Atmosphere (Oct 2020)
Satellite-Based Study and Numerical Forecasting of Two Tornado Outbreaks in the Ural Region in June 2017
Abstract
Strong tornadoes are common for the European part of Russia but happen rather rare east of the Urals. June 2017 became an exceptional month when two tornado outbreaks occurred in the Ural region of Russia, yielded $3 million damage, and resulted in 1 fatality and 14 injuries. In this study, we performed detailed analysis of these outbreaks with different data. Tornadoes and tornado-related environments were diagnosed with news and eyewitness reports, ground-based meteorological observations, sounding data, global numerical weather prediction (NWP) models data, synoptic charts, satellite images, and data of specially conducted aerial imaging. We also estimated the accuracy of short-term forecasting of outbreaks with the WRF-ARW mesoscale atmospheric model, which was run in convection-permitting mode. We determined the formation of 28 tornadoes during the first outbreak (3 June 2017) and 9 tornadoes during the second outbreak (18 June 2017). We estimated their intensity using three different approaches and confirmed that, based on the International Fujita scale (IF), one of the tornadoes had the IF4 intensity, being the first IF4 tornado in Russia in the 21st century and the first-ever IF4 tornado reported beyond the Ural Mountains. The synoptic-scale analysis revealed the similarity of two outbreaks, which both formed near the polar front in the warm part of deepening southern cyclones. Such synoptic conditions yield mostly weak tornadoes in European Russia; however, our analysis indicates that these conditions are likely favorable for strong tornadoes over the Ural region. Meso-scale analysis indicates that the environments were favorable for tornado formation in both cases, and most severe-weather indicators exceeded their critical values. Our analysis demonstrates that for the Ural region, like for other regions of the world, combined use of the global NWP model outputs indicating high values of severe-weather indices and the WRF model forecast outputs explicitly simulating tornadic storm formation could be used to predict the high probability of strong tornado formation. For both analyzed events, the availability of such tornado warning forecast could help local authorities to take early actions on population protection.
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