Noise Analysis of Geosynchronous Global-Scale Observations of the Limb and Disk (GOLD) Mission Far Ultra Violet (FUV) Image Data

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Currently, tens of thousands of commercial satellites are operating and planned for launch in geostationary orbit. High-energy particles are known to be a major cause of failures or malfunctions in these satellites. Therefore, predicting high-energy electron flux in geostationary orbit has become an important part of space weather information. While the global-scale observations of the limb and disk (GOLD) mission far ultra violet (FUV) instrument is not designed for radiation belt observations to measure particle flux, it has been providing observational images of the ionosphere and thermosphere since it reached geostationary orbit in October 2018. In particular, previous studies have shown that high-energy particle flux can be inferred from noise in the images, as demonstrated through SOHO image analysis. By inputting the observed image noise into machine learning algorithms or artificial intelligence (AI) techniques, it becomes possible to estimate electron flux. This can then be applied to derive space science data from images (e.g., weather images) acquired by various existing satellites launched for commercial purposes. In particular, if we can utilize already collected solar images or images from various commercial satellites in operation, we can obtain energy measurements at various locations that existing scientific satellites (e.g., US's GOES, Korea's KSEM) cannot cover (e.g., over Africa). This is a new method of utilizing previously underutilized image data and has the potential to maximize the usability of satellite data.

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