MTG Lightning Imager usability from a high-latitude perspective

The main thunderstorm season in the Nordic region is relatively short, and the annual average lightning flash density is modest compared to southern Europe. However, behind these moderate statistics, individual thunderstorms can reach severe intensity. In fact, the region experiences severe thunderstorms every summer, producing not only intense lightning but also downbursts, tornadoes, and large hail.

Thunderstorms in the Nordic region are monitored using the ground-based Nordic Lightning Information System (NORDLIS), which covers Finland, Sweden, Norway, Estonia, Latvia, and Lithuania. NORDLIS has provided real-time lightning observations for over 20 years. Previous studies have shown that NORDLIS performs very well in detecting cloud-to-ground lightning within the network’s central areas, though its performance declines in the outermost regions.

The MTG Lightning Imager (LI) is the first instrument of its kind in Europe, offering real-time lightning observations across the continent since 2024. Due to its geostationary orbit, the MTG LI performs optimally in southern and central Europe, as well as in Africa—regions where most thunderstorms occur within its field of view. The Nordic region lies at the edge of the LI’s coverage, with a highly tilted viewing angle, suggesting reduced detection performance. This raises an important question for national meteorological services in the Nordics: what is the value of MTG LI data in this region?

In this study, we demonstrate that the LI performs surprisingly well even at very high latitudes (>70°N), suggesting that the data may be highly usable in these areas. We present experiences from two thunderstorm seasons (2024 and 2025) at the Finnish Meteorological Institute. Our analysis includes latitudinal performance, diurnal variation, location accuracy based on ground-truth use cases, and recommendations for using LI data in high-latitude meteorological services. We also showcase various methods for visualizing LI data alongside other meteorological datasets and products.