Development of an Ensemble-Based Forecasting Tool of Hazardous Icing Conditions for German Transmission System Operators

Abstract

<div class="section abstract"><div class="htmlview paragraph">Weather situations that are associated with hazardous icing conditions near the ground impact multiple kinds of infrastructure. A prominent example of a transmission system failure due to wintry conditions was the snow storm over the Münsterland, Germany, in 2005, as exceptional high amounts of wet snow in conjunction with supercooled water led to heavy damages on conductor cables and tower collapses (Klinger et al., <span class="xref">2011</span>). In this work, an interdisciplinary approach between energy and aviation meteorology is presented. The study was conducted for the purpose of providing a forecast tool of hazardous icing conditions for German transmission system operators (TSOs). TSOs are faced with icing on ground-based objects just like aviation on the ground.</div><div class="htmlview paragraph">For the purpose of obtaining an ensemble-based forecasting tool to detect hazardous icing of structures near the ground, the already existing aeronautical icing model “Advanced Diagnosis and Warning System for Aircraft Icing Environments” (ADWICE) (Tafferner et al., <span class="xref">2003</span>; Leifeld, <span class="xref">2004</span>) is expanded by the ensemble prediction system ICON-EU-EPS (ADWICE-EPS). Due to differences in meteorology and customer’s needs between in-flight icing conditions and icing on ground-based obstacles like transmission lines, ADWICE-EPS is tried to be tailored towards the scope of application. ADWICE’s ability to distinguish between different icing regimes (scenarios) is maintained and a forecasting tool for precipitation icing is developed by involving forecast fields of ICON-EU- EPS. In order to compare the performance of the original and the extended tool, case studies are conducted for this icing regime.</div><div class="htmlview paragraph">The findings suggest that modifications generate better results than the original tool. For the two precipitation icing case studies, certain aspects of the ADWICE algorithm cause biases. By implementing further data, the detected biases are lowered substantially. Limitations for this work arise from the small number of cases which at the same time provides a basis for future research.</div></div>