Squall Line Research Articles

The influence of multiple groups of biological ice nucleating particles on microphysical properties of mixed-phase clouds observed during MC3E

Abstract. A new empirical parameterization (EP) for multiple groups of primary biological aerosol particles (PBAPs) is implemented in the aerosol–cloud model (AC) to investigate their roles as ice nucleating particles (INPs). The EP describes the heterogeneous ice nucleation by (1) fungal spores, (2) bacteria, (3) pollen, (4) detritus of plants, animals, and viruses, and (5) algae. Each group includes fragments from the originally emitted particles. A high-resolution simulation of a midlatitude mesoscale squall line by AC is validated against airborne and ground observations. Sensitivity tests are carried out by varying the initial vertical profiles of the loadings of individual PBAP groups. The resulting changes in warm and ice cloud microphysical parameters are investigated. The changes in warm microphysical parameters, including liquid water content and cloud droplet number concentration, are minimal (<10 %). Overall, PBAPs have little effect on the ice number concentration (<6 %) in the convective region. In the stratiform region, increasing the initial PBAP loadings by a factor of 1000 resulted in less than 40 % change in ice number concentrations. The total ice concentration is mostly controlled by various mechanisms of secondary ice production (SIP). However, when SIP is intentionally shut down in sensitivity tests, increasing the PBAP loading by a factor of 100 has an effect of less than 3 % on the ice phase. Further sensitivity tests revealed that PBAPs have little effect on surface precipitation and on the shortwave and longwave flux (<4 %) for a 100-fold perturbation in PBAPs.

Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the hybrid three-dimensional ensemble–variational data assimilation (3DEnVar) algorithm in the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. A domain of 3 km horizontal grid spacing is configured, and hourly update cycles are performed using initial and lateral boundary conditions from the 3 km grid High-Resolution Rapid Refresh (HRRR). Results show that a baseline RRFS run is able to represent the observed convection, although with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and 25 % of the static BEC with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius from 3 layers to 1 layer in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection is greatly improved when using planetary boundary layer pseudo-observations, especially at 4 h forecast, and the bias of the 2 h forecast of temperature is reduced below 800 hPa. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into the current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

Is an NWP-Based Nowcasting System Suitable for Aviation Operations?

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.

A Case Study of Midlatitude Noctilucent Clouds and Its Relationship to the Secondary‐Generation Gravity Waves Over Tropopause Inversion Layer

AbstractA sporadic case of noctilucent clouds (NLCs) was observed unexpectedly on the night of the 6–7 July 2020 in Beijing (40°2′N, 115°30′E). The noticeable wavy structures and observed ambient temperature (135.54K at the mesosphere and lower thermosphere [MLT]), both indicated that the increase in temperature oscillations could be the cold phase of gravity waves (GWs). The reasons for NLC formation were analyzed based on the observations and model data sets in our study. The real‐time synoptic analysis revealed that there were GWs originally generated by a squall line in the troposphere. Due to the blocked effect of a stable tropopause inversion layer (TIL), the GWs broke, leading to strong energy dissipation near the TIL. The reverse ray tracing analysis between the TIL and NLCs' layer revealed the travel distance (206.88 km) and time (49.91 min) of GWs. These findings show that the turbulence over the TIL (at approximately 14.64 km) excited secondary GWs, which propagated upwards toward the mesosphere and probably interacted with diurnal and semi‐diurnal tides. The cold phase of the larger‐amplitude waves can provide optimal conditions for NLCs forming. Our study highlights the significance of dynamic coupling mechanisms regarding the effects from troposphere to MLT thermal conditions and offers a case study for the increasing occurrences of NLCs at midlatitudes.

Effects of Convective Mergers on the Evolution of Microphysical and Electrical Activity in a Severe Squall Line Simulated by WRF Coupled With Explicit Electrification Scheme

AbstractTo investigate the process of convective merger (CM) and its effect on thunderstorms evolution and corresponding electrical activity, a severe squall line that took place on 27 July 2015 over the Beijing Metropolitan Region (BMR) is simulated and studied using the Weather Research and Forecasting (WRF) model coupled with an explicit electrification lightning scheme (E‐WRF). The model‐simulated radar reflectivity reasonably captured the whole squall line evolution, including the merging of individual cells and storms. The variation of normalized flash frequency simulated by E‐WRF is highly consistent with the occurrence of a prominent flash rate surge during the CM both in the observation and the simulation. Cloud bridge is one of the regions where lightning events increase most. The upstream anvil and the sinking outflow led to the rapid connection and formation of new convective cells between the two older main storms. Subsequently, the mass of graupel and snow increased substantially at the middle level, and the mass of upper‐level ice was horizontally advected from upstream. During the merger, the in‐cloud charge distribution evolved from a staggered charge pocket structure into a vertically stratified five‐layer structure. This study supports the hypothesized role of CM in enhancing lightning activity, and further expands our understanding of CM effects on microphysics and charge‐redistribution.

Assimilation of Polarimetric Radar Observation With GSI Cloud Analysis for the Prediction of a Squall Line

AbstractDual‐polarization radars can provide rich three‐dimensional (3D) information on cloud precipitation structure. To utilize the existing polarimetric radar network in operational data assimilation systems, a new polarimetric radar‐based cloud analysis method is introduced. New features include the employment of fuzzy‐logic hydrometeor classification, improved estimation of liquid and ice regions, and newly‐added number concentration estimation. The new scheme is evaluated with a typical squall line case. Results show that the 3D cloud precipitation structure and short‐term precipitation forecast is consistently improved. With extra information from the polarimetric observations, the new scheme is able to produce reasonable polarimetric signatures for analysis. More supercooled water results in more latent heat release which enhances the updraft, leading to stronger convection in the subsequent forecast. This study emphasizes the importance of correct initial liquid and ice particle condition for the prediction of deep convection.

Assimilation of the pseudo-water vapor derived from extrapolated radar reflectivity to improve the forecasts of convective events

In this study, an approach is proposed to improve the numerical weather prediction (NWP) of convective event forecasts by combining very short-term extrapolated reflectivity (0−1h) with a numerical model. After direct assimilation of real radar observations, the pseudo-water vapor estimated from future extrapolated reflectivity is assimilated using a cycled three-dimensional variational assimilation (3DVAR) system. To filter extrapolated reflectivity values with large errors and extract reliable and useful convection information, a proposed regional filtering procedure is performed on the extrapolated reflectivity. In addition, regional filters of different scales and the reflectivity of different extrapolation times are examined. The assimilation results for two convection events with different characteristics show that assimilation of the pseudo-water vapor estimated from extrapolated reflectivity can improve the reflectivity and accumulated precipitation forecasting. The improvement effect is more significant in case 1, which is a well-organized squall line event, and less effective in case 2, which is a faster developing and evolving local convection event. In both cases, the regional filtering method can limit the wet bias in the analysis field and suppress spurious forecasts of reflectivity and precipitation caused by assimilating the pseudo-water vapor estimated from extrapolated reflectivity without regional filters. Additionally, the assimilation of a shorter (30 min) time window may prevent the introduction of more error into the analysis field, but the improvement of the forecasting skill may be limited.

Comparison of severe convection forecasts over China from assimilating Doppler radar observations using 4DEnKF and EnKF approaches

This study compared forecasts of a squall line and a mesoscale convective system (MCS) over China produced by assimilating Doppler radar data using four-dimensional ensemble Kalman filter (4DEnKF) and EnKF methods. In the 4DEnKF approach, four-dimensional ensemble covariances were incorporated to consider the time differences of the radar data collected at different elevation angles within a volume scan. In comparison with EnKF, results showed that 4DEnKF exhibited substantially higher quantitative forecast skills of radar reflectivity for a 6-h forecast, and better representation of structure and intensity of the squall line and MCS; EnKF largely failed to capture the intense convection segments of both cases. 4DEnKF demonstrated greater skills than EnKF in probabilistic reflectivity forecasts for thresholds of moderate and heavy precipitation. It was also found that 4DEnKF strengthened the cold pool and wind speed in the convective regions, and outperformed EnKF in terms of all standard variables at most levels, especially middle and lower levels. Diagnosis indicated that 4DEnKF was capable of producing much stronger analysis increments of temperature, wind and moisture during radar data assimilation cycles, which was favorable for initialization and development of convection.

The Influence of the Diurnal Cycle in Wind Shear and Thermodynamics on Squall Lines in the West African Monsoon

Abstract The West African monsoon has a clear diurnal cycle in boundary layer properties, synoptic flow, and moist convection. A nocturnal low-level jet (LLJ) brings cool, moist air into the continent and we hypothesize that it may support storms by providing vertical wind shear and a source of moisture. We use idealized simulations to investigate how the mean diurnal cycle in temperature and humidity compared with that of the wind shear impacts on mature squall lines. Thermodynamic diurnal changes dominate those of the winds, although when isolated the LLJ wind is favorable for more intense systems. Bulk characteristics of the storms, including in-cloud upward mass flux and—if precipitation evaporation is accounted for—total surface rain rates, correlate well with the system-relative inflow of convectively unstable air and moisture into the storms. Mean updraft speeds and mean rainfall rates over the storms do not correlate as well with system-relative inflows due to variations in storm morphology such as cold pool intensity. We note that storms tend to move near the speed of the African easterly jet and so maximize the inflow of convectively unstable air. Our results explain the observed diurnal cycle in organized moist convection, with the hours from 1800 to 0000 UTC being the most favorable. Storms are more likely to die after this, despite the LLJ supporting them, with the environment becoming more favorable again by midday. Significance Statement Large organized storms dominate rainfall in the West African Sahel, but models struggle to predict them at the correct time of day and the underlying mechanisms that control their timings are not well understood. Using idealized simulations, we show that the temperature and humidity of the late evening are favorable for such storms whereas inflow from the low-level jet supports storms overnight. Storm inflows of available energy and moisture predict upward mass transport and total rainfall rates, whereas the strength of the storm’s cold pool is important for storm structure and intensity. Our results demonstrate how the environmental wind profile (which varies throughout the day) interacts with internal storm dynamics, posing a major challenge to parameterized models.

Microphysical Piggybacking in the Weather Research and Forecasting Model

AbstractThis paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. “Driving the simulation” means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions.

Analysis of the Characteristics and Evolution Mechanisms of a Bow-Shaped Squall Line in East China Observed with Dual-Polarization Doppler Radars

To gain a deeper understanding of the formation and evolutionary mechanisms of a bow-shaped squall line (BSL) that occurred in East China on 10 May 2021, observations from S-band dual-polarization radars, a disdrometer and other instruments are used to investigate the characteristics and evolution of the kinematic, microphysical and radar echo structure within the squall line during its formative and mature stages. The results are as follows. The updraft induced by upper-level divergence and vertical thermal instability induced by the cold source at the middle and top of the troposphere provided environmental conditions suitable for the formation and strengthening of a squall line. The characteristics of the vertical vorticity at the leading edge of the squall line provided a good indication of its echo structure and evolutionary trend. The mechanism behind a new echo phenomenon—double high-differential reflectivity (ZDR) bands—observed in plan position indicator scans produced by the dual-polarization radar is investigated from the kinematic and microphysical structural perspectives. The evolutionary characteristics of the microphysical structure of the bulk of the squall line and its trailing stratiform cloud region are analyzed based on the quasi-vertical profiles retrieved from the S-band dual-polarization radar in Quzhou. Moreover, a conceptual model describing this type of BSL with a trailing region of stratiform rain in the warm sector is developed to provide technical support for the monitoring and early warning of BSLs.

Analysis of the 12 April 2020 Northern Louisiana Tornadic QLCS

On 12 April 2020, a tornadic quasi-linear convective system (QLCS) produced two EF-3 tornadoes in Ouachita Parish, Louisiana in close proximity to instrumentation operated by the University of Louisiana Monroe’s (ULM) Atmospheric Science program. In addition to the in situ environmental information, a high-resolution aerial damage survey was conducted by the ULM Unmanned Aerial Systems program. In this paper, these datasets are used to provide a comprehensive environmental and storm-scale analysis of the tornadic QLCS through northern Louisiana. In addition, we discuss the importance of aerial damage surveys, and how Doppler radar-derived tornado intensity estimates compared to the damage survey.

A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight safety. Monitoring and forecast of thunderstorms are, therefore, important topics. In this work, a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 h, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is, with 0.64, still quite high for the 60 min forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD), and is used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

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.

A Climatology of Snow Squalls in Southern New England 1994–2018

Abstract Snow squalls are sudden snow events that last less than 1 h, are characterized by low visibility and gusty winds, and can result in notable societal impacts. This analysis develops a climatology of non-lake-effect snow squall events in southern New England for 1994–2018 and investigates the synoptic environment and mesoscale factors conducive to their formation. National Weather Service surface observations were used to identify events; sea level pressure maps, composite radar charts, and a cell-tracking algorithm were used to determine their organization and movement; and ERA5 hourly reanalysis data were used to analyze the associated synoptic and infer mesoscale features, as well as convective and symmetric instability. A total of 100 events were identified and categorized into four distinct types on the basis of the direction of movement of the associated radar echoes, which is closely linked to characteristic synoptic structures and mesoscale factors. The four types are Classic (squall movement from the northwest; 72 events), Atlantic (from the southwest; 15 events), Northern (from the north; 9 events), and Special (varying; 4 events). All types have a 500-hPa trough over the Northeast but differ in the structure of the trough and its relation to lower-level flow, which accounts for the differences in movement of the squalls. The snow events occur in shallow, convective squall lines, and the ingredients for convection were present in all cases. Both upright and symmetric instability are typically present, all cases had at least one lower-tropospheric layer with cyclonic differential vorticity advection, and many cases were also associated with frontogenesis.

Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.

IDENTIFICATION OF TROPICAL SQUALL LINE USING INFRARED CHANNEL HIMAWARI-8 SATELLITE IMAGERY (CASE STUDY OF 6-7 DECEMBER 2020 IN THE INDIAN OCEAN)

<p>Tropical squall line is a linear type of Mesoscale Convective Systems (MCS) phenomenon. On December 6-7, 2020, the Infrared (IR1) Himawari-8 satellite image in the Indian Ocean of Indonesian region, shows a cloud line identified as the tropical squall line. This study aims to identify the characteristics of the tropical squall line phenomenon that occurs in the Indian Ocean south of West Java using Himawari-8 Infrared (IR1) satellite imagery. Satellite image data is processed using an algorithm adapted to the MCC Maddox 1980 criteria. Furthermore, an objective analysis is carried out on the data based on the criteria from previous studies. The result shows that the tropical squall occurred for 19 hours with the initial type of tropical squall formation as intersecting convective band. In the mature stage, the trailing stratiform region and convective line develops an asymmetric pattern and shows a vortex (Mesoscale Convective Vortices) that forms inside the stratiform region. The result of rainfall distribution using the GSMaP model shows a category of heavy rain with rainfall in tropical squall areas exceeding 10 mm per hour.</p>

Nexus of ambient flow and squall line via turbulence in the March 2018 meso-scale convective system over Southeast China

On March 4, 2018, an extremely strong squall line occurs suddenly over Southeast China, incurring devastatingly strong surface winds. In this study, we first present a high-resolution model simulation, then apply the recently developed theory of canonical transfer, which bears a form reminiscent of the Poisson bracket in Hamiltonian mechanics, to investigate the underlying dynamical processes. Using a recently developed functional analysis apparatus, multiscale window transform, the fields are reconstructed onto three scale windows, namely, ambient flow window, squall line-scale window, and turbulence window, which for convenience are denoted as windows 0, 1, 2, respectively. It is found that, among all the canonical transfers of kinetic energy (KE), only that between windows 0 and 2, and that between windows 1 and 2, are significant, and the two correspond remarkably well in space, except for opposite signs. Specifically, at the leading edge, KE is first transferred from the ambient flow over the spectrum all the way to the turbulence scale processes, and then goes to window 1 to form the squall line. At the trailing edge, this process is reversed. While the downscale cascading at the leading edge and upscale transfer at the trailing edge are expected in the classical shock hypothesis of squall lines, the existence of the secondary upscale and downscale transfers, respectively, at the leading and trailing edge, are not seen before. Most importantly, these secondary canonical transfers make the turbulence-scale window function like a hub, bridging the ambient flow and the squall line. To our knowledge, such an energetic scenario, i.e., the nexus of two adjacent scales in a spectrum via a “remote mediator” is not seen before in the literature. This study testifies to the importance of turbulent flows in the maintenance of squall lines, and may yield a clue to a better simulation.

Simulated QLCS Vortices in a High-Shear, Low-CAPE Environment

Abstract Tornadoes produced by quasi-linear convective systems (QLCSs) in low instability environments present distinctive challenges for forecasters. This study analyzes a population of 56 vortices (all cyclonic) in a full-physics, case study simulation to examine vortex characteristics and their relationships to the pre-line environment. Peak surface vortex intensity correlates with peak vortex depth, peak surface wind speed, and vortex pathlength. The strongest vortices are the deepest and longest lived, implying that they would be most detectable. The modeled surface vortices are primarily associated with gust front cusps and bow echoes, line breaks, and supercell-like features. Strong vortices frequently have sustained, superposed surface vorticity and near-ground updrafts for several minutes. Although weak vortices lack this superposition, they often exhibit impressive midlevel vorticity and midlevel updrafts. The environments of the weak and strong vortices are similar with small, yet statistically significant, differences in several thermodynamic and kinematic fields. The profiles near strong vortices have more low-level CAPE, steeper lapse rates, and stronger deep-layer vertical wind shear. However, the small magnitudes of the differences imply that forecasters might struggle to discriminate well between nontornadic and tornadic environments in high-shear, low-CAPE events. Despite the similarities, the profiles produce distinct reflectivity, updraft, and vertical vorticity distributions in idealized cloud model simulations. The most intense updrafts and vortices in the idealized runs occur when the environmental profiles from the strong vortex cases are combined with a QLCS orientation more normal to the lower-tropospheric vertical wind shear.

Storm-Scale Predictability and Analysis of the 13 April 2020 Central Savannah River Area Tornado Outbreak

Abstract An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.