Abstract
Abstract. Strong winds induced by extratropical storms cause a large number of power outages, especially in highly forested countries such as Finland. Thus, predicting the impact of the storms is one of the key challenges for power grid operators. This article introduces a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with forest inventory. We start by identifying storm objects from wind gust and pressure fields by using contour lines of 15 m s−1 and 1000 hPa, respectively. The storm objects are then tracked and characterized with features derived from surface weather parameters and forest vegetation information. Finally, objects are classified with a supervised machine-learning method based on how much damage to the power grid they are expected to cause. Random forest classifiers, support vector classifiers, naïve Bayes processes, Gaussian processes, and multilayer perceptrons were evaluated for the classification task, with support vector classifiers providing the best results.
Highlights
Strong winds caused by extratropical storms are among the most significant natural hazards in Europe, causing massive damage to the forests and society (e.g., Schelhaas et al, 2003; Schelhaas, 2008; Ulbrich et al, 2008; Seidl et al, 2014; Valta et al, 2019); extratropical storms are responsible for 53 % of all losses related to natural hazards in Europe (Kron and Schuck, 2013)
Random forest classifiers, support vector classifiers, naïve Bayes processes, Gaussian processes, and multilayer perceptrons were evaluated for the classification task, with support vector classifiers providing the best results
We present a novel method to identify, track, and classify extratropical storm objects based on how many power outages they are expected to induce
Summary
Strong winds caused by extratropical storms are among the most significant natural hazards in Europe, causing massive damage to the forests and society (e.g., Schelhaas et al, 2003; Schelhaas, 2008; Ulbrich et al, 2008; Seidl et al, 2014; Valta et al, 2019); extratropical storms are responsible for 53 % of all losses related to natural hazards in Europe (Kron and Schuck, 2013) Such storms pose a huge challenge for power distribution companies in highly forested countries such as Finland (Gardiner et al, 2010) where falling trees cause power outages for hundreds of thousands of customers every year (Niemelä, 2018). They require a large workforce to fix caused damage rapidly
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