The Impact of Severe Storms on Forecasting the Ionosphere-Thermosphere system through the assimilation of SWARM-derived neutral mass density into physics-based models

The response of the Ionosphere - Thermosphere (IT) system to severe storm conditions is of great importance to fully understand its coupling mechanisms. The challenge to represent the governing processes of the upper atmosphere depends, to a large extent, on an accurate representation of the true state of the IT system, that we obtain by assimilating relevant measurements into physics-based models. Thermospheric Mass Density (TMD) is the summation of total neutral mass within the atmosphere that is derived from accelerometer measurements of satellite missions such as CHAMP, GOCE, GRACE(-FO) and Swarm. TMD estimates can be assimilated into physics-based models to modify the state of the processes within the IT system. Previous studies have shown that this modification can potentially improve the simulations and predictions of the ionospheric electron density. These differences could also be interpreted as an indicator of the ionosphere-thermosphere interaction. The research presented here, aims to quantify the impact of data satellite based TMD assimilation on numerical model results. Subject of this study is the Coupled Thermosphere-Ionosphere-Plasmasphere electrodynamics (CTIPe) physics-based model in combination with the recently developed Thermosphere-Ionosphere Data Assimilation (TIDA) scheme. TMD estimates from the ESA’s Swarm mission are assimilated in CTIPe-TIDA during the 16 to the 20 of March 2015. This period was characterized by a strong geomagnetic storm that triggered significant changes in the IT system, the so-called St. Patrick day storm 2015. To assess the changes in the IT system during storm conditions due to data assimilation, the model results from assimilating SWARM mass density normalized to the altitude of 400 km are compared to independent thermospheric estimates like GRACE-TMDS. In order to evaluate the impact of the data assimilation on the ionosphere, the corresponding output of electron density is compared to high-quality electron density estimates derived from data-driven model of the DGFI-TUM

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

Assimilating Space-Based Thermospheric Neutral Density (TND) Data Into the TIE-GCM Coupled Model During Periods With Low and High Solar Activity

The global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space-based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space-based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm-C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm-B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE-GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.</p

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

Predicting global thermospheric neutral density during periods with high geomagnetic activity

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

Assessing a calibration and data assimilation technique for predicting multi-level global thermospheric neutral density fields

An accurate estimation of the Thermospheric Neutral Density (TND) is important for predicting the orbit of satellites and objects, for example, those with the altitude of less than 1000 km. Models are often used to simulate TNDs but their accuracy is limited due to uncertainties. Satellite missions such as CHAMP, GRACE, GOCE, Swarm, and GRACE-FO or the Satellite Laser Ranging (SLR) missions can be used to estimate along-track TNDs. However, spatial and temporal coverage of these space borne TNDs is restricted to the mission design. To make the best use of the modelling tools and measurements, we applied these along-track TND measurements within the sequential Calibration and Data Assimilation (C/DA) framework proposed by (Forootan et al., 2022, doi:10.1038/s41598-022-05952-y). The C/DA is used to re-calibrate the NRLMSISE00 model, which is called “C/DA-NRLMSISE00”, whose outputs fit well to the introduced space-borne TNDs. The C/DA-NRLMSISE00 is applicable for forecasting TNDs and individual neutral mass compositions at any predefined vertical level (between ~100 and ~600 km) with user-defined spatial-temporal sampling. Seven periods (between 2003 - 2020) with considerable geomagnetic activity are selected for our investigations because most of the available models lack accuracy to provide reasonable TND simulations. Independent comparisons are performed with the space-borne TNDs that were not used within the C/DA framework, as well as with the outputs of other thermospheric models such as Jacchia-Bowman 2008 (JB2008) and the High Accuracy Satellite Drag Model (HASDM) database. The numerical results indicate that indeed the new model is suitable for producing multi-level global thermospheric neutral density fields

How can space-borne along-track neutral density measurements be used to predict multi-level global thermospheric neutral density fields?

An accurate estimation of the Thermospheric Neutral Density (TND) is important for predicting the orbit of satellites and objects, for example, those with the altitude of less than 1000 km. Models are often used to simulate TNDs but their accuracy is limited due to modelling restrictions and sensitivity to the calibration period. Satellite missions such as CHAMP, GRACE, GOCE, Swarm, and GRACE-FO are equipped with on-board accelerometer sensors to measure drag forces, which can be used to estimate along-track TNDs. However, spatial and temporal coverage of these space borne TNDs is restricted to the mission design. To make the best use of the modelling tools and measurements, we applied these along-track TND measurements within the sequential Calibration and Data Assimilation (C/DA) framework proposed by (Forootan et al., 2022, doi:10.1038/s41598-022-05952-y). The C/DA is used to re-calibrate the NRLMSISE00 model, which is called “C/DA-NRLMSISE00”, whose outputs fit well to the introduced space-borne TNDs. The C/DA-NRLMSISE00 is applicable for forecasting TNDs and individual neutral mass compositions at any predefined vertical level (between ~100 and ~600 km) with user-defined spatial-temporal sampling. Nine time periods (October 2003, July 2004, March 2008, April 2010, March 2015, September 2017, August 2018, September 2020 and October 2021) associated with space weather storms are selected for our investigations because most of the available models lack accuracy to provide reasonable TND simulations. Independent comparisons are performed with the space-borne TNDs that were not used within the C/DA framework, as well as with the outputs of other thermospheric models such as Jacchia-Bowman 2008 (JB2008) and the High Accuracy Satellite Drag Model (HASDM) database. The numerical results indicate improvements in the Root Mean Squared Errors (RMSE) of the C/DA-NRLMSISE00's TND forecasts compared to NRLMSISE-00, JB2008 and HASDM along-track of the LEO missions. The percentage reductions are found to be: 51%, 8% and 8 % along GRACE (2003, average altitude 490 km), 25%, 20% and 48% along GOCE (2010, average altitude 270 km), 46%, 37% and 35% along Swarm B (2015, average altitude 520 km), 54%, 12% and 5 % along Swarm B (2017, average altitude 514 km), and 41% and 64% along GRACE (FO) (2021, average altitude 504 km), respectively