Specification of the Ionosphere-Thermosphere Environment for Orbital Propagation: A Case Study

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Monday, 3 February 2014
Hall C3 (The Georgia World Congress Center )
Humberto C. Godinez, LANL, Los Alamos, NM; and M. Shoemaker, E. Lawrence, D. Higdon, A. Walker, R. Linares, A. Ridley, and J. Koller

The Ionosphere-Thermosphere environment undergoes constant and sometimes dramatic changes due to solar and geomagnetic activity. Furthermore, given that this environment has a significant effect on space infrastructure, such as satellites, it is important to understand the potential changes caused by space weather events. This work presents a case study of four time periods using assimilation methods with the Global Ionosphere-Thermosphere Model (GITM). The main objective is to analyze the changes in the global upper atmospheric environment caused by extreme space weather events, including the Halloween storm, and analyze the effect on satellite drag and collision uncertainty. In particular, an ensemble Kalman filter (EnKF) assimilation method is applied to GITM and used to incorporate observations from the CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) missions. The work is part of the Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking (IMPACT) project in Los Alamos National Laboratory. The goal of the project is to develop and calibrate a new physics-based atmospheric dynamics model that is driven by space weather and suitable for orbital drag calculations.

The GITM model is a physics-based model which solves the full Navier-Stokes equations for density, velocity, and temperature for a number of neutral and charged components. Additionally, the model explicitly solves for a number of neutral and ion species, and includes a number of parameters to represent the influence of solar and geomagnetic activity. This enables GITM to represent a variety of physical phenomena that are of interest for the community. To enhance the results of GITM, the EnKF data assimilation combines model results, observational data, and their respective uncertainties to obtain a more accurate representation of the ionosphere-thermosphere environment. In our work, derived density fields from CHAMP and GRACE are assimilated into GITM over the four time periods of interest. The assimilation is used to estimate both the model state and relevant parameters. The corrected density field is then used to estimate an accurate orbit for a number of satellites and a collision probability is then computed.

The experiments show that key solar parameters, which act as proxy for solar activity, exert a significant influence in the evolution of the total atmospheric density. Furthermore, the results also show the strong correlation that exists between upper atmospheric density and solar activity. That is, the correlation is strong during solar active times, and weak during solar quiet times. Indicating that at active times the sun dominates the changes in the ionosphere-thermosphere, while at quiet times internal processes dominate the evolution of the ionosphere-thermosphere.