A simultaneous calibration and data assimilation (C/DA) to improve NRLMSISE00 using thermospheric neutral density (TND) from space-borne accelerometer measurements

Abstract

Summary Improving Thermospheric Neutral Density (TND) estimates is important for computing drag forces acting on Low-Earth-Orbit (LEO) satellites and debris. Empirical thermospheric models are often used to compute TNDs for the precise orbit determination experiments. However, it is known that simulating TNDs are of limited accuracy due to simplification of model structure, coarse sampling of model inputs, and dependencies to the calibration period. Here, we apply TND estimates from accelerometer measurements of the Challenging Minisatellite Payload (CHAMP) and the Gravity Recovery and Climate Experiment (GRACE) missions as observations to improve the NRLMSISE-00 model, which belongs to the Mass Spectrometer and Incoherent Scatter (MSIS) family of models. For this, a novel simultaneous Calibration and Data Assimilation (C/DA) technique is implemented that uses the Ensemble Kalman Filter (EnKF) and the Ensemble Square-Root Kalman Filter (EnSRF) as merger. The application of C/DA is unique because it modifies both model-derived TNDs, as well as the selected model parameters. The calibrated parameters derived from C/DA are then used to predict TNDs in locations that are not covered by CHAMP and GRACE orbits, and forecasting TNDs of the next day. The C/DA is implemented using daily CHAMP- and/or GRACE-TNDs, for which compared to the original model, we find 27% and 62% reduction of misfit between model and observations in terms of Root Mean Square Error (RMSE) and Nash Coefficient, respectively. These validations are performed using the observations along the orbital track of the other satellite that is not used in the C/DA during 2003 with various solar activity. Comparisons with another empirical model, i.e., Jacchia-Bowman 2008, indicate that the C/DA results improve these quality measurements on an average range of 50% and 60%, respectively.

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

“…Panel (a) presents time series of the mean TND estimates derived from GRACE and Swarm missions along their orbits at their original altitude during February 2015, and (b) represents them at the common altitude of 400 km. The height-dependent function and vertical profile of NRLMSISE-00 model was used to transform the TNDs to a common altitude of 400 km (see the Supplementary file Section S3 online), which makes it easier to compare the consistency of TNDs derived from GRACE and Swarm-A/-B/-C 47 . In fact, this figure demonstrates that a bias exists between the TND estimates of various satellite missions and the mean bias of kg/ is estimated between the four TND estimates of Fig.…”

“…Besides, they are not very efficient for forecasting. (4) The calibration and data assimilation (C/DA) can provide the opportunity to update the model’s states (similar to DA) and can simultaneously calibrate the model’s key parameters 46 , 47 . The advantage of this approach is that the calibrated parameters can be used to simulate TNDs in locations outside of the orbital tracks of LEO satellite, or to forecast TNDs.…”

“…The methodology of this study follows that of Forootan et al 47 , categorized as (4), who proposed a C/DA method based on the sequential ensemble Kalman filter (EnKF) to calibrate the NRLMSISE-00 model 22 . In their study, CHAMP and GRACE TND estimates, normalised at 400 km altitude, were used as observation.…”

CASPA-ADM: a mission concept for observing thermospheric mass density

“…In addition to the scientific objectives, the measurements of CASPA-ADM are expected to be useful for near real-time assimilation in thermosphere models. Some authors claim improvements of 20% in the accuracy of the thermosphere density [16], which is expected to significantly enhance the accuracy for operational orbit prediction and collision warning. This would require to transmit and process the data 1-2 times per day in an automated and robust way.…”

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

“…For the calibration methods of the three-axis accelerometer, currently, there is no statistically unified standard; the most popular calibration method is a back-to-back comparison calibration method. The standard accelerometer and the three-axis accelerometer are installed using the back-to-back method and the acceleration, which is measured by the standard accelerometer, is applied as the input of the three-axis accelerometer (Deka et al, 2021;Forootan et al, 2021;Zhang et al, 2021). This method has the advantages of having a simple principle, convenient operation and low-cost fabrication, which have made the method increasingly acceptable as an important solution to calibrate three-axis accelerometers (Yu et al, 2020;Yeh et al, 2020).…”