Date of Award

8-17-2024

Document Type

Masters Project

Abstract

The cross polar cap potential (CPCP) serves as an indicator of the energy flow into the magnetosphereionosphere system and can saturate during geomagnetic storms as the solar wind electric field increases. In this project, we investigated the uncertainties in the cross polar cap potential saturation problem by examining the differences in its estimation from different sources. We first focused on the relationship between the CPCP from different sources and their relationships with SuperMAG Auroral Electrojet (SME) and Auroral Electrojet (AE) indices to try and find different distributions. We then tried to find the relationship between CPCP values with solar wind parameters using linear regression, random forest, and multi-layer perceptron models. The parameters we use are Interplanetary Magnetic Field (IMF) components, plasma, geomagnetic index data from the OMNI database, and the SuperDARN Mapex CPCP dataset with data from 2000 to 2020. Our work showed that the CPCP has the highest Pearson correlation coefficient with the velocity, magnetic field magnitude and its vertical component, and the month compared to other drivers. Among all the models we developed, the Random Forest model performed significantly better compared to traditional regression algorithms, like Ridge, Lasso, and Elastic Net. On the basis of all model performances Neural network performed better than the Random Forest regression model with a Pearson correlation coefficient of 0.92. Similarly, the model performances displayed the same behavior for CPCP estimations made from the Polar Cap Index, however, the Pearson correlation coefficients between the predictions and actual values were higher at 0.96 for both hemispheres. Combined with the CPCP behavior for different geomagnetic activity levels, the prediction models can help shed light on the CPCP saturation problem, especially during the presence of large data gaps of external solar wind driving.

Handle

http://hdl.handle.net/11122/16334

Download

Share

COinS