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Meso-scale Coupling of M-I Systems
Meso-scale Coupling of M-I Systems
Coupling between magnetosphere and ionosphere-thermosphere (IT) systems occurs over a wide range of temporal and spatial scales. Recent studies showed that coupling and energy transport can be locally more efficient at mesoscales and even small scales as compared to large scales. The High-latitude Input for Meso-scale electrodynamics (HIME) framework is developed in order to incorporate observations of meso-scale electrodynamics as drivers for the global ionosphere-thermosphere models. PI: Olga Verkhoglyadova, JPL/NASA-CalTech
Machine Learning Model for Ionospheric Convection
Machine Learning Model for Ionospheric Convection
To accurately represent the high-latitude ionospheric convection in multi-scale, we develop state-of-art machine learning algorithms using data from individual SuperDARN radars. PI: Hyunju Connor, GSFC/NASA
Investigation of Interhemispheric Asymmetries
Investigation of Interhemispheric Asymmetries
This study aims to advance the understanding of origin and propagation of interhemispheric asymmetries and associated magnetosphere-ionosphere-thermosphere (M-I-T) coupling processes employing numerical models of the Geospace. PI: Hyomin Kim, NJIT
Image Credit: C. Huang et al., 2019
ULF TEC perturbations driven by magnetospheric waves
ULF TEC perturbations driven by magnetospheric waves
Perturbations in ionospheric Total Electron Content (dTEC) with timescales of ~10s-1000s can be driven from above by processes in the magnetosphere – including magnetospheric Ultra Low Frequency (ULF) waves – and below by processes such as earthquakes that couple to acoustic-gravity waves in the atmosphere. Characterizing the subset of dTEC related to ULF waves is of high priority as several studies have linked these perturbations to ionospheric space weather. PI: Michael Hartinger, SSI
Image Credit: SVS NASA
Machine-Learning Ready Auroral Image Database
Machine-Learning Ready Auroral Image Database
A better understanding of the morphology of auroral forms is critical to our understanding of magnetospheric dynamics and the coupling of the magnetosphere to the upper atmosphere. Machine learning offers the possibility of surfacing new knowledge in this area, but most existing auroral image databases are not yet machine learning-ready. This project aims to compile a large-scale, homogeneous, machine learning-ready database of auroral images. PI: Jeremiah Johnson, UNH
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