Automated thunderstorm tracking: utilization of three-dimensional lightning and radar data

V K Meyer,H Höller,H D Betz

doi:10.5194/acp-13-5137-2013

Abstract

Abstract. This paper presents a new hybrid method for automated thunderstorm observation by tracking and monitoring of electrically charged cells (ec-TRAM). The developed algorithm combines information about intense ground precipitation derived from low-level radar-reflectivity scans with three-dimensionally resolved lightning data, which are provided by the European VLF/LF lightning detection network LINET. Based on the already existing automated radar tracker rad-TRAM (Kober and Tafferner, 2009), the new method li-TRAM identifies and tracks electrically active regions in thunderclouds using lightning data only. The algorithm ec-TRAM uses the output of the two autonomously operating routines rad-TRAM and li-TRAM in order to assess, track, and monitor a more comprehensive picture of thunderstorms. The main motivation of this work is to assess the benefit of three-dimensionally resolved total lightning (TL) information for thunderstorm tracking and monitoring. The focus is laid on the temporal development whereby TL is characterized by an effective in-cloud (IC) and cloud-to-ground (CG) event discrimination. It is found that the algorithms li-TRAM and ec-TRAM are both feasible methods for thunderstorm monitoring with potential for nowcasting. The tracking performance of li-TRAM turns out to be comparable to that of rad-TRAM, a result that strongly encourages utilization of lightning data as independent data source for thunderstorm tracking. It is found that lightning data allow an accurate and close monitoring of storm regions with intense internal dynamics as soon as convection induces electrical activity. A case study shows that the current short-term storm dynamics are clearly reflected in the amount of strokes, change of stroke rates and IC/CG ratio. The hybrid method ec-TRAM outperforms rad-TRAM and li-TRAM regarding reliability and continuous assessment of storm tracks especially in more complexly developing storms, where the use of discharge information contributes to more detailed information about storm stage and storm evolution.

Highlights

M rithm ec-tracking and monitoring procedure (TRAM) uses the output of the two autonomously operating routines rad-TRAM and li-TRAM in order to as- Hazardous impacts of thunderstorm-related phenomena like sess, track, and monitor a more comprehensive picture of heavy rain, hail, and lightning strikes render short-term prethunderstorms
This paper focuses on three aspects: (1) the usefulness and performance of identifying and tracking thunderstorms based on lightning data alone and compared to conventional tracking using radar data, (2) the further improvement of thunderstorm nowcasting by a hybrid lightning and radar method, and (3) the comprehensive in-cloud and cloudto-ground event discrimination
A comparison of storm track duration (Fig. 6) shows that rad-TRAM and li-TRAM have equal tracking performances, but both tend to be outperformed by ec-TRAM

Summary

Introduction

M rithm ec-TRAM uses the output of the two autonomously operating routines rad-TRAM and li-TRAM in order to as- Hazardous impacts of thunderstorm-related phenomena like sess, track, and monitor a more comprehensive picture of heavy rain, hail, and lightning strikes render short-term prethunderstorms. It is found that lightning data allow et al, 2002; Hering et al, 2004; Forster and Tafferner, 2009; an accurate and close monitoring of storm regions with in- Rossi et al, 2010). By employing an advanced modification of the commonly used time-of-arrival (TOA) method, LINET calculates the emission height of a discharge source in addition to the two-dimensional location (latitude and longitude). This allows for a consistent discrimination between in-cloud and cloud-to-ground strokes that is independent of stroke parameters (e.g. like peak current and amplitude)

Methods

Results

Conclusion