Nicholas Dietrich, University of Colorado Boulder; Tomoko Matsuo, University of Colorado Boulder; Chih-Ting Hsu, High Altitude Observatory, NCAR
Keywords: Data assimilation, neutral density, solar and magnetospheric drivers, uncertainty quantification
Abstract:
Increasing our knowledge of thermospheric neutral densities variability is crucial for reducing satellite position forecasting errors in low Earth orbit (LEO). With satellite drag being the largest source of uncertainty for LEO satellites, reducing neutral density uncertainties will be necessary to keep the number of satellite conjunctions within a manageable level. When neutral density becomes highly variable, especially during geomagnetic storms or solar flares, satellite position uncertainty ellipsoids expand and may cause an untenable number of conjunction alerts. While a global monitoring system is not available for neutral densities, information of thermospheric states can be inferred from more abundant ionospheric observations. In particular, the Global Navigation Satellite Systems (GNSS) systems USAs GPS, Russias GLONASS, Chinas BeiDou and EU Galileo produce radio signals with the capability to monitor the ionosphere and thermosphere globally and continuously.
Using the Ensemble Adjustment Kalman Filter (EAKF), this study focuses on exploiting the strong coupling between the thermosphere and ionosphere captured in the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). Assimilating radio occultation (RO) data from the COSMIC missions is used to specify and forecast neutral densities. The EAKF uses linear regression to map increments in observation space to increments in state space, allowing electron density observations to be used to update states of the neutral atmosphere such as temperature and composition (O2, N2, O, HE). A lack of observing systems for Helium means Helium’s true compositions are largely unknown and estimation of helium from RO data has not been previously attempted.
The need of helium estimation for orbit determination is investigated and looked to be corrected with the EAKF. An observing system simulation experiment (OSSE) is used to assess COSMIC RO data assimilation impact on temperature and composition estimation. Improvements to temperature and composition estimation is validated through LEO satellite orbit errors from propagating satellite orbits through the estimated neutral density field.
Prior work assimilating COSMIC RO observations into TIEGCM has shown it is critical to correct model bias stemming from errors in specifying solar and magnetospheric drivers as this bias had shown measurable impacts on precise orbit determination. In TIEGCM solar and magnetospheric drivers are parameterized by the F10.7 and Kp index, respectively. The bias can be substantially reduced through initializing ensemble members with selecting drivers distributions for F10.7 and Kp index that minimized the squared differences between modeled and CHAMP neutral densities and between modeled and COSMIC electron densities. This study formalizes this ensemble minimalization scheme to reduce the model biases of TIEGCM prior to data assimilation of RO observations. The ensemble modeling approach is used to estimate drivers by probabilistically quantifying the relationship between solar and magnetospheric drivers with thermospheric state variables. The result of running TIEGCM with this distribution of solar and magnetospheric drivers is an uncertainty quantification of thermospheric states, notably neutral density, generated by ensemble members. Real CHAMP neutral densities are used to estimate the probabilistic relationship of both drivers and verified through orbit propagation errors and density observation errors. Additionally, a purposely biased prior driver distribution is fed in to test how well the system corrects TIEGCM to match true neutral densities.
Date of Conference: September 14-17, 2021
Track: Atmospherics/Space Weather