Challenging Minisatellite Payload Research Articles

Thermospheric Neutral Density Data Assimilation System Based on the Whole Atmosphere Model During the November 2003 Storm

AbstractThe Iterative Driver Estimation and Assimilation (IDEA) data assimilation technique was used with the Whole Atmosphere Model (WAM) to improve neutral density specification in the upper thermosphere. Two different neutral density data sources were examined to enhance the capability of simulating the global thermospheric state. The first were accelerometer estimates of neutral density from the Challenging Mini‐Satellite Payload (CHAMP) satellite. The second were neutral density estimates from the Global Ultraviolet Imager (GUVI) limb‐scan airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. Due to the intensity of the November 2003 storm, two changes were necessary in WAM. The first was allowing the Kp geomagnetic index to exceed 9 and the second was changing the relationship between Kp and the solar wind parameters used to drive the model. With these changes, results show that IDEA effectively captures the thermospheric neutral density at the CHAMP satellite altitude and follows the time‐dependence through the November 2003 storm period. Furthermore, a cross‐comparison was conducted with the GUVI dayside limb scan measurements. GUVI neutral densities within 270–320 km show the closest agreement with WAM when CHAMP data was assimilated by IDEA. We speculate on the potential for observations from GUVI at 300 km to be used as a data source in the IDEA‐WAM simulations. These simulations demonstrate the utility of the IDEA data assimilation technique with physical models and that using either accelerometer observations or ultraviolet airglow limb measurement during extreme storm periods could be used.

Interplanetary Influence on Thermospheric Mass Density: Insights From Deep Learning Analyses

AbstractIn this study, the thermospheric mass density (TMD) features observed by the CHAllenging Minisatellite Payload between 2002 and 2010 were extracted using deep learning (DL) technology; the TMD features were then mapped and modeled with the Interplanetary environment information (IEI), solar radiation, and geomagnetic indices. The DL model was used to simulate the TMD features during Day of Year (DOY) 222–241 in 2014, a period that experienced complex solar‐terrestrial environmental variations. We explore the TMD features under different solar‐terrestrial environmental conditions and discuss the effects of various inputs by comparing the DL simulation results with satellite observations from Gravity Recovery and Climate Experiment‐A and Swarm‐A, as well as the simulation results from Jacchia‐Bowman 2008, Naval Research Laboratory Mass Spectrometer Incoherent Scatter radar model 2.1, and Drag Temperature Model 2013. These results show that the DL model can better capture the TMD features after adding IEI. Part of these TMD features, including the high‐latitude TMD enhancement during the space hurricane event (DOY 232, 2014) and global TMD variations under complex solar‐terrestrial environmental disturbances (DOY 222–225, 2014), cannot be well described by the geomagnetic indices. The DL model indicates that the east‐west component of the interplanetary magnetic field (IMF By) has a great impact on TMD variations, and its modulation is different from the typical energy injection process during storms. Our results emphasize the crucial influence of IEI on TMD under both geomagnetic disturbances and quiet conditions.

The Feature Analysis and Modeling of Upper Atmospheric Midnight Density Maximum

The features of upper atmospheric midnight density maximum (MDM) around low geographic latitudes are studied based on neutral mass densities data at altitudes 360–480 km, derived from the accelerometer measurements aboard on the three polar orbiting satellites CHAMP (CHAllenging Minisatellite Payload), GRACE-A (Gravity Recovery and Climate Experiment-A), and SWARM-C (The Earth's Magnetic Field and Environment Explorers-C). The MDM appears during the local times from 23:00 to 02:00 LT (Local Time), whose peak locates at the low latitudes within 15∘ and two valleys locate at the middle latitudes between 35∘ and 45∘ on both hemispheres separately. The structure of MDM drifts toward the southern hemisphere overall. The MDM's amplitude decreases with increases in altitude and solar radiation level. The seasonal effect weakens the MDM's amplitudes around the summer and winter solstices, while the amplitudes around the spring and autumn equinoxes are extremely significant due to the slight seasonal difference between both hemispheres. Three atmospheric density models DTM2000 (Drag Temperature Model 2000), NRLMSISE00 (US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter Radar Extended atmosphere model), and JB2008 (Jacchia-Bowman 2008 model) are used to simulate the MDM along these three satellites' orbits, and compared with the observations. It is found that the JB2008 model is failed to describe the MDM, and the other two models underestimate the MDM's amplitudes at altitudes 360 km and 480 km: the simulated amplitudes by the DTM2000 model are 46% and 53% of the observed amplitudes, respectively, and only 33% and 26% for the NRLMSISE00 model. These three models are also failed to depict the MDM's variation with altitude, solar radiation level, and seasonal effects. In order to correct the model prediction, a 6th-order Legendre polynomial of geographic latitude, coupled with arguments of local time and altitude, is designed to fit the MDM signals from the three satellites' observations. In terms of amplitude and phase of the MDM, the fitting results agree with the observations very well, and the correlation coefficient is 0.923. It indicates that this empirical polynomial could be helpful to the density model correction and high accuracy prediction of spacecrafts in low Earth orbits.

Third‐Order Structure Functions of Zonal Winds in the Thermosphere Using CHAMP and GOCE Observations

AbstractWe use multi‐year observations of cross‐track winds (u) from the CHAllenging Minisatellite Payload (CHAMP) and the Gravity Field and Steady State Ocean Circulation Explorer (GOCE) to calculate third‐order structure functions in the thermosphere as a function of horizontal separation (s). They are computed using the mean (〈δu3〉) and the median and implemented over non‐polar satellite paths in both hemispheres. On height averages, 〈δu3〉 is shown to scale with s2 for s ≃ 80–1,000 km, in agreement with equivalent estimates in the lower atmosphere from aircraft observations. Conversely, follows an s3 power law for almost the whole s range, consistent with the two‐dimensional turbulence scaling law for a direct enstrophy cascade. These scaling laws appear independent of winds in distinct atmospheric regions. Furthermore, the functions are predominantly positive, indicating a preferential cyclonic motion for the wind.

Distinguishing Density and Wind Perturbations in the Equatorial Thermosphere Anomaly

AbstractIn this paper, the equatorial thermosphere anomaly (ETA) is investigated using accelerometer measurements to determine whether the feature is density‐dominated, wind‐dominated, or some combination of the two. An ascending‐descending accelerometry (ADA) technique is introduced to address the density‐wind ambiguity that appears when interpreting the ETA in atmospheric drag acceleration analyses. This technique separates ascending and descending acceleration measurements to determine if a wind's directionality influences the interpretation of the observed ETA feature. The ADA technique is applied to accelerometer measurements taken from the Challenging Minisatellite Payload mission and has revealed that the ETA is primarily density‐dominated from 9:00 to 16:00 local time (LT) near 400 km altitude, with the acceleration perturbations behaving similarly between 2003 and 2004 across all seasons. This finding suggests that the perturbations in the acceleration due to in‐track wind perturbations are small compared to the perturbations due to mass density, while indicating that the formation mechanisms across these local times are similar and persistent. The results also revealed that in the terminator region at 18:00 LT the acceleration perturbations deviate appreciably between ascending and descending passes, indicating different or multiple processes occurring at this local time compared to the 9:00–16:00 LT ascribed to the ETA. These results help constrain ETA formation theories to specific local times and thermospheric property responses without the use of supplemental wind measurements, while also indicating regions where in‐track winds cannot always be neglected.

Machine Learning Based Modeling of Thermospheric Mass Density

AbstractIn this study, we propose a machine learning based approach to construct an empirical model of thermospheric mass densities, based on the MultiLayer Perceptron and bi‐directional Long Short‐Term Memory for ensemble learning model (MBiLE). The MBiLE model was trained by using only the thermospheric mass density from Swarm C satellite at ∼450 km altitude. To assess the performance of the MBiLE model, the model predictions were compared with observations from several satellites, namely, the Swarm C, the Challenging Minisatellite Payload (CHAMP) and the Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE) satellites. The determination coefficients (R2) for the three satellites are 0.98, 0.99, and 0.98, respectively. The MBiLE model predicts the thermospheric mass density well not only at Swarm C altitude but also at lower altitudes. Earlier empirical models based on multivariate least‐square‐fitting approach failed to achieve this good altitude generalization (e.g., Liu et al., 2013, https://doi.org/10.1002/jgra.50144; Xiong et al., 2018a, https://doi.org/10.5194/angeo‐2018‐25). Further tests have been made by checking the MBiLE model prediction deviations in relation to magnetic local time, day of year, solar flux level, and magnetic activities. No obvious dependences are found for these parameters. Comparing with the NRLMSIS‐2.0 model, the MBiLE model improves prediction accuracy by 91%, 66%, and 56% at the three satellites altitudes. The results indicate that the MBiLE model has the ability to predict well the thermospheric mass density over a wide altitude range, for example, from 224 to 528 km, offering potential for atmospheric research applications.

Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

AbstractThe global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space‐based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space‐based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm‐C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm‐B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE‐GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.

Quiet Time Thermospheric Gravity Waves Observed by GOCE and CHAMP

AbstractThe Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE) and CHAllenging Minisatellite Payload (CHAMP) satellites measure in‐situ thermospheric density and cross‐track wind. When propagating obliquely to the satellite track in a horizontal plane (i.e., not purely along‐track or cross‐track), gravity waves (GWs) can be observed both in the density and cross‐track wind perturbations. We employ the Wavelet Analysis, red noise model, dissipative dispersion and polarization relations for thermospheric GWs, and specific criteria to determine whether a quiet‐time (Kp < 3) thermospheric traveling atmospheric disturbances (TADs) event is a GW or not. The first global morphology of thermospheric GWs instead of TADs is reported. The fast intrinsic horizontal phase speed (cIH > 600 m/s) of most GWs suggests that they are not generated in the lower/middle atmosphere (where cIH < 300 m/s). A second population of GWs with slower speeds (cIH = 50–250 m/s) in GOCE are likely from the lower/middle atmosphere, but they occur much less frequently in CHAMP. GW hotspots occur during the high‐latitude and the winter midlatitude regions. GW amplitudes exhibit semi‐annual and annual variations. These findings suggest that most GOCE and CHAMP GWs are higher‐order GWs from primary GW sources in the lower/middle atmosphere. Finally, the average propagation direction of the CHAMP GWs exhibits a clear diurnal cycle, with clockwise (counterclockwise) occurring in the northern (southern) hemisphere and equatorward propagation occurring at ∼13 LST. This suggests that the predominant GW propagation direction is opposite to the background wind direction.

IMF By Effects on the Strength and Latitude of Polar Electrojets: CHAMP and Swarm Joint Observations

AbstractIn this study, the impact of the interplanetary magnetic field (IMF) By component on the peak strength and position of polar electrojets (PEJs) is analyzed, based on 16 years of joint observations from Challenging Minisatellite Payload (CHAMP; 2000–2010) and Swarm (2014–2020) satellites, while focusing on the local time and hemispherical differences. With a more positive IMF By, the duskside eastward PEJ enhances significantly at June solstice in the Northern Hemisphere. Around midnight, the impact of IMF By on the PEJ is more significant in the winter hemisphere; the westward PEJ is stronger in the Northern (Southern) Hemisphere for a more positive (negative) IMF By. In 03–08 MLT, the impact of IMF By is similar in both the winter and summer hemispheres: for IMF By > 0 and By < 0, the PEJ in the dawn sector is stronger in the December and June solstices, respectively. Around noon, a more duskward (dawnward) IMF By component leads to a higher eastward (westward) PEJ in the northern (southern) summer hemisphere. The northern westward (eastward) PEJ peaks at higher latitudes than the southern PEJ for a negative (positive) IMF By.

The Altitudinal Dependences of the Inter‐Hemispheric Asymmetry in the Mid‐Latitude Ionospheric Post‐Midnight Enhancement During Equinox

AbstractUsing observations from multi‐satellites at different altitudes, including the CHAllenging Minisatellite Payload (CHAMP), the Gravity Recovery and Climate Experiment, Swarm B, and Defense Meteorological Satellites Program F17, the ionospheric post‐midnight enhancement at mid‐latitudes and the associated inter‐hemispheric asymmetry during equinox are investigated in this study. During equinox months, the ionospheric electron density enhancement during post‐nighttime at mid‐latitudes is visible in both hemispheres, however, it is asymmetric between the Northern and Southern Hemispheres. At most longitudes, inter‐hemispheric asymmetry of Mid‐latitude Ionospheric Post‐midnight Enhancement (MIPE) reverses with altitudes, from a stronger electron density in the Northern Hemisphere at CHAMP altitude to a stronger electron density in the Southern Hemisphere at top ionosphere during equinox. The reversal altitude and reversal time have significant longitudinal differences. The effective ionospheric uplifting induced by the combination of neutral winds and geomagnetic field configuration is the main contribution to the asymmetry reversal of MIPE at lower altitudes, as shown in the simulations from the SAMI2 and HWM14 models. In comparison with that in the Southern Hemisphere, the stronger neutral winds in the Northern Hemisphere move the plasma along the geomagnetic field lines to a higher altitude with lower chemical recombination, resulting in the enhancement of electron density.

Local Time Variations of Ionospheric F Layer Radial Current in Response to Enhanced Solar Wind Input

AbstractUsing Challenging Minisatellite Payload and the Republic of China Satellite‐1 observations, the response of ionospheric radial current (IRC) in the F region to the enhancement of merging electric field (Em) at different magnetic local times (MLT) is investigated. Possible physical mechanisms are discussed in terms of neutral wind, conductivity, and prompt penetration electric field (PPEF). The disturbance IRC (ΔIRC) increases in the upward (downward) direction in the daytime (nighttime) within 3 hr after Em enhancement. However, disturbance zonal winds increase westward (eastward) at 12–18 MLT (00–06 MLT). The reduced F region electron density may help weaken IRC at 06–12 MLT and 18–24 MLT. This work indicates that the daytime eastward (nighttime westward) PPEF drives equatorward (poleward) Hall current (JH) at low latitudes, resulting in both upward (downward) ΔIRC and eastward (westward) plasma drift at the F region magnetic equator.

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

AbstractUnderstanding the variation of the Thermospheric Mass Density (TMD) is important for solar‐terrestrial physics and applications for spacecraft safety. The thermosphere, as an open system, is impacted by various space environment conditions and has complicated temporal and spatial features. Consequently, TMD observations contain a wealth of multi‐scale feature information. How to extract such information from observations is a challenge that requires ongoing research. It is vital to improving our understanding of the TMD features. Deep learning (DL) can learn complex representations directly from raw data, which makes it a compelling feature extraction and modeling tool for providing a novel perspective for TMD modeling. The Residual Network is used in this study to build a DL model with deep network architecture. The observations of CHAllenging Minisatellite Payload are utilized in the training phase, while the Gravity Recovery and Climate Experiment, High Accuracy Satellite Drag Model and Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model are used to evaluate the performance of the DL model. The results reveal that, compared with the shallow model of the typical Multi‐Layer Perceptron, the DL model can better extract multi‐scale features in the observations while retaining generalization capabilities. Controlled simulation experiments allow us to extract the effects of different physical processes, which improves the interpretability of the DL model. It is demonstrated that the DL model can discriminate the physical processes corresponding to the different space environment indices by simulating Equatorial Mass density Anomaly and geomagnetic storms.

Stochastic modeling of physical drag coefficient – Its impact on orbit prediction and space traffic management

Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. It jeopardizes space safety and long-term sustainability, necessitating better space domain awareness (SDA). Correct modeling of uncertainties in force models and orbital states, among other things, is an essential part of SDA. For objects in the low-Earth orbit (LEO) region, the uncertainty in the orbital dynamics mainly emanate from limited knowledge of the atmospheric drag-related parameters and variables. In this paper, which extends the work by Paul et al. (2021), we develop a feed-forward deep neural network model for the prediction of the satellite drag coefficient for the full range of satellite attitude (i.e., satellite pitch ∈ (-90°,+90°) and satellite yaw ∈ (0°,+360°)). The model simultaneously predicts the mean and the standard deviation and is well-calibrated. We use numerically simulated physical drag coefficient data for training our neural network. The numerical simulations are carried out using the test particle Monte Carlo method using the diffuse reflection with incomplete accommodation gas-surface interaction model. Modeling is carried out for the well-known CHAllenging Minisatellite Payload (CHAMP) satellite. Finally, we use the Monte Carlo approach to propagate CHAMP over a three-day period under various modeling scenarios to investigate the distribution of radial, along-track, and cross-track orbital errors caused by drag coefficient uncertainty. The key takeaways of this paper are - (a) a constant drag coefficient cannot be used for reliable SDA purposes, and (b) stochastic machine learning models allow for the computation of drag coefficients in a timely manner while providing reliable uncertainty estimates.

Comparative Accuracies of Models for Drag Prediction During Geomagnetically Disturbed Periods: A First Principles Model Versus Empirical Models

AbstractWe examine the accuracy of density prediction by the first principles model Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) developed by the National Center for Atmospheric Research and compare it to the accuracy of three empirical models: Jacchia 71, the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Extended 2000 (NRLMSIS), Jacchia 1971, and Jacchia‐Bowman 2008. Comparisons are made for three large storms: the October 2003 storm, the March 2013 storm, and the March 2015 storm. To evaluate the accuracy of these models we use tracking data for nine space objects in low Earth orbit. Additionally, we evaluate the accuracy of the TIEGCM and NRLMSIS with data from high precision accelerometers on the Challenging Minisatellite Payload (CHAMP) and Gravity field and Circulation Explorer (GOCE) satellites. The goal is to assess the use of a first principles model as a potential tool for forecasting satellite drag during large magnetic storms. For the storms considered, we found the TIEGCM, JB2008, and NRLMSIS models to be substantially more accurate than the Jacchia 71 model. The accuracies of the TIEGCM and JB2008 models were similar, but overall, the TIEGCM was more accurate. We found smaller differences for TIEGCM versus CHAMP than for NRLMIS for the Halloween Storm, and smaller differences than results published for JB2008 and the assimilative model HASDM. The empirical models are at present more practical for operational purposes, but the TIEGCM, developed as a research model, with a greater focus on operational use offers the potential for improved utility during stressing conditions.

Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response

AbstractWe present results from a study of the time lags between changes in the energy flow into the polar regions and the response of the thermosphere to the heating. Measurements of the neutral density from the Challenging Mini‐satellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) missions are used, along with calculations of the total Poynting flux entering the poles. During two major geomagnetic storms in 2003, these data show increased densities are first seen on the dayside edge of the auroral ovals after a surge in the energy input. At lower latitudes, the densities reach their peak values on the dayside earlier than on the night side. A puzzling response seen in the CHAMP measurements during the November 2003 storm was that the density at a fixed location near the “Harang discontinuity” remained at unusually low levels during three sequential orbit passes, while elsewhere the density increased. The entire database of measurements from the CHAMP and GRACE missions were used to derive maps of the density time lags across the globe. The maps show a large gradient between short and long time delays between 60° and 30° geographic latitude. They confirm the findings from the two storm periods, that near the equator, the density on the dayside responds earlier than on the nightside. The time lags are longest near 18–20 hr local time. The time lag maps could be applied to improve the accuracy of empirical thermosphere models, and developers of numerical models may find these results useful for comparisons with their calculations.

Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement

AbstractAt 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.

Multi‐Model Ensembles for Upper Atmosphere Models

AbstractMulti‐model ensembles (MMEs) are used to improve the forecasts of thermospheric neutral densities. A variety of algorithms for constructing the model weights for the MMEs are described and have been implemented including: performance weighting, independence weighting, and non‐negative least squares. Using both empirical and physics‐based models, compared against in situ Challenging Minisatellite Payload (CHAMP) observations, the skill of each MME weighting approach has been tested in both solar minimum and maximum conditions. In both cases the MME performs better than any individual model. A non‐negative least squares weighting for the MME on a set of bias corrected models provides a 68% and 50% reduction in the mean square error compared to the best model (Jacchia‐Bowman 2008) in the solar minimum and maximum cases, respectively.

Topside Ionosphere Sounding From the CHAMP, GRACE, and GRACE‐FO Missions

AbstractSatellites in Low Earth Orbit (LEO) are essential for sounding the topside ionosphere. In this work, we present and validate a data set of Total Electron Content (TEC) and in situ electron density observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE‐Follow‐On missions as well as a TEC data set from the CHAllenging Minisatellite Payload mission. Concerning TEC, special emphasis is put to ensure optimal consistency to the already existing Swarm and Gravity field and steady‐state ocean circulation explorer (GOCE) TEC data sets. The newly processed satellite missions allow covering two full solar cycles with LEO slant TEC. Furthermore, the twin satellite missions GRACE and GRACE‐FO equipped with inter‐satellite K‐band ranging allows to derive the horizontal TEC and, due to the small inter‐satellite distance of the satellite pairs, an approximation for local electron density. However, the derived value of electron density is relative and requires calibration using external information. In this work, the calibration is performed using the IRI‐2016 model. Radar observations, as well as in situ electron density observations available from Swarm B Langmuir probes, are used for validation. Conjunctions between satellites are used to validate the TEC time series. The newly derived data set is shown to be highly consistent with the already existing data sets with standard deviations below 3 TECU for TEC (even 1 TECU was reached for low solar flux) and an offset below 7 × 1010 m−3 with a standard deviation near 1 × 1011 m−3 for the electron density.

Solar Flux Dependence of Upper Thermosphere Diurnal Variations: Observed and Modeled

AbstractUpper thermosphere mass density over the declining phase of solar cycle 23 is investigated using a day‐to‐night ratio (DNR) of thermosphere properties to evaluate how much relative change occurs climatologically between day and night. Challenging Minisatellite Payload (CHAMP) observations from 2002 to 2009, MSIS 2.0 output, and TIEGCM V2.0 simulations are analyzed to assess their relative response in DNR. The CHAMP observations demonstrate nightside densities decrease more significantly than dayside densities as solar flux decreases. This causes a steadily increasing CHAMP mass density DNR from around two to greater than four with decreasing solar flux. The MSIS 2.0 nightside densities decrease, with decreasing solar flux, more significantly than the dayside, resulting in the same trend as CHAMP. TIEGCM V2.0 displays an opposite trend in density DNR with decreasing solar flux due to dayside densities decreasing more significantly than nightside densities. A sensitivity analysis of the two models reveals the TIEGCM V2.0 to have greater sensitivity in temperature to levels of solar flux, while MSIS 2.0 displayed a greater sensitivity in mean molecular weight. The pressure DNR from both models contributed the most to the density DNR value at 400 km. As solar flux decreases, the two models' estimate of pressure DNR deviate appreciably and trend in opposite directions. The TIEGCM V2.0 dayside temperatures during middle‐to‐low solar flux are too cold relative to MSIS 2.0. Increasing the dayside temperature values by about 50–100 K and decreasing the nightside temperature slightly would bring the TIEGCM V2.0 into better agreement with MSIS 2.0 and CHAMP observations.

Swarm A and C Accelerometers: Data Validation and Scientific Interpretation

AbstractThe fourth European Space Agency Earth Explorer (Swarm) was launched in 2013 and it comprises a constellation of three identical satellites. The primary mission objective is analyzing and modeling the geomagnetic field and its temporal evolution, based on the measurements of a vector and a scalar magnetometer on board. Each spacecraft (A, B, and C) carries an accelerometer, supposed to measure the non‐gravitational forces. However, this instrument did not work as expected; the processing of the related data sets has a significant latency. The focus was primarily on Swarm C, because its signal‐to‐noise ratio is the best amongst the three spacecrafts. For this satellite, the data are available for the whole mission timeline. At the end of 2021, a batch of Swarm A accelerometer data set was disseminated. Swarm A and C have flown side‐by‐side for most of the mission and, therefore, their measurements are supposed to be nearly identical after calibration. The work presented in this paper shows that such a correspondence occurs: at high frequency similar signatures are visible in both accelerometer data sets, near the equator and the poles. These features agree with measurements of other instruments on board and with earlier findings based on CHAllenging Minisatellite Payload and Gravity Recovery and Climate Experiment data. The equatorial structures show a correlation between the equatorial mass anomaly and the equatorial ionization anomaly. At the poles, the data are related to the polar cap index and to field‐aligned‐currents enhancements measured by the magnetometers on board. This research shows, for the first time, scientific results obtained from the Swarm accelerometers.