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1. Estimation of Ion Temperature in the Upper Ionosphere Along the Swarm Satellite Orbits

   https://doi.org/10.1029/2021EA001925  

   Lomidze, L. ; Burchill, J. K. ; Knudsen, D. J. ; Huba, J. D. ( November 2021 , Earth and Space Science)

   Ion temperature is one of the key parameters that provides insight into the thermal balance of the coupled ionosphere‐thermosphere system. Together with the temperatures of neutral and electron gases, it affects physical and chemical processes and parameters in the upper atmosphere. These include the ion‐neutral collision frequencies, chemical reaction rates and plasma scale height, all of which influence the variation and distribution of ionospheric plasma. The European Space Agency's three, nearly polar‐orbiting Swarm satellites at about 500 km altitude measure ionospheric electron temperature, density, and ion drifts using electric field instrument (EFI) Langmuir probes and Thermal Ion Imagers. Measurements of the ion temperature, though initially planned, are not available due to technical problems with the ion imagers. This paper describes a model that estimates the ion temperature along the orbits of Swarm satellites and evaluates the validity of the corresponding data. This data‐driven, physics‐based model combines an ion heat balance equation of the upper ionosphere, the Swarm EFI measurements, and empirical models for neutral composition, winds, and electric field. The validity of this approach was investigated using a physics‐based ionosphere model (SAMI3) for different geophysical conditions. We have studied the effects of various assumptions and input data limitations to the model accuracy, and have validated the estimated ion temperature against independent measurements from low, middle, and high‐latitude incoherent scatter radars (ISRs). When compared with the ISR data, the obtained Swarm‐based ion temperature shows small systematic errors (1%–2%), high correlations (Swarm A/C 0.8, Swarm B 0.6), and random errors of 10%–20%.

    

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2. Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application

   https://doi.org/10.1038/s41598-022-11049-3  

   Licata, Richard J. ; Mehta, Piyush M. ( May 2022 , Scientific Reports)

   Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

    

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3. Lower-thermosphere–ionosphere (LTI) quantities: current status of measuring techniques and models

   https://doi.org/10.5194/angeo-39-189-2021  

   Palmroth, Minna ; Grandin, Maxime ; Sarris, Theodoros ; Doornbos, Eelco ; Tourgaidis, Stelios ; Aikio, Anita ; Buchert, Stephan ; Clilverd, Mark A. ; Dandouras, Iannis ; Heelis, Roderick ; et al ( January 2021 , Annales Geophysicae)

   Abstract. The lower-thermosphere–ionosphere (LTI) system consists of the upper atmosphere and the lower part of the ionosphere and as such comprises a complex system coupled to both the atmosphere below and space above. The atmospheric part of the LTI is dominated by laws of continuum fluid dynamics and chemistry, while the ionosphere is a plasma system controlled by electromagnetic forces driven by the magnetosphere, the solar wind, as well as the wind dynamo. The LTI is hence a domain controlled by many different physical processes. However, systematic in situ measurements within this region are severely lacking, although the LTI is located only 80 to 200 km above the surface of our planet. This paper reviews the current state of the art in measuring the LTI, either in situ or by several different remote-sensing methods. We begin by outlining the open questions within the LTI requiring high-quality in situ measurements, before reviewing directly observable parameters and their most important derivatives. The motivation for this review has arisen from the recent retention of the Daedalus mission as one among three competing mission candidates within the European Space Agency (ESA) Earth Explorer 10 Programme. However, this paper intends to cover the LTI parameters such that it can be used as a background scientific reference for any mission targeting in situ observations of the LTI. 

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4. Maximizing Mission Utility within Operational Constraints for the SWARM-EX CubeSat Mission

   https://doi.org/https://doi.org/10.2514/6.2022-0236  

   David J. Fitzpatrick, Evan Bauch ( September 2022 , AIAA SCITECH 2022 Forum)

   With space more accessible than ever, academic institutions like the University of Colorado (CU) Boulder have exhibited that CubeSats (compact, homogeneous, rectangular satellites with masses below 14 [kg]) can be leveraged for remarkable space missions capable of making significant advances to both scientific and technological fields. One such CubeSat project is the NSF-funded Space Weather Atmospheric Reconfigurable Multiscale Experiment (SWARM-EX), which will launch three 3U CubeSats into a “swarm” that will demonstrate autonomous formation flying capabilities while simultaneously studying the spatial and temporal variability of ion-neutral interactions in the equatorial Ionosphere-Thermosphere region. Although the small stature of CubeSats and their standardized deployer options help to lower unit development cost and facilitate launch opportunities, the physical size limits of CubeSats prove to be a double-edged sword vis-à-vis sustaining a stable power state while hosting instruments with high power demands and often strict pointing requirements. For SWARM-EX, this issue is magnified by the mission’s ambitious goals; to comply with mission requirements, a SWARM-EX spacecraft is required to concurrently (1) point the science instruments no more than 30° off ram when they are operational, (2) point the GNSS patch antenna no more than 30° off zenith when the spacecraft are separated by ≤ 10 [km], (3) point the X-Band patch antenna no more than 18° off boresight from the ground station during downlink, (4) maximize the differential ballistic coefficient during differential drag maneuvers, and (5) maximize solar array power generation at all times. Consequently, advanced CubeSat Missions like SWARM-EX require innovative systems engineering solutions to remain power-positive during on-orbit operations. Through a combination of intricate pointing profiles, orbital simulations, a comprehensive and coordinated ConOps, battery state of charge simulation tools, and expertise from previous CubeSat missions, the SWARM-EX team has conceived a plan to successfully meet all these mission requirements; it is the aim of the authors to illuminate these strategies as a case study. 

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5. The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data

   https://doi.org/10.1029/2020SW002660  

   Kodikara, Timothy ; Zhang, Kefei ; Pedatella, Nicholas Michael ; Borries, Claudia ( May 2021 , Space Weather)

   This study presents a comprehensive comparison of the impact of solar activity on forecasting the upper atmosphere through assimilation of radio occultation (RO)‐derived electron density (Ne) into a physics‐based model (TIE‐GCM) using an ensemble Kalman filter (KF). Globally abundant RO‐derivedNeoffers one of the most promising means to test the effect of assimilation on the model forecasted state on a global scale. This study emphasizes the importance of understanding how the assimilation results vary with solar activity, which is one of the main drivers of thermosphere‐ionosphere dynamics. This study validates the forecast states with independent RO‐derived GRACE (Gravity Recovery and Climate Experiment mission)Nedata. The principal result of the study is that the agreement between forecastNeand data is better during solar minimum than solar maximum. The results also show that the agreement between data and forecast is mostly better than that of the standalone TIE‐GCM driven with observed geophysical indices. The results emphasize that TIE‐GCM significantly underestimateNein altitudes below 250 km and the assimilation ofNeis not as effective in these lower altitudes as it is in higher altitudes. The results demonstrate that assimilation ofNesignificantly impacts the neutral mass density estimates via the KF state vector—the impact is larger during solar maximum than solar minimum relative to a control case that does not assimilateNe. The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique.

    

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