Forecasting global and multi-level thermospheric neutral density and ionospheric electron content by tuning models against satellite-based accelerometer measurements

Global estimation of thermospheric neutral density (TND) on various altitudes is important for geodetic and space weather applications. This is typically provided by models, however, the quality of these models is limited due to their imperfect structure and the sensitivity of their parameters to the calibration period. Here, we present an ensemble Kalman filter (EnKF)-based calibration and data assimilation (C/DA) technique that updates the model’s states and simultaneously calibrates its key parameters. Its application is demonstrated using the TND estimates from on-board accelerometer measurements, e.g., those of the Gravity Recovery and Climate Experiment (GRACE) mission (at ∼410 km altitude), as observation, and the frequently used empirical model NRLMSISE-00. The C/DA is applied here to re-calibrate the model parameters including those controlling the influence of solar radiation and geomagnetic activity as well as those related to the calculation of exospheric temperature. The resulting model, called here ‘C/DA-NRLMSISE-00’, is then used to now-cast TNDs and individual neutral mass compositions for 3 h, where the model with calibrated parameters is run again during the assimilation period. C/DA-NRLMSISE-00 is also used to forecast the next 21 h, where no new observations are introduced. These forecasts are unique because they are available globally and on various altitudes (300–600 km). To introduce the impact of the thermosphere on estimating ionospheric parameters, the coupled physics-based model TIE-GCM is run by replacing the O2, O1, He and neutral temperature estimates of the C/DA-NRLMSISE-00. Then, the non-assimilated outputs of electron density (Ne) and total electron content (TEC) are validated against independent measurements. Assessing the forecasts of TNDs with those along the Swarm-A (∼467 km), -B (∼521 km), and -C (∼467 km) orbits shows that the root-mean-square error (RMSE) is considerably reduced by 51, 57 and 54%, respectively. We find improvement of 30.92% for forecasting Ne and 26.48% for TEC compared to the radio occulation and global ionosphere maps (GIM), respectively. The presented C/DA approach is recommended for the short-term global multi-level thermosphere and enhanced ionosphere forecasting applications

Preliminary Validation of Thermosphere Observations from the TOLEOS Project

OBSERVATIONS of upper atmospheric neutral mass density (NMD) and wind are critical to understand the coupling mechanisms between Earth’s ionosphere, thermosphere, and magnetosphere. The ongoing Swarm DISC (data, innovation, and science cluster) project TOLEOS (thermosphere observations from low-Earth orbiting satellites) aims to provide better calibrated NMD and crosswind data from CHAMP, GRACE, and GRACE-FO (follow-on) satellite missions. The project uses state-of-the-art models, calibration techniques, and processing standards to improve the accuracy of these data products and ensure inter-mission consistency. Here, we present preliminary results of the quality of the data in comparison to the high accuracy drag temperature model DTM2020, and physics-based TIE-GCM (thermosphere ionosphere electrodynamics general circulation model) and CTIPe (coupled thermosphere ionosphere plasmasphere electrodynamics) models

TOLEOS: Thermosphere Observations from Low-Earth Orbiting Satellites

The objective of the TOLEOS project is to process the CHAMP, GRACE, and GRACE-FO accelerometer measurements with improved processing standards to obtain thermosphere density and crosswind data products. These new data products will cover the entirety of the accelerometer missions and complement the existing ESA databases for Swarm and GOCE. The improvements in the processing focus on the radiation pressure modelling, which is expected to have a significant effect on the density and crosswind data, in particular at altitudes above 450 km during solar minimum conditions. Substantial validation activities are performed since the project’s start in June 2021 and will continue until the end of the project in July 2022

Extended forward and inverse modeling of radiation pressure accelerations for LEO satellites

For low Earth orbit (LEO) satellites, activities such as precise orbit determination, gravity field retrieval, and thermospheric density estimation from accelerometry require modeled accelerations due to radiation pressure. To overcome inconsistencies and better understand the propagation of modeling errors into estimates, we here suggest to extend the standard analytical LEO radiation pressure model with emphasis on removing systematic errors in time-dependent radiation data products for the Sun and the Earth. Our extended unified model of Earth radiation pressure accelerations is based on hourly CERES SYN1deg data of the Earth’s outgoing radiation combined with angular distribution models. We apply this approach to the GRACE (Gravity Recovery and Climate Experiment) data. Validations with 1 year of calibrated accelerometer measurements suggest that the proposed model extension reduces RMS fits between 5 and 27%, depending on how measurements were calibrated. In contrast, we find little changes when implementing, e.g., thermal reradiation or anisotropic reflection at the satellite’s surface. The refined model can be adopted to any satellite, but insufficient knowledge of geometry and in particular surface properties remains a limitation. In an inverse approach, we therefore parametrize various combinations of possible systematic errors to investigate estimability and understand correlations of remaining inconsistencies. Using GRACE-A accelerometry data, we solve for corrections of material coefficients and CERES fluxes separately over ocean and land. These results are encouraging and suggest that certain physical radiation pressure model parameters could indeed be determined from satellite accelerometry data.Deutsches Zentrum für Luft- und Raumfahrt http://dx.doi.org/10.13039/501100002946ftp://ftp.tugraz.at/outgoing/ITSG/tvgogo/orbits/GRACE/ftp://podaac-ftp.jpl.nasa.gov/allData/grace/L1B/JPL

Extended forward and inverse modeling of radiation pressure accelerations for LEO satellites

<jats:title>Abstract</jats:title><jats:p>For low Earth orbit (LEO) satellites, activities such as precise orbit determination, gravity field retrieval, and thermospheric density estimation from accelerometry require modeled accelerations due to radiation pressure. To overcome inconsistencies and better understand the propagation of modeling errors into estimates, we here suggest to extend the standard analytical LEO radiation pressure model with emphasis on removing systematic errors in time-dependent radiation data products for the Sun and the Earth. Our extended unified model of Earth radiation pressure accelerations is based on hourly CERES SYN1deg data of the Earth’s outgoing radiation combined with angular distribution models. We apply this approach to the GRACE (Gravity Recovery and Climate Experiment) data. Validations with 1 year of calibrated accelerometer measurements suggest that the proposed model extension reduces RMS fits between 5 and 27%, depending on how measurements were calibrated. In contrast, we find little changes when implementing, e.g., thermal reradiation or anisotropic reflection at the satellite’s surface. The refined model can be adopted to any satellite, but insufficient knowledge of geometry and in particular surface properties remains a limitation. In an inverse approach, we therefore parametrize various combinations of possible systematic errors to investigate estimability and understand correlations of remaining inconsistencies. Using GRACE-A accelerometry data, we solve for corrections of material coefficients and CERES fluxes separately over ocean and land. These results are encouraging and suggest that certain physical radiation pressure model parameters could indeed be determined from satellite accelerometry data.</jats:p&gt

Thermospheric neutral density from accelerometer measurements of GRACE, CHAMP and Swarm

TND-IGG RL01: This dataset is the first release of thermospheric neutral densities (TND) processed at the Institute of Geodesy and Geoinformation (IGG), University of Bonn, Germany. TNDs are derived from accelerometer measurements of the satellites GRACE-A, CHAMP and Swarm-C. For GRACE-A and CHAMP we first calibrate the accelerometer data within a precise orbit determination procedure (Vielberg et al., 2018). For Swarm-C we use the calibrated along-track accelerations from ESA (Siemes et al., 2016). In a second step, solar and Earth radiation pressure accelerations according to Vielberg and Kusche (2020) are reduced from the calibrated accelerometer data. The resulting atmospheric drag is then related to the thermospheric neutral density following the direct procedure by Doornbos et al. (2010) with temperature and density of atmospheric constituents from the empirical model NRLMSIS2.0. We apply an accommodation coefficient of 0.93 for GRACE, 0.82 for Swarm and 0.85 for CHAMP. Detailed information about the processing can be found in the ReadMe.txt and in Vielberg et al. (2021, in review). The final thermospheric neutral densities with a temporal resolution of 10 seconds are provided as monthly netCDF files

Forecasting Global Thermospheric Neutral Density through Calibration and Data Assimilation of GRACE Measurements into the NRLMSISE-00 model

The uncertainty in thermospheric neutral density (TND) estimates is one of the largest and persistent sources of uncertainty in orbit determination and prediction (OD/OP) of low Earth orbit space objects. The TNDs required for these applications are typically obtained from corresponding models. However, the simulation and forecasting skills of these models are limited due to the model structures and the calibration period of the model parameters. Here, we present an Ensemble Kalman Filter (EnKF)-based Calibration and Data Assimilation (C/DA) approach that provides the opportunity to update the model's states and simultaneously calibrates the model’ s most sensitive parameters, such as those related to solar radiation and geomagnetic activity as well as those controlling the calculation of exospheric temperature. The advantageof this approachis that the calibrated parameters can be applied to simulate the global map of global TNDs and forecasting them in future. In this study, we investigate the improvement of the NRLMSISE-00 model after implementing the C/DA scheme using TNDsderived from the accelerometer measurements of the Gravity Recovery and Climate Experiment mission (GRACE) during February 2015 with a wide range of solar activity. We demonstrate the forecasting skills of C/DA covering the altitude of 300-600 km, though the GRACE measurements were introduced at the altitude of 410 km during the C/DA period. The calibrated model are validated along the Swarm-A, -B, and -C with mean altitude of 480, 480 and 528 km,respectively. The results indicate that our TND forecasts agree well with the POD-derived densities. After implementing the C/DA, the root-mean-squares of error (RMSE) of TND forecasts has been reduced comparedto the original NRLMSISE-00 densities,i.e., 51, 57 and 54% along the Swarm-A,-B and -C, respectively. The numerical assessment is useful to demonstrate the capability of the C/DA technique in reducing the modelling errors and their value for forecasting TNDs for applications such as collision analysis

Scale Factors of the Thermospheric Density: A Comparison of Satellite Laser Ranging and Accelerometer Solutions

A major problem in the precise orbit determination (POD) of satellites at altitudes below 1,000 km is the modeling of the aerodynamic drag which mainly depends on the thermospheric density and causes the largest non‐gravitational acceleration. Typically, empirical thermosphere models are used to calculate density values at satellite positions but current thermosphere models cannot provide the required accuracy. Thus, unaccounted variations in the thermospheric density may lead to significantly incorrect satellite positions. For the first time, we bring together thermospheric density corrections for the NRLMSISE‐00 model in terms of scale factors with a temporal resolution of 12 hr derived from satellite laser ranging (SLR) and accelerometer measurements. Whereas, the latter are in situ information given along the satellite orbit, SLR results have to be interpreted as mean values along the orbit within the underlying time interval. From their comparison, we notice a rather similar behavior with correlations of up to 80% and more depending on altitude. During high solar activity, scale factors vary around 30% at low solar activity and up to 70% at high solar activity from the value one. In addition, we found the scaled thermospheric density decreasing stronger as the modeled density of NRLMSISE‐00. To check the reliability of the SLR‐derived scale factors, we compare the POD result of two different software packages, namely DOGS‐OC from DGFI‐TUM and GROOPS from IGG Bonn. Furthermore, a validation of our estimated scale factors with respect to an external data set proofs the high quality of the obtained results.Plain Language Summary: Variations in the density of the thermosphere must be taken into account when modeling and predicting the motion of satellites in the near‐Earth environment. Typically, thermospheric densities at the position of satellites are provided by models, but their accuracy is limited. Due to the sensitivity of satellites orbiting the Earth in the altitude range of the thermosphere, they can be used to derive information about the thermospheric density. In this study, we compare for the first time thermospheric density corrections in terms of scale factors for the NRLMSISE‐00 model with a temporal resolution of 12 hr derived from two geodetic measurement techniques, namely satellite laser ranging (SLR) and accelerometry. Our results demonstrate that both measurement techniques can be used to derive comparable scale factors of the thermospheric density, which vary around the desired value one. This indicates to which extent the NRLMSISE‐00 model differs from the observed thermospheric density. On average, during high solar activity, the model underestimates the thermospheric density and can be scaled up using the estimated scale factors. We additionally discuss our estimated scale factors with respect to an external data set. Furthermore, we validate the approach of deriving scale factors from SLR measurements by using two independent software packages.Key Points: For the first time, we compare scale factors of the thermospheric density derived from satellite laser ranging (SLR) and accelerometer measurements. The estimated scale factors vary by up to 30% at low solar activity and up to 70% at high solar activity from the desired value 1. Correlations of 0.7–0.8 are obtained between the estimated scale factors from SLR and accelerometer measurements depending on the height.German Research Foundation (DFG)Technical University of Munich (TUM

New Thermosphere Neutral Mass Density and Crosswind Datasets from CHAMP, GRACE, and GRACE-FO

We present new neutral mass density and crosswind observations for the CHAMP, GRACE, and GRACE-FO missions, filling the last gaps in our database of accelerometer-derived thermosphere observations. For consistency, we processed the data over the entire lifetime of these missions, noting that the results for GRACE in 2011–2017 and GRACE-FO are entirely new. All accelerometer data are newly calibrated. We modeled the temperature-induced bias variations for the GRACE accelerometer data to counter the detrimental effects of the accelerometer thermal control deactivation in April 2011. Further, we developed a new radiation pressure model, which uses ray tracing to account for shadowing and multiple reflections and calculates the satellite’s thermal emissions based on the illumination history. The advances in calibration and radiation pressure modeling are essential when the radiation pressure acceleration is significant compared to the aerodynamic one above 450 km altitude during low solar activity, where the GRACE and GRACE-FO satellites spent a considerable fraction of their mission lifetime. The mean of the new density observations changes only marginally, but their standard deviation shows a substantial reduction compared to thermosphere models, up to 15% for GRACE in 2009. The mean and standard deviation of the new GRACE-FO density observations are in good agreement with the GRACE observations. The GRACE and CHAMP crosswind observations agree well with the physics-based TIE-GCM winds, particularly the polar wind patterns. The mean observed crosswind is a few tens of m · s−1 larger than the model one, which we attribute primarily to the crosswind errors being positive by the definition of the retrieval algorithm. The correlation between observed and model crosswind is about 60%, except for GRACE in 2004–2011 when the signal was too small to retrieve crosswinds reliably

TOLEOS: Thermosphere Observations from Low-Earth Orbiting Satellites

The TOLEOS project will provide new thermosphere density and crosswind observations derived from the accelerometer data of the CHAMP, GRACE, and GRACE-FO missions. The accurate calibration of the accelerometer data and the upgrade of the radiation pressure model are key elements of the project, which is funded by the Swarm Data, Innovation, and Science Cluster (Swarm DISC). To improve the radiation pressure modelling, we use ray tracing techniques in combination with high-fidelity geometry models of the satellites, which were augmented with the thermo-optical properties of the surfaces. This substantially reduces the uncertainty stemming from the satellite geometry modelling and shadowing effects. In addition, we introduce thermal models of the satellites to account for the radiation of heat from the satellites themselves. We will elaborate the accelerometer data calibration and briefly explain the upgraded radiation pressure modelling. Further, we will compare the new thermosphere density and crosswind observations to the existing observations to the highlight the differences and demonstrate the effects of the upgraded processing