Comparison of a SAFIR lightning detection network in northern Germany to the operational BLIDS network

Abstract

[1] This paper presents a comparison of a regional lightning detection network based on SAFIR sensors (Surveillance et Alerte Foudre par Interférometrie Radioélectrique) with the operational German lightning detection system (BLIDS) that uses LPATS (Lightning Positioning and Tracking System) and IMPACT (Improved Performance from Combined Technology) sensors. The Institute for Meteorology and Climatology (IMUK) of the University of Hannover runs a regional lightning detection network in northern Germany (UHSN). It consists of three SAFIR receiving stations in a triangle of about 180 km side-length. As an a priori assumption that either BLIDS or UHSN represents the truth is not possible, the relative performance of each system in comparison with the other one is investigated. Probability of detection (POD) is used to investigate flash events simultaneously sensed by both networks. POD of UHSN was calculated assuming BLIDS measurements as true and, vice versa, POD of BLIDS was calculated assuming UHSN measurements as true. This comparison yielded that UHSN, as expected, is far more sensitive to intracloud lightning. In contrast, the sensitivity of UHSN for cloud-to-ground lightning was much lower than that of the BLIDS network. Hence the sensitivity of UHSN for both lightning types together (total lightning) is dominated by intracloud lightning and is consequently higher than that for BLIDS. The sensitivity of UHSN was found to exhibit strong horizontal variations. These are dominated by the network geometry and data processing procedures. The present study shows strengths and weaknesses of both systems and contributes to a better assessment of the potentials of each system.

Statistical characteristics of convective wind gusts in Germany

“…Data from the lightning information service "BLitz InformationDienst Siemens" (BLIDS), which is part of the EUropean Cooperation for LIghtning Detection network (EUCLID; Schulz et al, 2016;Drüe et al, 2007), were used to ensure that wind measurements were related to thunderstorm events. We considered only the time and location of the lightning, whereas polarity and power information were neglected.…”

“…We considered only the time and location of the lightning, whereas polarity and power information were neglected. Because cloud-to-cloud (CC) lightning was not recorded entirely due to the lower frequency range (Drüe et al, 2007), only cloud-to-ground (CG) lightning was taken into account for identifying thunderstorm days. Although three-quarters of all lightning is CC (Rakov and Uman, 2003), we assume that severe convective storms with strong downdrafts and thus strong gusts at the surface produce a sufficient number of CG flashes.…”

A European lightning density analysis using 5 years of ATDnet data

“…This range should however be less than the separation between storm clouds, meaning that it would be rare that coincident flashes from separate storms would be correlated together by chance. Figure 2 of Drüe et al (2007) indicates that, although the majority of fixes within a flash are likely to be within 10 km of each other, sources in excess of 20 km are still possible from the same flash. Given the 5 km average location uncertainty of ATDnet at the limits of Europe, these time and space correlation criteria seem justified.…”

“…The first step was to convert ATDnet "fixes" into "flashes". ATDnet strokes were converted into flashes using the approach derived from that presented by Drüe et al (2007). Individual fixes were compared against each other using spatial and temporal criteria.…”

The European lightning location system EUCLID – Part 1: Performance analysis and validation

“…For all applications of lightning data, it is important to know the performance of the employed lightning location system (LLS) related to cloud-to-ground (CG) flashes/strokes in terms of location accuracy (LA) and detection efficiency (DE). Often it is tried to determine the performance of an LLS by network cross comparison with data from different LLS covering the same area (Drüe et al, 2007;Poelman et al, 2013a) but such comparisons typically do not provide any clear results as long as none of the two networks is of high and validated performance. Ideally one of the networks should be a kind of reference network for a certain performance parameter.…”