<p>  </p> <p>Rapid intensification of low-level rotation in non-classic tornadic storms in southeastern United States, often at time scales shorter than the volume updates from existing opera- tional radars, calls for a deeper understanding of storm-scale processes. There is growing evidence that the highly nonlinear interactions between vertical wind shear and cold pools regulate the intensity of downdrafts, low- and mid-level updrafts, and thus tornadic poten- tial in supercells. Tornado-strength circulations are more likely associated with cold pools of intermediate strength. The microphysical pathway leading to storm electrification also plays a major role in the regulation of cold pool intensity. Storm electrification and subsequent lightning initiation are a by-product of charging of ice hydrometeors in the mixed-phase updrafts. Lightning flashes frequently initiate along the periphery of turbulent updrafts and total flash rate is controlled by the updraft speed and volume.</p> <p><br></p> <p>In the first part of this work, polarimetric fingerprints like ZDR and KDP columns (proxies for mixed-phase updraft strength) are objectively identified to track rapid fluctuations in updraft intensity. We quantify the volume of ZDR and KDP columns to evaluate their utility in predicting temporal variability in lightning flash characteristics and the onset of severe weather. Using observational data from KTLX radar and Oklahoma Lightning Mapping Array, we had previously found evidence of temporal covariance between ZDR column volume and the total lightning flash rate in a tornadic supercell in Oklahoma. </p> <p><br></p> <p> Here, we extend our analysis to three high-shear low-CAPE (HSLC) cases observed during the 2016-17 VORTEX-SE field campaign in Northern Alabama. In all three scenarios (one tornadic and one nontornadic supercell, and a quasi-linear convective system), the KDP column volume had a stronger correlation with total flash rates than the ZDR column volume. We also found that all three storms maintained a normal tripole charge structure, with majority of the cloud-to-ground (CG) strikes lowering negative charge to the ground. The tornadic storm’s CG polarity changed from negative to positive at the same time it entered a region with higher surface equivalent potential temperature. In contrast to the Oklahoma storm, lightning flash initiations in HSLC storms occurred primarily outside the footprint of ZDR and KDP column objects.</p> <p><br></p> <p>Storm dynamics coupled with microphysical processes such as diabatic heating/cooling and advection/sedimentation of hydrometeors also plays a significant role in electrification of thunderstorms. Simulation of deep convection, therefore, needs to account for the feedback of microphysics to storm dynamics. In the second part of this work, the NSSL microphysics scheme is used to simulate ice mass fluxes, cold pool intensity, and noninductive charging rates. The scheme is run in its triple-moment configuration in order to provide a more realis- tic size-sorting process that avoids pathologies that arise in double-moment representations.</p> <p><br></p> <p>We examine the possible tertiary linkage between noninductive charging rates and cold pool through their dependence on mixed-phase microphysical processes. The Advanced Re- gional Prediction System (ARPS) model is used to simulate the same three HSLC cases from VORTEX-SE 2016-17 IOPs. WSR-88D radar reflectivity and Doppler velocity observations are assimilated in a 40-member ensemble using an ensemble Kalman filter (EnKF) filter.</p> <p><br></p> <p>In all three cases, the simulated charge separation is consistent with the observed normal tripole. Greater updraft mass flux, supercooled liquid water concentration, and nonprecip- itation mass flux explain the nontornadic supercell’s higher total flash rate compared to the tornadic supercell. Positive and negative graupel charging rates were found to have the greatest linear correlation with updraft mass flux, followed by precipitation mass flux in all three cases. At zero time lag, horizontal buoyancy gradients associated with a surface cold pool were not found to be correlated with either the charging rates or the updraft and precipitation mass flux. Total flash rate based on empirical relationships between simulated ice mass fluxes was lower than the observed values.</p>

Severe weather outbreaks are violent weather events that can cause major damage and injury. Unfortunately, forecast models can mistakenly predict the intensity of these events. Frequently, the prediction of outbreaks is inaccurate with regards to their intensity, hindering the efforts of forecasters to confidently inform the public about intensity risks. This research aims to improve outbreak intensity forecasting using severe weather parameters and an outbreak ranking index to predict outbreak intensity. Areal coverage values of gridded severe weather diagnostic variables, computed from the North American Regional Reanalysis (NARR) database for outbreaks spanning 1979 to 2013, will be used as predictors in an artificial intelligence modeling ensemble to predict outbreak intensity. NARR fields will be dynamically downscaled to a National Severe Storms Laboratory-defined WRF 4-km North American domain on which areal coverages will be computed. The research will result in a model that will predict verification information on the model performance.

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The use of numerical weather prediction (NWP) has brought significant improvements to severe weather outbreak forecasting; however, determination of the primary mode of severe weather (in particular tornadic and nontornadic outbreaks) continues to be a challenge. Uncertainty in model runs contributes to forecasting difficulty; therefore it is beneficial to a forecaster to understand the sources and magnitude of uncertainty in a severe weather forecast. This research examines the impact of data assimilation, microphysics parameterizations, and planetary boundary layer (PBL) physics parameterizations on severe weather forecast accuracy and model variability, both at a mesoscale and synoptic-scale level. NWP model simulations of twenty United States tornadic and twenty nontornadic outbreaks are generated. In the first research phase, each case is modeled with three different modes of data assimilation and a control. In the second phase, each event is modeled with 15 combinations of physics parameterizations: five microphysics and three PBL, all of which were designed to perform well in convective weather situations. A learning machine technique known as a support vector machine (SVM) is used to predict outbreak mode for each run for both the data assimilated model simulations and the different parameterization simulations. Parameters determined to be significant for outbreak discrimination are extracted from the model simulations and input to the SVM, which issues a diagnosis of outbreak type (tornadic or nontornadic) for each model run. In the third phase, standard synoptic parameters are extracted from the model simulations and a k-means cluster analysis is performed on tornadic and nontornadic outbreak data sets to generate synoptically distinct clusters representing atmospheric conditions found in each type of outbreak. Variations among the synoptic features in each cluster are examined across the varied physics parameterization and data assimilation runs. Phase I found that conventional and HIRS-4 radiance assimilation performs best of all examined assimilation variations by lowering false alarm ratios relative to other runs. Phase II found that the selection of PBL physics produces greater spread in the SVM classification ability. Phase III found that data assimilation generates greater model changes in the strength of synoptic-scale features than either microphysics or PBL physics parameterization.

<p> It has been hypothesized that some measurable properties of $Z_{DR}$ arcs in supercells may change in the minutes prior to tornadogenesis and tornadogenesis failure, and that $Z_{DR}$ arc area will change with SRH and can be used as a real-time proxy to estimate SRH. Output form the Cloud Model 1 (CM1) along with a polarimetric emulator is used to simulate $Z_{DR}$ arcs in 9 tornadic and 9 non-tornadic supercells. A random forest algorithm is used to automatically identify the $Z_{DR}$ arcs. Finally the inflow sector SRH is calculated at times when $Z_{DR}$ arcs are identified. To analyze the change in intensity and area a comparison between the average $Z_{DR}$ value inside and outside of the arc, as well as the spatial size of the arc and storm was done. Model calculated SRH is then compared to these metrics.</p> <p> </p> <p> It has also been observed that hail fallout complicates the automatic identification of $Z_{DR}$ arcs. In this study, three experiments are run where the simulated $Z_{DR}$ arcs are produced. One using all categories of hydrometeors, one where wet growth and melting of hail is excluded, and one excluding the contribution to $Z_{DR}$ from the hail hydrometeor category. The same analysis as above is repeated for all three experiments. Finally observed $Z_{DR}$ arcs are analyzed to see if these results are applicable to the real world. </p>

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