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Quantifying the uncertainties of the ionospheric conditions associated with the solar wind with the WAM-IPE model

Presentation #108.07 in the session Improving Understanding of the Sun-Earth System Through Advanced Statistical and Machine Learning Techniques.

Published onOct 20, 2022
Quantifying the uncertainties of the ionospheric conditions associated with the solar wind with the WAM-IPE model

In this study, we will estimate the uncertainties of the ionosphere-thermosphere (IT) conditions simulated by the coupled Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) forecast system for varying driving conditions. The historical data of solar wind, interplanetary magnetic field (IMF), and Kp index are collected to generate synthetic data to drive the model by using the advanced variational autoencoder (VAE). High scalable multi-fidelity and multi-level uncertainty quantification (UQ) methods are then applied to the high-resolution WAM-IPE to improve the prediction accuracy of WAM-IPE. We will present the uncertainties of plasma drifts, neutral winds, and electron density simulated by the model and analyze the most important drivers that are associated with these uncertainties during quiet and storm conditions separately. In the next step, we will further explore the uncertainties of the IT system associated with the tides propagating up from the lower atmosphere. This study will be extended to analyze the most important drivers that are responsible for the generation and development of the equatorial and low latitude ionospheric electron density irregularities, which are one of the most common space weather phenomena in the ionosphere-thermosphere (IT) system and can seriously impact radio propagation in the ionosphere. Quantifying the uncertainty in the IT model is important to make probabilistic predictions of the ionospheric irregularities. This study will also reveal the advantages of understanding the connections between solar wind and ionosphere variations and making predictions of space weather phenomena in the IT system through combining physics-based IT model with advanced statistical/machine learning methods.

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