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Improving Forecasting Ability of GITM using Data-driven Model Refinement

Creator:

Ponder, Brandon M., Ridley, Aaron J., Goel, Ankit, and Bernstein, Dennis S.

Description:

This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.

Keyword:

Thermosphere, GITM, CHAMP, GRACE, MSIS, Upper Atmosphere Modeling, and Data Assimilation

Citation to related publication:

Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290

Discipline:

Engineering and Science