Machine Learning Approach for Forecasting Space Weather Effects in the Ionosphere with Uncertainty Quantification

Abstract

<p>Space weather can cause strong sudden disturbances in the Earth’s ionosphere that can degrade the performance and reliability of Global Navigation Satellite System (GNSS) operations. To minimize such degradations, ionospheric effects need to be precisely and timely corrected by providing information of the spatially and temporally variable Total Electron Content (TEC). To obtain such corrections and early warning information of space weather events, we need to model the nonlinear space weather processes focusing on their impact on the ionosphere. Machine Learning (ML) models can learn nonlinear relationships from data to solve complex phenomena such as space weather. To interpret ML model results, it is crucial to know their quality and reliability. Quantifying the uncertainty of the ML results is an important step toward developing a “trustworthy” model, providing reliable results, and improving the model explainability.</p><p>This study presents a novel ML model to forecast the vertical TEC (VTEC) utilizing state-of-the-art supervised learning techniques and robustly assessing the uncertainty of the achieved results. The data are systematically analyzed, selected and pre-processed for optimal model learning, especially during space weather events. Results from our previous study (Natras and Schmidt, 2021) were improved in terms of data, ensemble modelling, and uncertainty quantification. The input data are expanded with additional parameters of the solar wind and the interplanetary magnetic field from OmniWeb and spectral irradiance measurements from the solar instrument LYRA onboard the spacecraft PROBA2 (Dominique et al., 2013). Also, new input features have been derived, such as daily differences, time derivatives, moving averages, etc. We applied ensemble modeling to combine diverse ML models based on different learning algorithms with different training data sets. The ensemble model enhances the performance of base learners and quantifies the uncertainty of results. This approach shows potential for forecasting VTEC in different ionospheric regions during quiet and storm periods, while providing the uncertainties of the forecasting results.</p><p><strong>Keywords: </strong>Machine Learning, Space Weather, Ionosphere, Vertical Total Electron Content (VTEC), Forecasting, Uncertainty Quantification</p><p> </p><p><strong>