This paper presents a review on the PECASUS service, which provides advisories on enhanced space weather activity for civil aviation. The advisories are tailored according to the Standards and Recommended Practices of the International Civil Aviation Organization (ICAO). Advisories are disseminated in three impact areas: radiation levels at flight altitudes, GNSS-based navigation and positioning, and HF communication. The review, which is based on the experiences of the authors from two years of running pilot ICAO services, describes empirical models behind PECASUS products and lists ground- and space-based sensors, providing inputs for the models and 24/7 manual monitoring activities. As a concrete example of PECASUS performance, its products for a post-storm ionospheric F2-layer depression event are analyzed in more detail. As PECASUS models are particularly tailored to describe F2-layer thinning, they reproduce observations more accurately than the International Reference Ionosphere model (IRI(STORM)), but, on the other hand, it is recognized that the service performance is much affected by the coverage of its input data. Therefore, more efforts will be directed toward systematic measuring of the availability, timeliness and quality of the data provision in the next steps of the service development.

Ionospheric disturbances, often associated with geomagnetic storms, may cause threats to radio systems used for communication and navigation. One example is the super storm on 20 November 2003, when plenty of strong and unusual perturbations were reported. This paper reveals additional information on the dynamics in the high‐latitude ionosphere over Europe during this storm. Here analyses of wavelike traveling ionospheric disturbances (TIDs) over Europe are presented, based on estimates of the total electron content (TEC) derived from ground‐based Global Navigation Satellite System (GNSS) measurements. These TIDs are ionospheric signatures of thermospheric surges initiated by space weather events. The source region of these TIDs is characterized by enhanced spatial gradients, TEC depression, strong uplift of the F₂ layer, the vicinity of the eastward auroral electrojet, and strong aurora E layers. Joule heating is identified as the most probable driver for the TIDs observed over Europe during 20 November 2003. The sudden heating of the thermosphere leads to strong changes in the pressure and thermospheric wind circulation system, which in turn generates thermospheric wind surges observed as TID signatures in the TEC. Either the dissipation of the eastward auroral electrojet or particle precipitation are considered as the source mechanism for the Joule heating. In the course of the storm, the TEC observations show a southward shift of the source region of the TIDs. These meridional dislocation effects are obviously related to a strong compression of the plasmasphere. The presented results demonstrate the complex interaction processes in the thermosphere‐ionosphere‐magnetosphere system during this extreme storm.

We have performed a large statistical study of the peak emission altitude of green O(¹D₂–¹S₀) (557.7 nm) and blue ${\mathrm{N}}_{\mathrm{2}}^{+}$ 1 N (427.8 nm) aurora using observations from a network of all-sky cameras stationed across northern Finland and Sweden recorded during seven winter seasons from 2000 to 2007. Both emissions were found to typically peak at about 114 km. The distribution of blue peak altitudes is more skewed than that for the green, and the mean peak emission altitudes were 114.84 ± 0.06 and 116.55 ± 0.07 km for green and blue emissions, respectively. We compare simultaneous measurements of the two emissions in combination with auroral modelling to investigate the emission production mechanisms. During low-energy electron precipitation ($<\sim$ 4 keV), when the two emissions peak above about 110 km, it is more likely for the green emission to peak below the blue emission than vice versa, with the difference between the two heights increasing with their average. Modelling has shown that under these conditions the dominant source of O(¹S), the upper state of the green line, is energy transfer from excited N₂ ($A^{\mathrm{3}}{\mathrm{\Sigma }}_{\mathrm{u}}^{+}$ ), with a rate that depends on the product of the N₂ and O number densities. Since both number densities decrease with higher altitude, the production of O(¹S) by energy transfer from N₂ peaks at lower altitude than the N₂ ionisation rate, which depends on the N₂ number density only. Consequently, the green aurora peaks below the blue aurora. When the two emissions peak below about 110 km, they typically peak at very similar altitude. The dominant source of O(¹S) at low altitudes must not be energy transfer from N₂, since the rate of that process peaks above the N₂ ionisation rate and blue emission due to quenching of the long-lived excited N₂ at low altitudes. Dissociative recombination of ${\mathrm{O}}_{\mathrm{2}}^{+}$ seems most likely to be a major source at these low altitudes, but our model is unable to reproduce observations fully, suggesting there may be additional sources of O(¹S) unaccounted for.

Auroral arcs are often associated with magnetically quiet time and substorm growth phases. We have studied the evolution of auroral structures during global and local magnetic activity to investigate the occurrence rate of auroral arcs during different levels of magnetic activity. The ground‐magnetic and auroral conditions are described by the magnetometer and auroral camera data from five Magnetometers — Ionospheric radars — All‐sky cameras Large Experiment stations in Finnish and Swedish Lapland. We identified substorm growth, expansion, and recovery phases from the local electrojet index (IL) in 1996–2007 and analyzed the auroral structures during the different phases. Auroral structures were also analyzed during different global magnetic activity levels, as described by the planetary Kp index. The distribution of auroral structures for all substorm phases and Kp levels is of similar shape. About one third of all detected structures are auroral arcs. This suggests that auroral arcs occur in all conditions as the main element of the aurora. The most arc‐dominated substorm phases occur in the premidnight sector, while the least arc‐dominated substorm phases take place in the dawn sector. Arc event lifetimes and expectation times calculated for different substorm phases show that the longest arc‐dominated periods are found during growth phases, while the longest arc waiting times occur during expansion phases. Most of the arc events end when arcs evolve to more complex structures. This is true for all substorm phases. Based on the number of images of auroral arcs and the durations of substorm phases, we conclude that a randomly selected auroral arc most likely belongs to a substorm expansion phase. A small time delay, of the order of a minute, is observed between the magnetic signature of the substorm onset (i.e., the beginning of the negative bay) and the auroral breakup (i.e., the growth phase arc changing into a dynamic display). The magnetic onset was observed to precede the structural change in the auroral display. A longer delay of a few minutes was found between the beginning of the growth phase and the first detected auroral structure.