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1. A Formula for the Maximum Vertical Velocity in Supercell Updrafts

   https://doi.org/10.1175/JAS-D-20-0103.1  

   Peters, John M. ; Morrison, Hugh ; Nowotarski, Christopher J. ; Mulholland, Jake P. ; Thompson, Richard L. ( November 2020 , Journal of the Atmospheric Sciences)

   null (Ed.)

   Abstract In supercell environments, previous authors have shown strong connections between the vertical wind shear magnitude, updraft width, and entrainment. Based on these results, it is hypothesized that the influences of entrainment-driven dilution on buoyancy and maximum updraft vertical velocity w in supercell environments are a predictable function of the vertical wind shear profile. It is also hypothesized that the influences of pressure perturbation forces on maximum updraft w are small because of a nearly complete offset between upward dynamic pressure forces and downward buoyant pressure forces. To address these hypotheses, we derive a formula for the maximum updraft w that incorporates the effects of entrainment-driven dilution on buoyancy but neglects pressure gradient forces. Solutions to this formula are compared with output from previous numerical simulations. This formula substantially improves predictions of maximum updraft w over past CAPE-derived formulas for maximum updraft w , which supports the first hypothesis. Furthermore, integrated vertical accelerations along trajectories show substantial offsets between dynamic and buoyant pressure forces, supporting the second hypothesis. It is argued that the new formula should be used in addition to CAPE-derived measures for w in forecast and research applications when accurate diagnosis of updraft speed is required. 

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2. Disentangling the Influences of Storm-Relative Flow and Horizontal Streamwise Vorticity on Low-Level Mesocyclones in Supercells

   https://doi.org/10.1175/JAS-D-22-0114.1  

   Peters, John M. ; Coffer, Brice E. ; Parker, Matthew D. ; Nowotarski, Christopher J. ; Mulholland, Jake P. ; Nixon, Cameron J. ; Allen, John T. ( January 2023 , Journal of the Atmospheric Sciences)

   Abstract Sufficient low-level storm-relative flow is a necessary ingredient for sustained supercell thunderstorms and is connected to supercell updraft width. Assuming a supercell exists, the role of low-level storm-relative flow in regulating supercells’ low-level mesocyclone intensity is less clear. One possibility considered in this article is that storm-relative flow controls mesocyclone and tornado width via its modulation of overall updraft extent. This hypothesis relies on a previously postulated positive correspondence between updraft width, mesocyclone width, and tornado width. An alternative hypothesis is that mesocyclone characteristics are primarily regulated by horizontal streamwise vorticity irrespective of storm-relative flow. A matrix of supercell simulations was analyzed to address the aforementioned hypotheses, wherein horizontal streamwise vorticity and storm-relative flow were independently varied. Among these simulations, mesocyclone width and intensity were strongly correlated with horizontal streamwise vorticity, and comparatively weakly correlated with storm-relative flow, supporting the second hypothesis. Accompanying theory and trajectory analysis offers the physical explanation that, when storm-relative flow is large and updrafts are wide, vertically tilted streamwise vorticity is projected over a wider area but with a lesser average magnitude than when these parameters are small. These factors partially offset one another, degrading the correspondence of storm-relative flow with updraft circulation and rotational velocity, which are the mesocyclone attributes most closely tied to tornadoes. These results refute the previously purported connections between updraft width, mesocyclone width, and tornado width, and emphasize horizontal streamwise vorticity as the primary control on low-level mesocyclones in sustained supercells. Significance Statement The intensity of a supercell thunderstorm’s low-level rotation, known as the “mesocyclone,” is thought to influence tornado likelihood. Mesocyclone intensity depends on many environmental attributes that are often correlated with one another and difficult to disentangle. This study used a large body of numerical simulations to investigate the influence of the speed of low-level air entering a supercell (storm-relative flow), the horizontal spin of the ambient air entering the thunderstorm (streamwise vorticity), and the width of the storm’s updraft. Our results suggest that the rotation of the mesocyclone in supercells is primarily influenced by streamwise vorticity, with comparatively weaker connections to storm-relative flow and updraft width. These findings provide important clarification in our scientific understanding of how a storm’s environment influences the rate of rotation of its mesocyclone, and the associated tornado threat. 

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3. The Influence of Shear on Deep Convection Initiation. Part II: Simulations

   https://doi.org/10.1175/JAS-D-21-0144.1  

   Peters, John M. ; Morrison, Hugh ; Nelson, T. Connor ; Marquis, James N. ; Mulholland, Jake P. ; Nowotarski, Christopher J. ( June 2022 , Journal of the Atmospheric Sciences)

   This study evaluates a hypothesis for the role of vertical wind shear in deep convection initiation (DCI) that was introduced in Part I by examining behavior of a series of numerical simulations. The hypothesis states, “Initial moist updrafts that exceed a width and shear threshold will ‘root’ within a progressively deeper steering current with time, increase their low-level cloud-relative flow and inflow, widen, and subsequently reduce their susceptibility to entrainment-driven dilution, evolving toward a quasi-steady self-sustaining state.” A theoretical model that embodied key elements of the hypothesis was developed in Part I, and the behavior of this model was explored within a multidimensional environmental parameter space. Remarkably similar behavior is evident in the simulations studied here to that of the theoretical model, both in terms of the temporal evolution of DCI and in the sensitivity of DCI to environmental parameters. Notably, both the simulations and theoretical model experience a bifurcation in outcomes, whereby nascent clouds that are narrower than a given initial radiusR₀threshold quickly decay and those above theR₀threshold undergo DCI. An important assumption in the theoretical model, which states that the cloud-relative flow of the background environmentV_CRdetermines cloud radiusR, is scrutinized in the simulations. It is shown that storm-induced inflow is small relative toV_CRbeyond a few kilometers from the updraft edge, andV_CRtherefore plays a predominant role in transporting conditionally unstable air to the updraft. Thus, the critical role ofV_CRin determiningRis validated.

    

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4. Entrainment in a Simulated Supercell Thunderstorm. Part II: The Influence of Vertical Wind Shear and General Effects upon Precipitation

   https://doi.org/10.1175/JAS-D-21-0289.1  

   Jo, Enoch ; Lasher-Trapp, Sonia ( May 2022 , Journal of the Atmospheric Sciences)

   Supercell thunderstorms can produce heavy precipitation, and an improved understanding of entrainment may help to explain why. In Part I of this series, various mechanisms of entrainment were identified in the rotating stage of a single simulated supercell thunderstorm. The current study examines the strength and effectiveness of these mechanisms as a function of the environmental vertical wind shear in eight different supercell simulations. Entrainment is calculated directly as fluxes of air over the surface of the storm core; tracers are used to assess the resulting dilution of the moistest air ingested by the storm. Model microphysical rates are used to compare the impacts of entrainment on the efficiency of condensation/deposition of water vapor on hydrometeors within the core, and ultimately, upon precipitation production. Results show that the ascending “ribbons” of horizontal vorticity wrapping around the updraft contribute more to entrainment with increasing vertical wind shear, while turbulent eddies on the opposite side of the updraft contribute less. The storm-relative airstream introduces more low-level air into the storm core with increasing vertical wind shear. Thus, the total entrainment increases with increasing vertical wind shear, but the fractional entrainment decreases, yielding an increase in undiluted air within the storm core. As a result, the condensation efficiency within the storm core also increases with increasing vertical wind shear. Due to the increase in hydrometeors detrained aloft and the resulting enhanced evaporation as they fall, the precipitation efficiency evaluated using surface rainfall decreases with increasing vertical wind shear, as found in past studies.

    

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

   https://doi.org/10.1029/2020EA001490  

   Molina, Maria J. ; Gagne, David John ; Prein, Andreas F. ( August 2021 , Earth and Space Science)

   This is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present‐day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high‐resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid‐levels for intense thunderstorm development when low‐level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human‐labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out‐of‐sample robustness with hyperparameter tuning in certain applications.

    

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