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Abstract Quasi-linear convective system (QLCS) tornadoes are particularly difficult for forecasters to predict, commonly resulting in short or negative warning lead times. However, it is hypothesized that relatively deep, discrete updrafts may provide a nowcasting tool, indicating portions of a linear system where tornadogenesis is most probable due to enhanced low-level lifting capable of tilting and stretching vorticity. It is further hypothesized that discrete updraft properties correlate to tornado intensity and duration. Deep, discrete updrafts are easily identifiable in upper-tropospheric radar data, which are still available far from the nearest radar site, where low-level rotation data are unavailable. Using a dataset covering four years (2021–2024), it is shown 59.5% of QLCS tornadoes are preceded by discrete radar reflectivity cores several kilometers aloft, identified using gridded Multi-Radar Multi-Sensor (MRMS) data and indicating the presence of a deep updraft. The percentage of reflectivity core co-locations is higher for both more intense QLCS tornadoes (85.7% for EF-3 tornadoes) and for tornadoes occurring in the cool season (66.1%). Analysis of the near-storm environments in each case reveals 5–10 km shear is the best predictor both of tornado intensity and which tornadoes will be co-located with reflectivity cores, with greater shear resulting in more tornadoes having discrete updrafts. The operational applicability of reflectivity cores for QLCS tornadogenesis anticipation may be limited by the frequency of nontornadic cores, tornadoes without reflectivity cores, and cores offering only short lead times. 

Abstract This research attempts to use operational radar and satellite products to identify potential locations of quasi-linear convective system (QLCS) tornadogenesis, which can be difficult to predict. It is hypothesized that deep, discrete updrafts indicate portions of the QLCS capable of producing tornadoes, whereas shallower convection indicates more benign portions of the QLCS. To address this hypothesis, storm reports and storm surveys on 30–31 March 2022, during the second intensive observing period of the 2022 Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign, are used to identify locations of tornadoes within the QLCS. These tornado locations are then compared to representations of upper-tropospheric updrafts, namely, overshooting tops (OTs), which are identified with an algorithm using 1-min-resolution mesoscale sector data fromGOES-16Advanced Baseline Imager infrared brightness temperatures, and radar reflectivity cores aloft, identified with Multi-Radar Multi-Sensor (MRMS) 3D mosaic reflectivity products. Only a fraction (less than 30%) of tornadoes within the QLCS are associated with OTs, though over 85% of tornadoes are located near convective cores as indicated by cores of enhanced reflectivity at 9 km MSL. A numerical simulation of the event is also conducted using the Weather Research and Forecasting (WRF) Model which shows a strong relationship between simulated updraft intensity and reflectivity aloft. Given this apparent support of the hypothesis, the identification of updraft signatures within MRMS and high-resolution geostationary satellite data may ultimately help improve the identification of regions within QLCSs most likely to result in tornadoes. 

Abstract The challenges associated with nowcasting quasi-linear convective system (QLCS) tornadoes are well documented. One key challenge is that QLCS tornadoes typically develop within mesovortices (MVs), but not all MVs are tornadic. This study used radar and in situ Pod data collected during the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign to examine the characteristics that differentiate tornadic (TOR), wind-damaging (WD), and nondamaging (ND) MVs at various stages in their lifetimes and to investigate the low-level structure of QLCS MVs. Thirty-one QLCS MVs were manually identified and cataloged using the lowest elevation scans of the nearest WSR-88D and C-band on Wheels (COW) radars during the two years of PERiLS. TOR MVs, over their entire lifetimes, had stronger rotational velocities (Vrots), smaller diameters, and slightly longer lifetimes compared to WD and ND MVs. When MVs were analyzed during their pretornadic, predamaging, and prewarning phases (prephases), TOR and WD MVs had similar Vrots; however, TOR MVs typically had smaller diameters and contracted leading up to tornadogenesis, which could benefit nowcasters. In five cases, MVs were observed at the lowest WSR-88D elevation scans but were not visible in the COW data; the MV structure at different elevation angles for one case is presented. Eight Pods showed evidence of MV intercepts, demonstrated most notably by decreases in pressure. COW data, along with relatively weak wind speeds measured by Pods that collected data on MVs, suggest that vertical variations in low-level MV structure and strength can exist, which may not be adequately captured by the WSR-88D network. 

Abstract Cold pools play a range of important roles in quasi-linear convective systems (QLCSs), including maintenance via the development of new convective cells as well as baroclinic generation of horizontal vorticity. Although a number of QLCS cold pools have been characterized in the literature using one or a few sensors, their variability (both internally and across a range of environments) has still not been widely studied. This gap in knowledge extends particularly to high-shear low-CAPE (HSLC) convective environments common to the cool season in the southeastern United States, where the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign was focused. PERiLS specifically targeted environmental and storm-scale processes in QLCSs, including their cold pools. Our analysis focuses on the heterogeneity and temporal variability of cold pools across short time and spatial scales using numerous surface and sounding observations across five PERiLS QLCSs. The PERiLS cold pools are generally weaker than those previously studied in warm-season, midlatitude QLCSs, likely due to the lower CAPE and higher relative humidity values common to HSLC environments during PERiLS. Nevertheless, the distributions of most PERiLS cold pool variables at least partially overlap with those of previously studied QLCSs. The median PERiLS measurement reveals a cold pool that is ≈2.5 km deep, having a surface temperature decrease of ≈−6°C, and a peak outflow wind gust of ≈13 m s⁻¹. In the spirit of a “cold pool audit,” we present the internal and case-to-case variability of these particularly well-observed QLCSs. Significance StatementEvaporatively cooled air masses (“cold pools”) are created by quasi-linear convective systems (“QLCSs,” also called “squall lines”), and they in turn play important roles in the maintenance and structures of QLCSs. There have been relatively few direct measurements of cold pool variability, especially for the frequently severe QLCSs occurring during the cool season in the southeastern United States. Numerous surface and upper-air measurements from the recent Propagation, Evolution, and Rotation in Linear Storms (“PERiLS”) field experiment are used to document Southeastern QLCS cold pools. The PERiLS cold pools were surprisingly similar to, albeit somewhat weaker than, those found in prior studies of warm-season QLCSs in other regions. 

Abstract The Sierras de Córdoba (SDC) range in Argentina is a hotspot of deep moist convection initiation (CI). Radar climatology indicates that 44% of daytime CI events that occur near the SDC in spring and summer seasons and that are not associated with the passage of a cold front or an outflow boundary involve a northerly low-level jet (LLJ), and these events tend to preferentially occur over the southeast quadrant of the main ridge of the SDC. To investigate the physical mechanisms acting to cause CI, idealized convection-permitting numerical simulations with a horizontal grid spacing of 1 km were conducted using Cloud Model 1 (CM1). The sounding used for initializing the model featured a strong northerly LLJ, with synoptic conditions resembling those in a previously postulated conceptual model of CI over the region, making it a canonical case study. Differential heating of the mountain caused by solar insolation in conjunction with the low-level northerly flow sets up a convergence line on the eastern slopes of the SDC. The southern portion of this line experiences significant reduction in convective inhibition, and CI occurs over the SDC southeast quadrant. The simulated storm soon acquires supercellular characteristics, as observed. Additional simulations with varying LLJ strength also show CI over the southeast quadrant. A simulation without background flow generated convergence over the ridgeline, with widespread CI across the entire ridgeline. A simulation with mid- and upper-tropospheric westerlies removed indicates that CI is minimally influenced by gravity waves. We conclude that the low-level jet is sufficient to focus convection initiation over the southeast quadrant of the ridge. 

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (logNw), the mean volume diameter (D0), the mean mass diameter (Dm), the shape parameter (μ), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of logNw, lower D0, and higher μ, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of D0: the lower values of D0 were associated with higher values of logNw and were found more frequently during the afternoon, while the higher D0 were associated with lower logNw and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The logNw-D0 and LWC-D0 two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case.