Ionospheric Observations from the ISS: Overcoming Noise Challenges in Signal Extraction

TL;DR

This paper presents a novel statistical processing pipeline for ionospheric measurements from the ISS, effectively overcoming noise challenges to improve data coverage and monitor ionospheric variability during solar maximum.

Contribution

It introduces a noise modeling and signal extraction method using Gaussian processes that enhances ionospheric data analysis from the ISS during high solar activity.

Findings

Increased data coverage through noise-assisted processing.

Effective noise floor estimation and signal extraction.

Enhanced monitoring of ionospheric variability.

Abstract

The Electric Propulsion Electrostatic Analyzer Experiment (\`EP\`EE) is a compact ion energy bandpass filter deployed on the International Space Station (ISS) in March 2023 and providing continuous measurements through April 2024. This period coincides with the Solar Cycle 25 maximum, capturing unique observations of solar activity extremes in the mid- to low-latitude regions of the topside ionosphere. From these in situ spectra we derive plasma parameters that inform space-weather impacts on satellite navigation and radio communication. We present a statistical processing pipeline for \`EP\`EE that (i) estimates the instrument noise floor, (ii) accounts for irregular temporal sampling, and (iii) extracts ionospheric signals. Rather than discarding noisy data, the method learns a baseline noise model and fits the measurement surface using a scaled Vecchia Gaussian process approximation, recovering values typically rejected by thresholding. The resulting products increase data coverage and enable noise-assisted monitoring of ionospheric variability.