cap potential, dB/dt, and dB predictions during extreme eventsApplication of expanded model with empirical oval adjustments refines the conductance pattern and drastically improves dB/dt predictions [ABSTRACT FROM AUTHOR]

The exchange of energy between the lower atmosphere and the ionosphere thermosphere system is not well understood. One of the parameters that is important in the lower thermosphere is atomic oxygen. It has recently been observed that atomic oxygen is higher in summer at ~95 km. In this study, we investigate the sensitivity of the upper thermosphere to lower thermospheric atomic oxygen using the Global Ionosphere Thermosphere Model (GITM).We use the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) to drive the lower atmospheric boundary of atomic oxygen in GITM between ~95 and 100km and compare the results with the current mass spectrometer incoherent scatter (MSIS) driven GITM. MSIS has higher atomic oxygen in the winter hemisphere whileWACCM-X has higher atomic oxygen in the summer hemisphere. The reversal of atomic oxygen distribution affects the pressure distribution between 100 and 120 km, such that the hemisphere with larger O number density has stronger equatorward winds, and lower temperature mainly due to adiabatic and radiative cooling. This affects thermospheric scale heights such that the hemisphere with more O has lower N2 and thus enhanced O/N2. This behavior is observed in the opposite hemisphere when MSIS is used as the lower boundary for GITM. Overall, O/N2 forWACCM-X driven GITMmatches better with the global ultraviolet imager (GUVI) data. We find that the impact of lower thermospheric atomic oxygen on upper thermosphere is not just through diffusive equilibrium but also through secondary effects on winds and temperature. [ABSTRACT FROM AUTHOR]

Forecasting conditions in the thermosphere and ionosphere is a key outcome expected from space weather research. In this work, we perform numerical simulations using the first‐principles models Global Ionosphere‐Thermosphere Model (GITM) and Thermosphere‐Ionosphere Electrodynamics General Circulation Model (TIE‐GCM) to address the reliability of thermospheric‐ionospheric forecasts. When considering forecasts applicable to periods of geomagnetic activity, careful consideration is required of model inputs, which largely determine how the models will simulate disturbed conditions. We adopt an approach to drive the models with solar wind parameters and the 10.7 cm solar radio flux. This aligns our investigation with recent research and operational activities to forecast solar wind conditions on the Earth a few days in advance. In this work, we examine a weak geomagnetic storm, the June 2012 high‐speed‐stream event, for which we drive GITM and TIE‐GCM with observed solar wind and F10.7 values. We find general agreement between the simulations and observation‐based Global Ionospheric Maps of the total electron content (TEC) response. However, overestimated TEC response is found in the middle to low latitudinal region of the American sector and surrounding areas for both GITM and TIE‐GCM during similar time periods. By conducting numerical modeling experiments and comparing the modeling results with observational data, we find that the overestimated TEC response can be almost equally attributed to the solar wind driving and F10.7 driving during the June 2012 event. We conclude that the accuracy of the high‐latitude electric field and the solar irradiance is crucial to reproduce the TEC response in forecastable‐mode modeling. Key Points: Forecastable global thermosphere‐ionosphere modeling is carried out for a weak geomagnetic stormThe modeled daytime middle‐ to low‐latitude TEC response is primarily driven by the solar wind condition on the first day of the stormOn later days of the storm the solar irradiance plays a comparable role as the solar wind in determining the modeled daytime TEC response [ABSTRACT FROM AUTHOR]

We present an empirical model of thermospheric winds (High‐latitude Thermospheric Wind Model [HL‐TWiM]) that specifies F region high‐latitude horizontal neutral winds as a function of day of year, latitude, longitude, local time, and geomagnetic activity. HL‐TWiM represents the large‐scale neutral wind circulation, in geomagnetic coordinates, for the given input conditions. The model synthesizes the most extensive collection to date of historical high‐latitude wind measurements; it is based on statistical analyses of several decades of F region thermospheric wind measurements from 21 ground‐based stations (Fabry‐Perot Interferometers and Scanning Doppler Imaging Fabry‐Perot Interferometers) located at various northern and southern high latitudes and two space‐based instruments (UARS WINDII and GOCE). The geomagnetic latitude and local time dependences in HL‐TWiM are represented using vector spherical harmonics, day of year and longitude variations are represented using simple harmonic functions, and the geomagnetic activity dependence is represented using quadratic B splines. In this paper, we describe the HL‐TWiM formulation and fitting procedures, and we verify the model against the neutral wind databases used in its formulation. HL‐TWiM provides a necessary benchmark for validating new wind observations and tuning our physical understanding of complex wind behaviors. Results show stronger Universal Time variation in winds at southern than northern high latitudes. Model‐data intra‐annual comparisons in this study show semiannual oscillation‐like behavior of GOCE winds, rarely observed before in wind data. Key Points: We developed a comprehensive empirical model of high‐latitude F region thermospheric winds (HL‐TWiM)Universal Time variations in high‐latitude winds are stronger in the Southern than Northern HemisphereHL‐TWiM provides a necessary benchmark for validating new high‐latitude wind observations and tuning first principal models [ABSTRACT FROM AUTHOR]

On 11 June 2017, a sudden solar wind dynamic pressure decrease occurred at 1437 UT according to the OMNI solar wind data. The solar wind velocity did not change significantly, while the density dropped from 42 to 10 cm−3 in a minute. The interplanetary magnetic field BZ was weakly northward during the event, while the BY changed from positive to negative. Using the University of Michigan Block Adaptive Tree Solarwind Roe Upwind Scheme global magnetohydrodynamic code, the global responses to the decrease in the solar wind dynamic pressure were studied. The simulation revealed that the magnetospheric expansion consisted of two phases similar to the responses during magnetospheric compression, namely, a negative preliminary impulse and a negative main impulse phase. The simulated plasma flow and magnetic fields reasonably reproduced the Time History of Events and Macroscale Interactions during Substorms and Magnetospheric Multiscale spacecraft in situ observations. Two separate pairs of dawn‐dusk vortices formed during the expansion of the magnetosphere, leading to two separate pairs of field‐aligned current cells. The effects of the flow and auroral precipitation on the ionosphere‐thermosphere (I‐T) system were investigated using the Global Ionosphere Thermosphere Model driven by simulated ionospheric electrodynamics. The perturbations in the convection electric fields caused enhancements in the ion and electron temperatures. This study shows that, like the well‐studied sudden solar wind pressure increases, sudden pressure decreases can have large impacts in the coupled I‐T system. In addition, the responses of the I‐T system depend on the initial convection flows and field‐aligned current profiles before the solar wind pressure perturbations. Key Points: The decrease in the solar wind dynamic pressure led to two separate pairs of oppositely rotating vortices in the dawn and duskFACs accompanied each magnetospheric vortex and altered the ionosphere convection patternsJoule heating increased in the regions sandwiched by the perturbation FACs, leading to increased ion temperatures [ABSTRACT FROM AUTHOR]

The first long‐term comparison of day‐to‐day variability (i.e., weather) in the thermospheric winds between a first‐principles model and data is presented. The definition of weather adopted here is the difference between daily observations and long‐term averages at the same UT. A year‐long run of the Global Ionosphere Thermosphere Model is evaluated against a nighttime neutral wind data set compiled from six Fabry‐Perot interferometers at middle and low latitudes. First, the temporal persistence of quiet‐time fluctuations above the background climate is evaluated, and the decorrelation time (the time lag at which the autocorrelation function drops to e−1) is found to be in good agreement between the data (1.8 hr) and the model (1.9 hr). Next, comparisons between sites are made to determine the decorrelation distance (the distance at which the cross‐correlation drops to e−1). Larger Fabry‐Perot interferometer networks are needed to conclusively determine the decorrelation distance, but the current data set suggests that it is ∼1,000 km. In the model the decorrelation distance is much larger, indicating that the model results contain too little spatial structure. The measured decorrelation time and distance are useful to tune assimilative models and are notably shorter than the scales expected if tidal forcing were responsible for the variability, suggesting that some other source is dominating the weather. Finally, the model‐data correlation is poor (−0.07 < ρ < 0.36), and the magnitude of the weather is underestimated in the model by 65%. Plain Language Summary: Much like in the lower atmosphere, weather in the upper atmosphere is harder to predict than climate. Physics‐based models are becoming sophisticated enough that they can in principle predict the weather, and we present the first long‐term evaluation of how well a particular model, Global Ionosphere Thermosphere Model, performs. To evaluate the model, we compare it with a year of data from six ground‐based sites that measure the thermospheric wind. First, we calculate statistics of the weather, such as the decorrelation time, which characterizes how long weather fluctuations persist (1.8 hr in the data and 1.9 hr in the model). We also characterize the spatial decorrelation by comparing weather at different sites. The model predicts that the weather is much more widespread than the data indicates; sites that are 790 km apart have a measured correlation of 0.4, while the modeled correlation is 0.8. In terms of being able to actually predict a weather fluctuation on a particular day, the model performs poorly, with a correlation that is near zero at the low latitude sites, but reaches an average of 0.19 at the midlatitude sites, which are closer to the source that most likely dominates the weather: heating in the auroral zone. Key Points: A long‐term data‐model comparison of day‐to‐day thermospheric variability finds that GITM represents the weather poorly (−0.07 < ρ < 0.36)The average measured decorrelation time of 1.8 hr agrees with the modeled time of 1.9 hrThe weather in GITM contains too little spatial structure, when compared with the measured ∼1,000‐km decorrelation distance [ABSTRACT FROM AUTHOR]

The CYGNSS constellation was deployed in December 2016. To optimize the coverage of tropical cyclones, the eight observatories have to be evenly spaced out along the orbit. Since the spacecraft do not have a system of propulsion, differential drag maneuvers have been conducted to control the constellation phasing. However, many constraints have considerably restricted the differential drag operations, particularly during the first months of the mission. The study discusses these constraints and provides an evaluation of the effectiveness of the differential drag maneuvers during the first year of the mission. Future plans for the completion of the constellation phasing are also presented. [ABSTRACT FROM AUTHOR]

Constellations of GNSS-R satellites improve the coverage of regions of interest by repeating measurements in a shorter period of time than with a single spacecraft. However, the temporal and spatial resolution of the samples are dependent on each other. Detecting short time scale changes is generally done with coarser spatial resolution. Likewise, detailed observations of a region with small-scale features require longer intervals of time between observations. This study demonstrates the relationship between temporal and spatial resolution and its dependence on key mission design parameters such as the number of satellites, the number of orbit planes, and their inclination. [ABSTRACT FROM AUTHOR]

Abstract: The rapid increase of the number of objects in orbit around the Earth poses a serious threat to operational spacecraft and astronauts. In order to effectively avoid collisions, mission operators need to assess the risk of collision between the satellite and any other object whose orbit is likely to approach its trajectory. Several algorithms predict the probability of collision but have limitations that impair the accuracy of the prediction. An important limitation is that uncertainties in the atmospheric density are usually not taken into account in the propagation of the covariance matrix from current epoch to closest approach time. The atmosphere between 100 km and 700 km is strongly driven by solar and magnetospheric activity. Therefore, uncertainties in the drivers directly relate to uncertainties in the neutral density, hence in the drag acceleration. This results in important considerations for the prediction of Low Earth Orbits, especially for the determination of the probability of collision. This study shows how uncertainties in the atmospheric density can cause significant differences in the probability of collision and presents an algorithm that takes these uncertainties into account to more accurately assess the risk of collision. As an example, the effects of a geomagnetic storm on the probability of collision are illustrated. [ABSTRACT FROM AUTHOR]

Fabry-Perot interferometer (FPI) measurements of thermospheric temperatures and winds show the detection and successful determination of the latitudinal distribution of the midnight temperature maximum (MTM) in the continental mid-eastern United States. These results were obtained through the operation of the five FPI observatories in the North American Thermosphere Ionosphere Observing Network (NATION) located at the Pisgah Astronomic Research Institute (PAR) (35.2° N, 82.8° W), Virginia Tech (VTI) (37.2° N, 80.4° W), Eastern Kentucky University (EKU) (37.8° N, 84.3° W), Urbana-Champaign (UAO) (40.2° N, 88.2° W), and Ann Arbor (ANN) (42.3° N, 83.8° W). A new approach for analyzing the MTM phenomenon is developed, which features the combination of a method of harmonic thermal background removal followed by a 2-D inversion algorithm to generate sequential 2-D temperature residual maps at 30 min intervals. The simultaneous study of the temperature data from these FPI stations represents a novel analysis of the MTM and its large-scale latitudinal and longitudinal structure. The major finding in examining these maps is the frequent detection of a secondary MTM peak occurring during the early evening hours, nearly 4.5 h prior to the timing of the primary MTM peak that generally appears after midnight. The analysis of these observations shows a strong night-to-night variability for this double-peaked MTM structure. A statistical study of the behavior of the MTM events was carried out to determine the extent of this variability with regard to the seasonal and latitudinal dependence. The results show the presence of the MTM peak(s) in 106 out of the 472 determinable nights (when the MTM presence, or lack thereof, can be determined with certainty in the data set) selected for analysis (22%) out of the total of 846 nights available. The MTM feature is seen to appear slightly more often during the summer (27%), followed by fall (22%), winter (20%), and spring (18%). Also seen is a northwestward propagation of the MTM signature with a latitude-dependent amplitude. This behavior suggests either a latitudinal dependence of thermosphere tidal dissipation or a night-to-night variation of the composition of the higher-order tidal modes that contribute to the production of the MTM peak at mid-latitudes. Also presented in this paper is the perturbation on the divergence of the wind fields, which is associated with the passage of each MTM peak analyzed with the 2-D interpolation. [ABSTRACT FROM AUTHOR]

Abstract: This study is focused on improving the poorly understood seasonal dependence of northern high‐latitude F region thermospheric winds under active geomagnetic conditions. The gaps in our understanding of the dynamic high‐latitude thermosphere are largely due to the sparseness of thermospheric wind measurements. With current observational facilities, it is infeasible to construct a synoptic picture of thermospheric winds, but enough data with wide spatial and temporal coverage have accumulated to construct a meaningful statistical analysis. We use long‐term data from eight ground‐based and two space‐based instruments to derive climatological wind patterns as a function of magnetic local time, magnetic latitude, and season. These diverse data sets possess different geometries and different spatial and solar activity coverage. The major challenge is to combine these disparate data sets into a coherent picture while overcoming the sampling limitations and biases among them. In our previous study (focused on quiet time winds), we found bias in the Gravity Field and Steady State Ocean Circulation Explorer (GOCE) cross‐track winds. Here we empirically quantify the GOCE bias and use it as a correction profile for removing apparent bias before empirical wind formulation. The assimilated wind patterns exhibit all major characteristics of high‐latitude neutral circulation. The latitudinal extent of duskside circulation expands almost 10∘ from winter to summer. The dawnside circulation subsides from winter to summer. Disturbance winds derived from geomagnetic active and quiet winds show strong seasonal and latitudinal variability. Comparisons between wind patterns derived here and Disturbance Wind Model (DWM07) (which have no seasonal dependence) suggest that DWM07 is skewed toward summertime conditions. [ABSTRACT FROM AUTHOR]

The accuracy of global atmospheric models used to predict the middle/lower thermosphere characteristics is still an open topic. Uncertainties in the prediction of the gas properties in the thermosphere lead to inaccurate computations of the drag force on space objects (i.e. satellites or debris). Currently the lifetime of space objects and therefore the population of debris in low Earth orbit (LEO) cannot be quantified with a satisfactory degree of accuracy. In this paper, the Global Ionosphere Thermosphere Model (GITM) developed at the University of Michigan has been validated in order to provide detailed simulations of the thermosphere. First, a sensitivity analysis has been performed to investigate the effect of the boundary conditions on the final simulations results. Then, results of simulations have been compared with flight measurements from the CHallenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) satellites and with existing semiempirical atmospheric models (IRI and MSIS). The comparison shows a linear dependency of the neutral density values with respect to the solar activity. In particular, GITM shows an over-predicting or under-predicting behaviour under high or low solar activity respectively. The reasons for such behaviour can be attributed to a wrong implementation of the chemical processes or the gas transport properties in the model. [ABSTRACT FROM AUTHOR]

The North American Thermosphere Ionosphere Observing Network (NATION), comprising a new network of Fabry-Perot interferometers (FPIs), to be deployed in the Midwest of the United States of America is described. FPIs will initially be deployed to four sites to make coordinated measurements of the neutral winds and temperature in the Earth's thermosphere using measurements of the 630nm redline emission. The observing strategy of the network will take into account local observing conditions, and common volume measurements from multiple sites will be made in order to estimate local vector wind quantities. The network described is expandable, and as additional FPI sites are installed in North America, or elsewhere, the goal of providing the upper atmospheric research community with a robust dataset of neutral winds and temperatures can be achieved. [ABSTRACT FROM AUTHOR]

The article presents a study which addressed the problem of how to use data to improve the fidelity of a given model. Retrospective cost optimization was utilized which uses data to recursively update an unknown subsystem interconnected to a known system. Using global ionosphere-thermosphere model (GITM) as the truth model, it was demonstrated that measurements can be used to identify unknown physics.