Exploring severe weather environments using CM1 simulations: The 29 August 2020 event in the Balearic Islands

Abstract

On the morning of 29 August 2020, a supercell developed to the west of Mallorca (Balearic Islands, Spain) and then crossed the island. The storm produced strong winds, at least one EF2 category tornado on the Enhanced Fujita scale, heavy precipitation and large hail. The affected areas suffered power and landline phones outages, massive falling of trees that caused the closure of roads, and material damages on properties and infrastructures. Unfortunately this kind of high impact events are quite common in the western Mediterranean, specially during or near autumn, but generally elude the capabilities of standard forecasting procedures. A better understanding of their development and any improvement in their prediction are crucial to issue better warnings to civil protection and the general public. Assessment of the Convective Available Potential Energy and Storm Relative Helicity present in the prior environment highlights the area where the actual supercell developed. However, neither the severity of the convective system nor other important properties such as its trajectory, can be predicted by the classic analysis of larger scale ingredients. This paper proposes a novel approach to obtain this additional information by applying the CM1 model. This cloud-resolving model was developed precisely to study convective storms and therefore emerges as the ideal tool to provide clues on the likelihood of a supercell and its severity and trajectory. Specifically, we test the systematic run of several CM1 simulations over the favorable area highlighted by the classic analysis and explore the convective structures that can develop and evolve in such environments. The results point out the ability of the CM1-based method to capture for the 29 August 2020 event the supercell formation, its trajectory and severity parameters. Therefore, a CM1-based strategy can provide useful details about eventual convective structures and their characteristics, allowing forecasters to issue better weather warnings at operational level.