Real-time detection of solar flares from ground-based VLF data

TL;DR

This paper introduces a ground-based VLF data method for real-time solar flare detection, characterization, and alerting, utilizing trend analysis, multiple transmitter data, and propagation models, implemented in a Python package.

Contribution

The paper presents a novel real-time solar flare detection approach using ground-based VLF data, including an incremental algorithm and a Python implementation for improved resilience and speed.

Findings

Detects 82.7% of M and X flares within one quarter of their rise time.

Combines multiple VLF transmitters to estimate the Sun's X-ray flux.

Uses propagation models to compute D-region electron density profiles.

Abstract

A method for real-time solar flare detection and characterization using ground-based Very Low Frequency (VLF, 15-45 kHz) data is presented. The D-region, the ionosphere's lowest region, is monitored by VLF waves propagating in the Earth-Ionosphere waveguide. The D-region electron density increases during sudden surges in X-ray radiation from solar flares. This subsequently enhances HF absorption. By seeking trend changes in VLF phase data, an incremental algorithm finds solar flares. 82.7% of M and X solar flares are detected within one fourth of their rise time. In addition, several VLF transmitters are monitored simultaneously. Combining information from their phase variations leads to an estimation of the Sun's X-ray flux. Last, propagation models such as LMP or LWPC are combined with the VLF measurements to compute D-region electron density profiles. This method and its implementation in a new Python package are a step towards building a more resilient system for flare detection and alerts. Its reliance on ground-based data alone ensures an easy maintenance and a backup in case a satellite failure. It also provides alerts comparable to or faster than those obtained through satellite data, due to shortened data latency.