The research, carried out by Dieter Roel Poelman of the Royal Meteorological Institute of Belgium together with Hannes Kohlmann and Wolfgang Schulz of the Austrian Lightning Detection and Information System, exploits a simple physical fact: tall structures attract lightning. Towers, masts and chimneys — especially those exceeding 150 metres in height or standing on exposed mountain ridges — initiate or capture strikes far more often than the landscape around them. If you know exactly where such a structure stands, then lightning events recorded by the network in its immediate vicinity are very likely strikes on the structure itself. The difference between the network’s reported position and the structure’s true coordinates is, by definition, the location error. Multiply that logic across hundreds of structures and fourteen years of data, from 2012 to 2025, and you get something no instrumented tower could ever provide: a spatially distributed, statistically robust portrait of network performance across the whole continent.

Building the reference dataset was a project in itself. The team started from several dozen well-known structures along the ridges of the Alps, Apennines and Pyrenees, then systematically queried Wikipedia for towers, radio masts and similar installations of at least 150 metres — the same threshold used in earlier American work, below which a structure’s lightning-density signature becomes too faint to distinguish from background noise. Wikipedia was chosen because Europe lacks a centralized public equivalent of the U.S. Federal Aviation Administration’s obstacle database. After filtering to keep only structures separated by at least 2 kilometres from any other candidate — ensuring each lightning cluster could be attributed to a single object — and applying a density test comparing strike density within 500 metres of a structure to that in a surrounding ring out to 2 kilometres, 257 structures survived. Those with a density ratio below 1.5 were discarded, since a structure that does not measurably enhance local lightning activity is useless as a reference point.

The heart of the method is a clustering algorithm called DBSCAN, a standard density-based technique that groups spatially nearby events while ignoring isolated noise. Applied to the lightning events around each structure, it isolates the tight cluster of discharges most likely to represent strikes on the tower itself. The researchers fixed the clustering radius at 100 metres and required at least ten events per cluster, choices validated through sensitivity analysis: doubling the radius to 200 metres doubled the overall median error estimate, while the minimum-sample requirement proved far less influential. Once a cluster was identified, the location accuracy for each event was simply the distance between the network’s reported position and the structure’s known location, and the tower-level figure was the median of those distances. The authors are careful to note that the clustering radius is a statistical threshold, not a physical attachment distance.

To prove the approach works, the team benchmarked it against the two best-instrumented towers in Europe: the Gaisberg Tower near Salzburg, Austria, and the Säntis Tower in Switzerland. Both are fitted with sensors that record every strike with GPS-synchronized precision, giving researchers unambiguous ground truth. At Gaisberg, direct current measurements show the network’s median error falling from roughly 400 to 500 metres in the early 2000s to about 70 metres by 2025. The clustering method, fed only with network data, reproduced that trajectory almost exactly. At Säntis, the derived annual medians closely matched published values from direct measurements over 2010 to 2013. The agreement across two independent sites, over both short and long validation windows, is the strongest evidence that the technique measures something real rather than an artifact of the algorithm.

The headline numbers are impressive. Across all 257 structures, the mean location accuracy was 137 metres and the median 124 metres, with 95 percent of towers showing a median error below 258 metres. A bootstrap analysis with 10,000 resampling iterations put a 95 percent confidence interval of 115.5 to 135.2 metres around the median, and random subsampling of half or more of the towers barely moved the estimate — confirming the result is not driven by a few outliers. Just as striking is where the errors concentrate: the spatial pattern of tower-based accuracy closely mirrors the median semi-major axis of the 50 percent error ellipse that EUCLID itself assigns to every detected event. That ellipse, a mathematical construct describing the region where the true strike is expected to lie half the time, turns out to be a reliable real-time proxy for actual geolocation quality — a useful property for network operators, since it requires no ground truth at all.

The study also documents something quietly remarkable: the network is getting better, measurably, year after year. Splitting the dataset into the first three years (2012 to 2014) and the last three (2023 to 2025), the median location accuracy improved from 126 metres to 100 metres, and the mean from 135 to 119 metres. The authors attribute this directly to the continuous evolution of the network — older sensors replaced by newer models, sensor positions optimized through additions and relocations, and periodic upgrades to the central processing software. In other words, the unglamorous work of hardware refresh cycles and configuration tuning shows up directly in the numbers. A small set of four towers appeared to worsen in the recent period, but the analysis showed this was a sampling fluctuation: those structures had roughly half as many events in the recent window, and their full-period figures remained consistent with earlier years.

The method has honest limitations, and the authors catalogue them carefully. Nearby objects that were not in the reference list — most commonly wind turbines — can attract their own strikes, and if they fall within the clustering radius their discharges get lumped into the same cluster, dragging the estimated error slightly upward. Because of this, the reported values should be read as conservative, upper-bound estimates: the true network performance is probably at least as good as, and possibly better than, the numbers suggest. The Wikipedia-derived dataset also carries documentation bias, overrepresenting countries with richer infrastructure records, and the 2-kilometre separation rule excludes industrial clusters of chimneys and wind farms. The authors also compared their approach against the raster-based algorithm of Zhu and colleagues, developed for the U.S. National Lightning Detection Network, and found the two methods fundamentally consistent, with a median difference of just 2 metres across the structures both could process.

Why does any of this matter beyond the lightning community? Location accuracy underwrites everything a detection network is used for. Power utilities dispatch repair crews based on strike locations; aviation reroutes around storms using lightning data; insurers assess claims; wildfire agencies in Mediterranean Europe watch for dry-lightning ignitions. A 100-metre error versus a 500-metre error is the difference between knowing which feeder line was hit and merely which district. The study’s broader contribution is methodological: it demonstrates a scalable, low-cost way to audit a detection network continuously and everywhere at once, using nothing more than a list of tall structures and the network’s own data. The authors suggest a natural next step — examining individual events within clusters to test whether each event’s own error ellipse is representative, a question that matters for anyone relying on those ellipses in real time.

There is also a satisfying symmetry in the result. The very towers that have long served as lonely ground-truth outposts — Gaisberg, Säntis, Peissenberg — turn out to be instances of a continent-wide natural laboratory. By treating hundreds of uninstrumented structures as proxy reference points, the study transforms scattered infrastructure into a distributed validation instrument, and in doing so confirms that a decade and a half of sensor upgrades has genuinely paid off. Europe’s lightning map, the data show, is now accurate to within about a city block for half of all strikes, and steadily closing in on the rest. For a phenomenon that unfolds in microseconds and covers a continent, that is a remarkable feat of measurement — and, thanks to 257 towers standing quietly in the path of the storm, one that can now be checked anywhere, at any time.

Subject of Research: Validation of the EUCLID lightning detection network's location accuracy using tall structures across Europe

Article Title: Evaluating EUCLID location accuracy using lightning events near tall structures

Article References: Poelman, D. R., Kohlmann, H., & Schulz, W. (2026). Evaluating EUCLID location accuracy using lightning events near tall structures. Atmospheric Measurement Techniques, 19(20), 6571-6581. https://doi.org/10.5194/amt-19-6571-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-6571-2026

Keywords: lightning detection, EUCLID, location accuracy, tall structures, DBSCAN clustering, atmospheric measurement, Gaisberg Tower, Säntis Tower, sensor networks, ground truth, error ellipses, network upgrades