An official website of the United States government
Low Earth orbit (LEO) radio occultation|radio occultations (RO) constellations can provide global electron density profiles (EDPs) to better specify and forecast the ionosphere‐thermosphere (I‐T) system. To inform future RO constellation design, this study uses comprehensive Observing System Simulation Experiments (OSSEs) to assess the ionospheric specification impact of assimilating synthetic EDPs into a coupled I‐T model. These OSSEs use 10 different sets of RO constellation configurations containing 6 or 12 LEO satellites with base orbit parameter combinations of 520 or 800 km altitude, and 24° or 72° inclination. The OSSEs are performed using the Ensemble Adjustment Kalman Filter implemented in the data assimilation (DA) Research Testbed and the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM). A different I‐T model is used for the nature run, the Whole Atmosphere Model‐Ionosphere Plasmasphere Electrodynamics (WAM‐IPE), to simulate the period of interest is the St. Patrick's Day storm on March 13–18, 2015. Errors from models and EDP retrieval are realistically accounted for in this study through distinct I‐T models and by retrieving synthetic EDPs through an extension Abel inversion algorithm. OSSE assessment, using multiple metrics, finds that greater EDP spatial coverage leading to improved specification at altitudes 300 km and above, with the 520 km altitude constellations performing best due to yielding the highest observation counts. A potential performance limit is suggested with two 6‐satellite constellations. Lastly, close examination of Abel inversion error impacts highlights major EDP limitations at altitudes below 200 km and dayside equatorial regions with large horizontal gradients and low electron density magnitudes.
more »
« less
Specifying Satellite Drag Through Coupled Thermosphere‐Ionosphere Data Assimilation of Radio Occultation Electron Density Profiles
Abstract The largest obstacle to managing satellites in low Earth orbit (LEO) is accurately forecasting the neutral mass densities that appreciably impact atmospheric drag. Empirical thermospheric models are often used to estimate neutral densities but they struggle to forecast neutral densities during geomagnetic storms when they are highly variable. Physics‐based models are thus increasingly turned to for their ability to describe the dynamical evolution of neutral densities. However, these models require observations to constrain dynamical state variables to be able to forecast mass densities with adequate fidelity. The LEO environment has scarce neutral state observations. Here, we demonstrate, in simulated experiments, a reduction in orbit errors and neutral densities using a physics‐based, data assimilation approach with ionospheric observations. Using a coupled thermosphere‐ionosphere model, the Thermosphere Ionosphere Electrodynamics General Circulation Model, we assimilate Constellation Observing System for Meterology, Ionosphere, and Climate electron density profiles (EDPs) derived from radio occultation (RO) observations. We use the EDPs to directly update neutral states, improving errors for neutral temperature by 70% and neutral winds by 20%. Updated neutral temperature and neutral winds additionally improve helium composition errors by 60% and 40%, respectively. Improved neutral density estimates correspond to a reduction in orbit errors of 1.2 km over 2 days, a 70% reduction over a no‐assimilation control, and a 29 km improvement over 9 days. This study builds on the results of our earlier work to further develop and demonstrate the potential of using a vast and growing RO data source, with a physics‐based model, to overcome our limited number of neutral observations.
more »
« less
- Award ID(s):
- 1848544
- PAR ID:
- 10370357
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Space Weather
- Volume:
- 20
- Issue:
- 8
- ISSN:
- 1542-7390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
To improve Thermosphere–Ionosphere modeling during disturbed conditions, data assimilation schemes that can account for the large and fast-moving gradients moving through the modeled domain are necessary. We argue that this requires a physics based background model with a non-stationary covariance. An added benefit of using physics-based models would be improved forecasting capability over largely persistence-based forecasts of empirical models. As a reference implementation, we have developed an ensemble Kalman Filter (enKF) software called Thermosphere Ionosphere Data Assimilation (TIDA) using the physics-based Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model as the background. In this paper, we present detailed results from experiments during the 2003 Halloween Storm, 27–31 October 2003, under very disturbed ( K p = 9) conditions while assimilating GRACE-A and B, and CHAMP neutral density measurements. TIDA simulates this disturbed period without using the L1 solar wind measurements, which were contaminated by solar energetic protons, by estimating the model drivers from the density measurements. We also briefly present statistical results for two additional storms: September 27 – October 2, 2002, and July 26 – 30, 2004, to show that the improvement in assimilated neutral density specification is not an artifact of the corrupted forcing observations during the 2003 Halloween Storm. By showing statistical results from assimilating one satellite at a time, we show that TIDA produces a coherent global specification for neutral density throughout the storm – a critical capability in calculating satellite drag and debris collision avoidance for space traffic management.more » « less
-
Abstract The Iterative Driver Estimation and Assimilation (IDEA) data assimilation technique was used with the Whole Atmosphere Model (WAM) to improve neutral density specification in the upper thermosphere. Two different neutral density data sources were examined to enhance the capability of simulating the global thermospheric state. The first were accelerometer estimates of neutral density from the Challenging Mini‐Satellite Payload (CHAMP) satellite. The second were neutral density estimates from the Global Ultraviolet Imager (GUVI) limb‐scan airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. Due to the intensity of the November 2003 storm, two changes were necessary in WAM. The first was allowing the Kp geomagnetic index to exceed 9 and the second was changing the relationship between Kp and the solar wind parameters used to drive the model. With these changes, results show that IDEA effectively captures the thermospheric neutral density at the CHAMP satellite altitude and follows the time‐dependence through the November 2003 storm period. Furthermore, a cross‐comparison was conducted with the GUVI dayside limb scan measurements. GUVI neutral densities within 270–320 km show the closest agreement with WAM when CHAMP data was assimilated by IDEA. We speculate on the potential for observations from GUVI at 300 km to be used as a data source in the IDEA‐WAM simulations. These simulations demonstrate the utility of the IDEA data assimilation technique with physical models and that using either accelerometer observations or ultraviolet airglow limb measurement during extreme storm periods could be used.more » « less
-
Abstract We have discovered that the peak phase time of predawn thermosphere‐ionosphere Na (TINa) layers (∼110–150 km altitude) undergoes clear annual variations with the earliest occurrence in summer and latest in winter over Boulder (40.13°N, 105.24°W), which are closely correlated to annual phase variations of sunrise and tidal winds. Such discoveries were enabled by the first characterization of 12 monthly composites of TINa layers from January through December using 7 years of lidar observations (2011–2017). Despite their tenuous densities, the predawn TINa layers have nearly 100% occurrence rate (160 out of 164 nights of observations). Monthly composites show downward‐phase‐progression TINa descending at similar phase speeds as Climatological Tidal Model of the Thermosphere tidal winds. These TINa layers occur in ion convergence but neutral divergence regions, modeled using tidal winds. These results support the formation mechanism (neutralization of converged TINa+forming TINa) proposed previously and suggest that migrating tidal winds experience annual phase variations.more » « less
-
Abstract Physics‐based models of the ionosphere‐thermosphere system have been touted as the next big thing in the context of drag modeling and space operations for decades. However, the computational complexity of such models have primarily kept them being used operationally. We recently demonstrated a proof‐of‐concept for developing what we call a reduced order probabilistic emulator (ROPE) for the thermosphere using the thermosphere ionosphere electrodynamics ‐ general circulation model (TIE‐GCM). The methodology uses a page out of dynamical systems theory to first reduce the order of the state using dimensionality reduction and then modeling the temporal dynamics in the reduced state space. The methodology uses an ensemble of temporal dynamic models to provide uncertainty estimates in the prediction. This work focuses on the dimensionality reduction step of the ROPE development process and addresses three limitations of the proof‐of‐concept: (a) extending the altitude upper boundary from 450 km to nearly 1000 km, (b) employing deep learning for nonlinear dimensionality reduction over principal component analysis (PCA) for improved performance during storm periods, and (c) maintaining the spatial resolution of the physical TIE‐GCM model, without down‐sampling, to preserve the spatial scales and variations. Results show overall performance boost over PCA for the high‐resolution and extrapolated data set as well as reduced reconstruction errors during storm‐time conditions. This work represents a major step toward operationalization.more » « less
