Moisture Attribution and Sensitivity Analysis of a Winter Tornado Outbreak

Abstract

The tornado outbreak of 21–23 January 2017 caused 20 fatalities, more than 200 injuries, and over a billion dollars in damage in the Southeast United States. The event occurred concurrently with a record-breaking warm Gulf of Mexico (GoM) basin. This article explores the influence that warm GoM sea surface temperatures (SSTs) had on the tornado outbreak. Backward trajectory analysis, combined with a Lagrangian-based moisture-attribution algorithm, reveals that the tornado outbreak’s moisture predominantly originated from the southeast GoM and the northwest Caribbean Sea. We used the WRF Model to generate a control simulation of the event and explore the response to perturbed SSTs. With the aid of a tornadic storm proxy derived from updraft helicity, we show that the 21–23 January 2017 tornado outbreak exhibits sensitivity to upstream SSTs during the first day of the event. Warmer SSTs across remote moisture sources and adjacent waters increase tornado frequency, in contrast to cooler SSTs, which reduce tornado activity. Upstream SST sensitivity is reduced once convection is ongoing and modifying local moisture and instability availability. Our results highlight the importance of air–sea interactions before airmass advection toward the continental United States. The complex and nonlinear nature of the relationship between upstream SSTs and local precursor environments is also discussed.

Evaluation of convective parameters derived from pressure level and native ERA5 data and different resolution WRF climate simulations over Central Europe

“…Such deterioration is not observed in ERA5 and suggests that the large-scale circulation of the RCM increasingly diverges from that of the driving reanalysis away from the western boundaries. As the main purpose of dynamical downscaling is the addition of fine-scale details, it might be beneficial to prevent the deviation of the synoptic features in the RCM by decreasing the size of the 50-km domain (especially in the west-east direction) or applying spectral nudging (Molina et al 2020).…”

“…We performed a continuous integration (i.e., no reinitialization) without spectral nudging, which contrasts many previous studies examining model-derived convective environments (e.g., Robinson et al 2013;Trapp et al 2019;Rasmussen et al 2020). This methodology poses a potential limitation on the results as the RCM fields might significantly drift from reality, a problem that presumably exacerbates with the length of the simulation (Molina et al 2020). However, the statistical performance of the WRF runs utilized here shows no degradation with simulation time.…”

A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

“…Scalar thresholds for UH have been used to classify model simulated convection, with thunderstorms that exceed the predetermined threshold classified as severe (Sobash et al, 2011;Molina, Allen, & Prein, 2020). These dichotomous assignments derived from UH have been used in kilometer-scale climate simulations to estimate changes to severe hazards in a future climate (Trapp et al, 2011;Gensini & Mote, 2015).…”

“…Strongly rotating thunderstorms with high magnitudes of UH (e.g., ≥75 m 2 s ‐2 ) have a greater likelihood to be of supercell morphology (Clark et al., 2013; Sobash et al., 2016), a type of thunderstorm that observations have shown to be more likely to produce severe hazards (Bunkers et al., 2006; Duda & Gallus Jr, 2010). Scalar thresholds for UH have been used to classify model‐simulated convection, with thunderstorms that exceed the predetermined threshold classified as severe (Molina, Allen, & Prein, 2020; Sobash et al., 2011). These dichotomous assignments derived from UH have been used in kilometer‐scale climate simulations to estimate changes to severe hazards in a future climate (Gensini & Mote, 2015; Trapp et al., 2011).…”

“…However, the use of a heuristic to delineate non-severe and severe convection can result in incorrect categorizations of thunderstorms that fall near the predetermined threshold. UH values representative of severe convection also vary seasonally and regionally, based on the climatological environments that drive severe convection activity (Molina, Allen, & Prein, 2020;Sobash & Kain, 2017). Recently, Sobash et al (2020) trained a CNN to forecast severe hazard potential using severe thunderstorm parameters derived from WRF, showing that a CNN can learn from diagnostics.…”