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

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 $$\sim 410$$ ∼ 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 ($$\sim 467$$ ∼ 467 km), -B ($$\sim 521$$ ∼ 521 km), and -C ($$\sim 467$$ ∼ 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.

How can along-track accelerations be used to predict global and multi-level thermospheric neutral density?

“…The C/DA methodology was recently applied in (Forootan et al, 2022) to re-calibrate the commonly used NRLMSISE-00 empirical thermosphere model (Picone et al, 2002) against the TND estimates of GRACE (at the altitude of ∼ 410 km during February 2015).…”

“…In response to the "ESA Swarm DISC programme 2021's Open Call for Ideas for New data products, tools and services for Swarm", (Forootan, 2023) proposed to use the C/DA approach of (Forootan et al, 2022) to leverage the publicly available CHAMP, Swarm, and GRACE derived along-track TND data, and producing a global multi-level TND product. Therefore, unlike other available global DA derived TND outputs, this multi-level estimate is purely based on openly available models and measurements.…”

Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density

“…There exist numerous examples of assimilating different observations into numerical models to improve the TI estimation and forecast. To give some examples: TMD from low earth orbiting satellites (Matsuo et al 2012;Codrescu et al 2018;Ren and Lei 2020;Sutton 2018;Forootan et al 2022), thermospheric winds , TEC (Chartier et al 2016;Chen et al 2016b;Scherliess et al 2004;Solomentsev et al 2014), electron density (Chen et al 2016a;He et al 2019;Kodikara et al 2021), FUV [simulations by Fuller-Rowell et al (2004); Cantrall et al (2019)], thermosphere temperature (Laskar et al 2021) andcombinations [e.g., He et al (2021); Pedatella et al (2020)]. Because of the importance of an accurate representation of the ionosphere for communication and navigation systems, great attention has been given to data assimilation of electron density or TEC to improve the ionospheric state.…”

“…This is of special importance when the conditions in the upper atmosphere change fast, like during storm periods (Chartier et al 2013;Chen et al 2016b). In addition, some studies like Forootan et al (2022), evaluate the effects of TMD assimilation into the TI system. However, the impact of TMD DA on the global accuracy of the ionosphere electron density estimation during a major storm event has not been investigated or quantified yet.…”

Improving the estimation of thermospheric neutral density via two-step assimilation of in-situ neutral density into a numerical model

“…The model results were improved when comparing to CHAMP and GRACE observations. Forootan et al (2022) have applied a calibration and data assimilation technique to the empirical NRLMSISE-00 model using observations from the GRACE accelerometer.…”