Storm Modes Research Articles

Exploring NWS Forecasters’ Assessment of AI Guidance Trustworthiness

Abstract As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, and performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (i) how they make guidance use decisions and (ii) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting the probability of severe hail or a 2D convolutional neural network model predicting the probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful and used tools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts. Significance Statement We used a mixed-methods quantitative and qualitative approach to understand how National Weather Service (NWS) forecasters 1) make guidance use decisions within their operational forecasting process and 2) assess the trustworthiness of prototype guidance developed using artificial intelligence (AI). When taken as a whole, our findings illustrate that forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically and suggest that developers must center the end user when creating new AI guidance tools to ensure that the developed tools are useful and used.

Lightning Characteristics Associated With Storm Modes Observed During RELAMPAGO

AbstractGlobal satellite studies show a maximum in deep convection and lightning downstream of the Andes in subtropical South America. The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign was designed to investigate the physical processes that contribute to the rapid development of deep convection and mesoscale convective systems (MCSs) in Argentina. A lightning mapping array (LMA) was deployed to Argentina as part of RELAMPAGO to collect lightning observations from extreme storms in the region. This study combines lightning data from the LMA and the Geostationary Lightning Mapper onboard GOES‐16 with 1‐km gridded radar data to examine the electrical characteristics of a variety of convective storms throughout their life cycle observed during RELAMPAGO. Results from the full campaign show 48% of flashes are associated with deep convection that occurs along the eastern edge of the Sierras de Córdoba (SDC) overnight. These flashes are 65 km2 smaller on average compared to stratiform flashes, which occur most frequently 50–100 km east of the SDC in the early morning hours, consistent with the upscale growth of MCSs off the terrain. Analysis of the 13–14 December MCS shows that sharp increases in flash rates correspond to deep and wide convective cores that have high graupel and hail mass, 35‐dBZ volume, and ice water path. This work validates previous satellite studies of lightning in the region, but also provides higher spatial and temporal resolution information across the convective life cycle that has not been available in previous studies.

Verification of Quasi-Linear Convective Systems Predicted by the Warn-on-Forecast System (WoFS)

Abstract Quasi-linear convective systems (QLCSs) can produce multiple hazards (e.g., straight-line winds, flash flooding, and mesovortex tornadoes) that pose a significant threat to life and property, and are often difficult to accurately forecast. The NSSL Warn-on-Forecast System (WoFS) is a convection-allowing ensemble system developed to provide short-term, probabilistic forecasting guidance for severe convective events. Examination of WoFS’s capability to predict QLCSs has yet to be systematically assessed across a large number of cases for 0–6-h forecast times. In this study, the quality of WoFS QLCS forecasts for 50 QLCS days occurring between 2017 and 2020 is evaluated using object-based verification techniques. First, a storm mode identification and classification algorithm is tuned to identify high-reflectivity, linear convective structures. The algorithm is used to identify convective line objects in WoFS forecasts and Multi-Radar Multi-Sensor system (MRMS) gridded observations. WoFS QLCS objects are matched with MRMS observed objects to generate bulk verification statistics. Results suggest WoFS’s QLCS forecasts are skillful with the 3- and 6-h forecasts having similar probability of detection and false alarm ratio values near 0.59 and 0.34, respectively. The WoFS objects are larger, more intense, and less eccentric than those in MRMS. A novel centerline analysis is performed to evaluate orientation, length, and tortuosity (i.e., curvature) differences, and spatial displacements between observed and predicted convective lines. While no systematic propagation biases are found, WoFS typically has centerlines that are more tortuous and displaced to the northwest of MRMS centerlines, suggesting WoFS may be overforecasting the intensity of the QLCS’s rear-inflow jet and northern bookend vortex. Significance Statement Quasi-linear convective systems (QLCSs), also known as squall lines, can be very destructive to life and property as they produce multiple hazards such as hail, severe straight-line winds, flash flooding, and tornadoes that typically form quickly and may be difficult to observe on radar. These storms can occur year-round and have the propensity to develop overnight or into the early morning hours, potentially catching the public off-guard. An ensemble prediction system called the Warn-on-Forecast System (WoFS), created by the National Severe Storms Laboratory, has shown promise in accurately forecasting a variety of severe weather events. This research evaluates the quality of the WoFS’s QLCS forecasts. Results show WoFS can accurately predict these systems for forecast times out to 6 h.

Development and Investigation of GridRad-Severe, a Multiyear Severe Event Radar Dataset

Abstract Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ∼100 severe weather days per year and upward of 1.3 million objectively tracked storms from 2010 to 2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports and the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single-cell, multicell, or mesoscale convective system storms) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies.

Diagnosing Storm Mode with Deep Learning in Convection-Allowing Models

Abstract While convective storm mode is explicitly depicted in convection-allowing model (CAM) output, subjectively diagnosing mode in large volumes of CAM forecasts can be burdensome. In this work, four machine learning (ML) models were trained to probabilistically classify CAM storms into one of three modes: supercells, quasi-linear convective systems, and disorganized convection. The four ML models included a dense neural network (DNN), logistic regression (LR), a convolutional neural network (CNN), and semisupervised CNN–Gaussian mixture model (GMM). The DNN, CNN, and LR were trained with a set of hand-labeled CAM storms, while the semisupervised GMM used updraft helicity and storm size to generate clusters, which were then hand labeled. When evaluated using storms withheld from training, the four classifiers had similar ability to discriminate between modes, but the GMM had worse calibration. The DNN and LR had similar objective performance to the CNN, suggesting that CNN-based methods may not be needed for mode classification tasks. The mode classifications from all four classifiers successfully approximated the known climatology of modes in the United States, including a maximum in supercell occurrence in the U.S. Central Plains. Further, the modes also occurred in environments recognized to support the three different storm morphologies. Finally, storm mode provided useful information about hazard type, e.g., storm reports were most likely with supercells, further supporting the efficacy of the classifiers. Future applications, including the use of objective CAM mode classifications as a novel predictor in ML systems, could potentially lead to improved forecasts of convective hazards. Significance Statement Whether a thunderstorm produces hazards such as tornadoes, hail, or intense wind gusts is in part determined by whether the storm takes the form of a single cell or a line. Numerical forecasting models can now provide forecasts that depict this structure. We tested several automated algorithms to extract this information from forecast output using machine learning. All of the automated methods were able to distinguish between a set of three convective types, with the simple techniques providing similarly skilled classifications compared to the complex approaches. The automated classifications also successfully discriminated between thunderstorm hazards, potentially leading to new forecast tools and better forecasts of high-impact convective hazards.

A method for generating a quasi-linear convective system suitable for observing system simulation experiments

Abstract. To understand the impact of different assimilated observations on convection-allowing model forecast skill, a diverse range of observing system simulation experiment (OSSE) case studies are required (different storm modes and environments). Many previous convection-allowing OSSEs predicted the evolution of an isolated supercell generated via a warm air perturbation in a horizontally homogenous environment. This study introduces a new methodology in which a quasi-linear convective system is generated in a highly sheared and modestly unstable environment. Wind, temperature, and moisture perturbations superimposed on a horizontally homogeneous environment simulate a cold front that initiates an organized storm system that spawns multiple mesovortices. Mature boundary layer turbulence is also superimposed onto the initial environment to account for typical convective-scale uncertainties. Creating an initial forecast ensemble remains a challenge for convection-allowing OSSEs because mesoscale uncertainties are difficult to quantify and represent. The generation of the forecast ensemble is described in detail. The forecast ensemble is initialized by 24 h full-physics simulations (e.g., radiative forcing, surface friction, and microphysics). The simulations assume different surface conditions to alter surface moisture and heat fluxes and modify the effects of friction. The subsequent forecast ensemble contains robust non-Gaussian errors that persist until corrected by the data assimilation system. This purposely degraded initial forecast ensemble provides an opportunity to assess whether assimilated environmental observations can improve, e.g., the wind profile. An example OSSE suggests that a combination of radar and conventional (surface and soundings) observations are required to produce a skilled quasi-linear convective system forecast, which is consistent with real-world case studies. The OSSE framework introduced in this study will be used to understand the impact of assimilated environmental observations on forecast skill.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

AbstractBased on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms.

Distinguishing between Hodographs of Severe Hail and Tornadoes

Abstract Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.

An Iterative Storm Segmentation and Classification Algorithm for Convection-Allowing Models and Gridded Radar Analyses

Abstract Thunderstorm mode strongly impacts the likelihood and predictability of tornadoes and other hazards, and thus is of great interest to severe weather forecasters and researchers. It is often impossible for a forecaster to manually classify all the storms within convection-allowing model (CAM) output during a severe weather outbreak, or for a scientist to manually classify all storms in a large CAM or radar dataset in a timely manner. Automated storm classification techniques facilitate these tasks and provide objective inputs to operational tools, including machine learning models for predicting thunderstorm hazards. Accurate storm classification, however, requires accurate storm segmentation. Many storm segmentation techniques fail to distinguish between clustered storms, thereby missing intense cells, or to identify cells embedded within quasi-linear convective systems that can produce tornadoes and damaging winds. Therefore, we have developed an iterative technique that identifies these constituent storms in addition to traditionally identified storms. Identified storms are classified according to a seven-mode scheme designed for severe weather operations and research. The classification model is a hand-developed decision tree that operates on storm properties computed from composite reflectivity and midlevel rotation fields. These properties include geometrical attributes, whether the storm contains smaller storms or resides within a larger-scale complex, and whether strong rotation exists near the storm centroid. We evaluate the classification algorithm using expert labels of 400 storms simulated by the NSSL Warn-on-Forecast System or analyzed by the NSSL Multi-Radar/Multi-Sensor product suite. The classification algorithm emulates expert opinion reasonably well (e.g., 76% accuracy for supercells), and therefore could facilitate a wide range of operational and research applications. Significance Statement We have developed a new technique for automatically identifying intense thunderstorms in model and radar data and classifying storm mode, which informs forecasters about the risks of tornadoes and other high-impact weather. The technique identifies storms that are often missed by other methods, including cells embedded within storm clusters, and successfully classifies important storm modes that are generally not included in other schemes, such as rotating cells embedded within quasi-linear convective systems. We hope the technique will facilitate a variety of forecasting and research efforts.

Evaluation of Satellite-Derived Signatures for Three Verified Hailstorms in Central Argentina

Córdoba Province in Argentina is a global hotspot for deep hail-producing storms. Previous studies of hail formation and detection largely relied on satellite snapshots or modeling studies, but lacked hail validation, relying instead on proxy metrics. To address this limitation, this study used hail collected in the mountainous Córdoba region in collaboration with the citizen science program “Cosecheros de Granizo 2018–2020” including from a record-breaking hail event and from the 2018–2019 RELAMPAGO field campaign. Three cases including a MCS and two supercells, which have verified hail in different environment locations relative to the Sierras de Córdoba, were analyzed for multi-spectral signatures in GOES-16 satellite data. Brightness temperatures decreased over time after convective initiation, reaching values cooler than the tropopause with variations around those values of different magnitudes. Overall, all cases exhibited a slight weakening of the updraft and strong presence of smaller ice crystal sizes just prior to the hail report, especially for the larger hailstones. The results demonstrate promise in using satellite proxies for hail detection in multiple environments for different storm modes. The long-term goal is to better understand hail-producing storms and unique challenges of forecasting hail in this region.

Biases in the Prediction of Convective Storm Characteristics with a Convection Allowing Ensemble

Abstract The severity, duration, and spatial extent of thunderstorm impacts are related to convective storm mode. This study assesses the skill of the High-Resolution Rapid Refresh Ensemble (HRRR-E) and its deterministic counterpart (HRRRv4) at predicting convective mode and storm macrophysical properties using 35 convective events observed during the 2020 warm season across the eastern United States. Seven cases were selected from each of five subjectively determined convective organization modes: tropical cyclones, mesoscale convective systems (MCSs), quasi-linear convective systems, clusters, and cellular convection. These storm events were assessed using an object-based approach to identify convective storms and determine their individual size. Averaged across all 35 cases, both the HRRR-E and HRRRv4 predicted storm areas were generally larger than observed, with this bias being a function of storm lifetime and convective mode. Both modeling systems also underpredicted the rapid increase in storm counts during the initiation period, particularly for the smaller-scale storm modes. Interestingly, performance of the HRRRv4 differed from that of the HRRR-E, with the HRRRv4 generally having a larger bias in total storm area than the HRRR-E due to HRRRv4 predicting up to 66% more storm objects than the HRRR-E. The HRRR-E accurately predicted the convective mode 65% of the time, with complete misses being very rare (<5% of the time overall). However, an evaluation of rank histograms across all 35 cases revealed that the HRRR-E tended to be underdispersive when predicting storm size for all but the MCS mode.

Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations

Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.

11-Year Radar-Based Study of Tornadic Thunderstorms over Central Oklahoma

In an 11-y period (2000–2010) 126 tornadoes affected central Oklahoma within a 111-km (60-nm) radius of the Twin Lakes, Oklahoma (KTLX) WSR-88D. The tornadoes resulted in 265 injuries and 3 deaths. This study used archived WSR-88D data to obtain information about storm characteristics such as mode, width, height, and measures of the mesocyclone, at the time of initial tornado formation. The radar data provided information about the supercell spectrum and highlighted differences between tornado-producing supercells and tornado-producing quasi-linear convective systems (QLCSs), especially with respect to midlevel rotational velocity. Warning lead-time information also was obtained and compared with the radar characteristics. No specific radar attribute was strongly correlated with lead time, likely due to the multitude of variables involved in the tornado warning process. A strong correlation did exist between lead time and storm mode. Applying these findings in an operational environment similar to that found in central Oklahoma may enhance tornado warning performance.

Spectrum of Near-Storm Environments for Significant Severe Right-Moving Supercells in the Contiguous United States

AbstractProximity soundings have long been used to explore how the vertical structure of temperature, humidity, and winds influence convective storms and their associated hazards. In severe thunderstorm research and forecasting, convective parameters are often used to summarize certain characteristics of the sounding. While extremely useful, these parameters are unable to describe the rich complexity that is readily apparent in hodographs and skew T–logp diagrams. Motivated by a desire to retain more of these details, the present study uses self-organizing maps (SOMs) to group soundings based on their full vertical structure. The analysis makes use of a sample of more than 10 000 model proximity soundings for right-moving supercells associated with tornadoes and significant severe hail and straight-line winds in the contiguous United States (CONUS). Separate SOMs are developed for the wind and thermodynamic profiles, each with 3 × 3 nodes, resulting in a set of nine hodographs and nine skew T–logp diagrams that broadly represent the spectrum of near-storm environments for significant severe right-moving supercells in the CONUS. Both SOMs are shown to provide a good representation of the variability in key convective parameters, although, for the thermodynamic SOM, variations in LCL heights and midlevel lapse rates are somewhat limited. Based on the soundings assigned to them, the SOM nodes are characterized in terms of their associated hazards, their relationship with storm mode and mesocyclone strength, and their spatial and temporal variability. Potential applications of the SOMs in severe weather forecasting and idealized numerical simulations are also highlighted.

Dual Geostationary Lightning Mapper Observations

AbstractTwo Geostationary Lightning Mappers (GLMs) now observe spatial and temporal lightning distributions over a vast region. The GOES-16 GLM covers most land areas in the Western Hemisphere, and detects ~4 times as much lightning as the GOES-17 GLM. Although the continents dominate the lightning distributions year-round, each season exhibits widespread lightning over parts of the Atlantic Ocean and within three broad regions over the Pacific. These oceanic regions demonstrate the key role convective organization plays in producing larger, longer-lasting, and more energetic flashes observed by both GLMs over the oceans. Texture within the flash densities reveals a close relationship with the underlying topography, underscored by the complex diurnal cycles observed along coastlines and in mountainous regions. GLM information beyond flash frequency provides additional insights into storm mode and evolution. For example, over the Sierra Madre Occidental, time series reveal initially small, short-duration GLM flashes growing larger and longer as storms grow upscale. These mesoscale convective systems often transition offshore, contributing to an average flash area maximum over the eastern Pacific. Data quality improves during the study period with tuning of the ground system software. GLM artifacts due to solar intrusion and sun glint greatly diminish following the blooming filter installation, and the second-level threshold filter reduces false events along particular subarray boundaries (i.e., bar artifacts). Analysis of the overlap region reveals a pronounced north–south line near 103°W, with the GOES-16 (GOES-17) GLM detecting more flashes to the east (west) of this line.

Cold Pool Characteristics of Tornadic Quasi-Linear Convective Systems and Other Convective Modes Observed during VORTEX-SE

AbstractMany numerical studies have focused on the importance of baroclinically generated vorticity at the edge of cold pools in supercellular tornadogenesis, and observational work has consistently found that strongly tornadic supercells have less dense, more buoyant cold pools than weakly or nontornadic supercells. However, there is a lack of observational studies that consider potential relationships between cold pool characteristics (e.g., density) and tornado production within linear systems, such as mesoscale convective system (MCS) or quasi-linear convective system (QLCS) events. This study presents two tornadic QLCS events that were observed during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) field project in 2016 and 2017. Supercell and hybrid modes were also observed and compared to the observations from the linear systems. No obvious differences in the thermodynamic deficits of the tornadic and nontornadic samples were found, likely due to the weakness of the produced tornadoes (≤EF1) and the small tornadic sample size (five cold pools). Comparison across storm mode did find some differences, with QLCS cold pools producing larger virtual potential temperature and psuedoequivalent potential temperature deficits than those observed in supercells. More importantly, our findings suggest that, in a QLCS, the magnitude of density gradients along the leading edge of the cold pool may be related to tornadogenesis by virtue of the implied baroclinic vorticity generation.

The Influence of Terrain on the Convective Environment and Associated Convective Morphology from an Idealized Modeling Perspective

AbstractOrographic deep convection (DC) initiation and rapid evolution from supercells to mesoscale convective systems (MCSs) are common near the Sierras de Córdoba, Argentina, which was the focal point of the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. This study used an idealized numerical model with elongated north–south terrain similar to that of the Sierras de Córdoba to address how variations in terrain height affected the environment and convective morphology. Simulations used a thermodynamic profile from a RELAMPAGO event that featured both supercell and MCS storm modes. Results revealed that DC initiated earlier in simulations with higher terrain, owing both to stronger upslope flows and standing mountain waves. All simulations resulted in supercell formation, with higher-terrain supercells initiating closer to the terrain peak and moving slower off the terrain. Higher-terrain simulations displayed increases in both low-level and deep-layer wind shear along the eastern slopes of the terrain that were related to the enhanced upslope flows, supporting stronger and wider supercell updrafts/downdrafts and a wider swath of heavy rainfall. Deeper and stronger cold pools from these wider and stronger higher-terrain supercells led to surging outflow that reduced convective available potential energy accessible to deep convective updrafts, resulting in quicker supercell demise off the terrain. Lower-terrain supercells moved quickly off the terrain, merged with weaker convective cells, and resulted in a quasi-organized MCS. These results demonstrate that terrain-induced flow modification may lead to substantial local variations in convective morphology.

The Mechanisms Responsible for Large Near-Surface Vertical Vorticity within Simulated Supercells and Quasi-Linear Storms

AbstractDespite their structural differences, supercells and quasi-linear convective systems (QLCS) are both capable of producing severe weather, including tornadoes. Previous research has highlighted multiple potential mechanisms by which horizontal vorticity may be reoriented into the vertical at low levels, but it is not clear in which situation what mechanism dominates. In this study, we use the CM1 model to simulate three different storm modes, each of which developed relatively large near-surface vertical vorticity. Using forward-integrated parcel trajectories, we analyze vorticity budgets and demonstrate that there seems to be a common mechanism for maintaining the near-surface vortices across storm structures. The parcels do not acquire vertical vorticity until they reach the base of the vortices. The vertical vorticity results from vigorous upward tilting of horizontal vorticity and simultaneous vertical stretching. While the parcels analyzed in our simulations do have a history of descent, they do not acquire appreciable vertical vorticity during their descent. Rather, during the analysis period relatively large horizontal vorticity develops as a result of horizontal stretching, and therefore this vorticity can be effectively tilted into the vertical.

Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events

AbstractThis research quantifies the spatiotemporal statistics of composite radar reflectivity in the vicinity of severe thunderstorm reports. By using over 20 years (1996–2017) of data and 500,000 severe thunderstorm reports, this study presents the most comprehensive analysis of the mesoscale presentation of radar reflectivity composites during severe weather events to date. We first present probability matched mean composites of approximately 5,000 radar images centred on tornado reports that contain one of three types of manually‐labelled convective storm modes—namely, (a) quasi‐linear convective system (QLCS); (b) cellular; or (c) tropical system. Next, we generate composites for tornado report data stratified by EF‐scale and for four temporal periods during which notable severe weather events took place. The data are then stratified by hazard, region, season, and time of day. The results show marked spatiotemporal and intra‐hazard variability in radar presentation. In general, cellular convection is favoured in the Great Plains of the United States, whereas QLCS convection is favoured in the Southeast United States. Night and cool‐season subsets showed a preference for QLCS convection, whereas day and warm‐season subsets showed a preference for cellular convection. These results agree well with the existing literature and suggest that the data extraction and organization approach is sound. Because of this, these data will be useful for future image classification studies in climate and atmospheric sciences—particularly those involving storm mode classification.

The Influences of Effective Inflow Layer Streamwise Vorticity and Storm-Relative Flow on Supercell Updraft Properties

Abstract The relationship between storm-relative helicity (SRH) and streamwise vorticity ωs is frequently invoked to explain the often robust connections between effective inflow layer (EIL) SRH and various supercell updraft properties. However, the definition of SRH also contains storm-relative (SR) flow, and the separate influences of SR flow and ωs on updraft dynamics are therefore convolved when SRH is used as a diagnostic tool. To clarify this issue, proximity soundings and numerical experiments are used to disentangle the separate influences of EIL SR flow and ωs on supercell updraft characteristics. Our results suggest that the magnitude of EIL ωs has little influence on whether supercellular storm mode occurs. Rather, the transition from nonsupercellular to supercellular storm mode is largely modulated by the magnitude of EIL SR flow. Furthermore, many updraft attributes such as updraft width, maximum vertical velocity, vertical mass flux at all levels, and maximum vertical vorticity at all levels are largely determined by EIL SR flow. For a constant EIL SR flow, storms with large EIL ωs have stronger low-level net rotation and vertical velocities, which affirms previously established connections between ωs and tornadogenesis. EIL ωs also influences storms’ precipitation and cold-pool patterns. Vertical nonlinear dynamic pressure acceleration (NLDPA) is larger at low levels when EIL ωs is large, but differences in NLDPA aloft become uncorrelated with EIL ωs because storms’ midlevel dynamic pressure perturbations are substantially influenced by the tilting of midlevel vorticity. Our results emphasize the importance of considering EIL SR flow in addition to EIL SRH in the research and forecasting of supercell properties.