In 2024 May, the scientific community observed intense solar eruptions that resulted in a great geomagnetic storm and auroral extensions, highlighting the need to document and quantify these events. This study mainly focuses on their quantification. The source active region (AR; NOAA Active Region 13664) evolved from 113 to 2761 millionths of the solar hemisphere between May 4 and 14. NOAA AR 13664’s magnetic free energy surpassed 10³³ erg on May 7, triggering 12 X-class flares on May 8–15. Multiple interplanetary coronal mass ejections (ICMEs) were produced from this AR, accelerating solar energetic particles toward Earth. According to satellite and interplanetary scintillation data, at least four ICMEs erupted from AR 13664, eventually overcoming and combining each other. The shock arrival at 17:05 UT on May 10 significantly compressed the magnetosphere down to ≈5.04 R _E and triggered a deep Forbush Decrease. GOES satellite data and ground-based neutron monitors confirmed a ground-level enhancement from 2 UT to 10 UT on 2024 May 11. The ICMEs induced exceptional geomagnetic storms, peaking at a provisional Dst index of −412 nT at 2 UT on May 11, marking the sixth-largest storm since 1957. The AE and AL indices showed great auroral extensions that located the AE/AL stations into the polar cap. We gathered auroral records at that time and reconstructed the equatorward boundary of the visual auroral oval to 29.°8 invariant latitude. We compared naked-eye and camera auroral visibility, providing critical caveats on their difference. We also confirmed global disturbances of the storm-enhanced density of the ionosphere.

Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

In this paper we investigate quantitatively the effect of data gaps for four methods of estimating the amplitude spectrum of a time series: fast Fourier transform (FFT), discrete Fourier transform (DFT), Z transform (ZTR) and the Lomb–Scargle algorithm (LST). We devise two tests: the single-large-gap test, which can probe the effect of a single data gap of varying size and the multiple-small-gaps test, used to study the effect of numerous small gaps of variable size distributed within the time series. The tests are applied on two data sets: a synthetic data set composed of a superposition of four sinusoidal modes, and one component of the magnetic field measured by the Venus Express (VEX) spacecraft in orbit around the planet Venus. For single data gaps, FFT and DFT give an amplitude monotonically decreasing with gap size. However, the shape of their amplitude spectrum remains unmodified even for a large data gap. On the other hand, ZTR and LST preserve the absolute level of amplitude but lead to greatly increased spectral noise for increasing gap size. For multiple small data gaps, DFT, ZTR and LST can, unlike FFT, find the correct amplitude of sinusoidal modes even for large data gap percentage. However, for in-situ data collected in a turbulent plasma environment, these three methods overestimate the high frequency part of the amplitude spectrum above a threshold depending on the maximum gap size, while FFT slightly underestimates it.

The worldwide network of neutron monitors (NMs) is the primary instrument to study cosmic‐ray variability on time scales of up to 70 yr. Since the 1950s, 147 NMs with publicly available data have been in operation, and their records are archived in and distributed through different repositories and data sources. A comprehensive analysis of all available NM data sets (300 data sets from 147 NMs) is performed here to check the quality and consistency of the data. The data sources include World Data Center for Cosmic Rays, the Neutron Monitor Database, the Pushkov Institute of Terrestrial Magnetism, Ionosphere, and Radiowave Propagation (IZMIRAN) and individual station/institution databases. It was found that The data from the same NM can be nonidentical and of different quality in different sources. We give and tabulate here a recommendation for the optimal data source of each NM. We also present here a list of 29 “prime” stations with the longest and most reliable data. Verified data sets for these prime stations are provided as supplementary information.