Improving Forecasting Ability of GITM Using Data‐Driven Model Refinement. Issue 3 (20th March 2023)

• Record Type: Journal Article
• Authors: Ponder, Brandon M.; Ridley, Aaron J.; Goel, Ankit; Bernstein, D. S.
• Is Part Of: Space weather. Volume 21:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-20
• Subjects: thermosphere -- ionosphere -- thermal conductivity -- model refinement -- storm -- forecasting; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2022SW003290
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier‐Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents (N2, O2, and O) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm. Plain Language Summary: Physics‐based models have a difficult time accurately estimating the upper atmosphere density. These densities are needed to compute satellite orbit trajectories to monitor for potential collisions. Inaccurate density estimation can be due to variety of factors and so methods of correcting the model‐predicted density are needed. We are presenting a method to correct the densities using available satellite measurements from the Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment satellites and the commonly used empirical model NRLMSISE‐00. Upon reducing the model error, we show the improved ability of a physics‐based model to capture a geomagnetic storm. Key Points: Inaccurate approximations to physics terms and incorrect drivers within Global Ionosphere Thermosphere Model (GITM) can be corrected for using data‐driven model refinement Dynamic adjustments to the parameterized thermal conductivity coefficients can compensate for errors in model predicted mass densities Comparative statistics were computed when GITM was configured in a biased version, an out‐of‐the‐box version and the refined version