This research was completed to introduce a state-of-the-art Venus GCM to the modeling community. Validation studies were performed to give credence to the model's results. and This data set is made available under a Creative Commons Public Domain
license (CC0 1.0). The python scripts contained were ran on macOS
Monterey version 12.7 with Python 3.9.
Numpy version: 1.19.4
Pandas version: 1.2.0
Ponder, Brandon & Ridley, Aaron J. & Bougher, Stephen W. & Pawlowski, D. & Brecht, A. (2023). The Venus Global Ionosphere-Thermosphere Model (V-GITM): A Coupled Thermosphere and Ionosphere Formulation. JGR Planets. In Press.
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.
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
The research that produced this data focused on conducting a statistical comparison between horizontal winds modeled with GITM and those derived from the accelerometer aboard the GOCE satellite. The winds from GITM and GOCE were compared by constructing their respective probability densities under different levels of geomagnetic activity, and by distributing them as a function of geomagnetic activity, magnetic latitude, magnetic local time, day-of-the-year, and solar radio flux.
