ABSTRACT
Estimating global and multi-level Thermosphere Neutral Density (TND) is important for studying coupling processes within the upper atmosphere, and for applications like orbit prediction. Models are applied for predicting TND changes, however, their performance can be improved by accounting for the simplicity of model structure and the sampling limitations of model inputs. In this study, a simultaneous Calibration and Data Assimilation (C/DA) algorithm is applied to integrate freely available CHAMP, GRACE, and Swarm derived TND measurements into the NRLMSISE-00 model. The improved model, called 'C/DA-NRLMSISE-00', and its outputs fit to these measured TNDs, are used to produce global TND fields at arbitrary altitudes (with the same vertical coverage as the NRLMSISE-00). Seven periods, between 2003-2020 that are associated with relatively high geomagnetic activity selected to investigate these fields, within which available models represent difficulties to provide reasonable TND estimates. Independent validations are performed with along-track TNDs that were not used within the C/DA framework, as well as with the outputs of other models such as the Jacchia-Bowman 2008 and the High Accuracy Satellite Drag Model. The numerical results indicate an average 52%, 50%, 56%, 25%, 47%, 54%, and 63% improvement in the Root Mean Squared Errors of the short term TND forecasts of C/DA-NRLMSISE00 compared to the along-track TND estimates of GRACE (2003, altitude 490 km), GRACE (2004, altitude 486 km), CHAMP (2008, altitude 343 km), GOCE (2010, altitude 270 km), Swarm-B (2015, altitude 520 km), Swarm-B (2017, altitude 514 km), and Swarm-B (2020, altitude 512 km), respectively.
ABSTRACT
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 [Formula: see text] 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 ([Formula: see text] km), -B ([Formula: see text] km), and -C ([Formula: see text] 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.
