More Like this

1. Characteristics of Discrete Updrafts in Quasi-Linear Convective Systems, Environmental Influences, and Implications for Tornadogenesis Prediction

   https://doi.org/10.1175/WAF-D-25-0225.1  

   Wolff, Edward C; Trapp, Robert J; Nesbitt, Stephen W (July 2026, Weather and Forecasting)

   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. 

   more » « less
2. A Climatology of Quasi-Linear Convective Systems and Their Hazards in the United States

   https://doi.org/10.1175/WAF-D-19-0014.1  

   Ashley, Walker S.; Haberlie, Alex M.; Strohm, Jacob (December 2019, Weather and Forecasting)

   Abstract This research uses image classification and machine learning methods on radar reflectivity mosaics to segment, classify, and track quasi-linear convective systems (QLCSs) in the United States for a 22-yr period. An algorithm is trained and validated using radar-derived spatial and intensity information from thousands of manually labeled QLCS and non-QLCS event slices. The algorithm is then used to automate the identification and tracking of over 3000 QLCSs with high accuracy, affording the first, systematic, long-term climatology of QLCSs. Convective regions determined by the procedure to be QLCSs are used as foci for spatiotemporal filtering of observed severe thunderstorm reports; this permits an estimation of the number of severe storm hazards due to this morphology. Results reveal that nearly 32% of MCSs are classified as QLCSs. On average, 139 QLCSs occur annually, with most of these events clustered from April through August in the eastern Great Plains and central/lower Mississippi and Ohio River Valleys. QLCSs are responsible for a spatiotemporally variable proportion of severe hazard reports, with a maximum in QLCS-report attribution (30%–42%) in the western Ohio and central Mississippi River Valleys. Over 21% of tornadoes, 28% of severe winds, and 10% of severe hail reports are due to QLCSs across the central and eastern United States. The proportion of QLCS-affiliated tornado and severe wind reports maximize during the overnight and cool season, with more than 50% of tornadoes and wind reports in some locations due to QLCSs. This research illustrates the utility of automated storm-mode classification systems in generating extensive, systematic climatologies of phenomena, reducing the need for time-consuming and spatiotemporal-limiting methods where investigators manually assign morphological classifications. 

   more » « less
3. Machine Learning Estimation of Maximum Vertical Velocity from Radar

   https://doi.org/10.1175/AIES-D-23-0095.1  

   Chase, Randy J; McGovern, Amy; Homeyer, Cameron R; Marinescu, Peter J; Potvin, Corey K (April 2024, Artificial Intelligence for the Earth Systems)

   Abstract The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely, U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from three-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory’s convection permitting Warn-on-Forecast System (WoFS). A parametric regression technique using the sinh–arcsinh–normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65, and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the area of the 5 and 10 m s⁻¹updraft cores shows an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity, which could be useful in assessing a storm’s severe potential. Significance StatementAll convective storm hazards (tornadoes, hail, heavy rain, straight line winds) can be related to a storm’s updraft. Yet, there is no direct measurement of updraft speed or area available for forecasters to make their warning decisions from. This paper addresses the lack of observational data by providing a machine learning solution that skillfully estimates the maximum updraft speed within storms from only the radar reflectivity 3D structure. After further vetting the machine learning solutions on additional real-world examples, the estimated storm updrafts will hopefully provide forecasters with an added tool to help diagnose a storm’s hazard potential more accurately. 

   more » « less
4. Time Resolved Reflectivity Measurements of Convective Clouds

   https://doi.org/10.1029/2023GL105723  

   Dolan, Brenda; Kollias, Pavlos; van den Heever, Susan C.; Rasmussen, Kristen L.; Oue, Mariko; Luke, Edward; Lamer, Katia; Treserras, Bernat P.; Haddad, Ziad; Stephens, Graeme; et al (November 2023, Geophysical Research Letters)

   Abstract National Aeronautics and Space Administration's Investigations of Convective Updrafts (INCUS) mission aims to document convective mass flux through changes in the radar reflectivity (ΔZ) in convective cores captured by a constellation of three Ka‐band radars sampling the same convective cells over intervals of 30, 90, and 120 s. Here, high spatiotemporal resolution observations of convective cores from surface‐based radars that use agile sampling techniques are used to evaluate aspects of the INCUS measurement approach using real observations. Analysis of several convective cells confirms that large coherent ΔZstructure with measurable signal (>5 dB) can occur in less than 30 s and are correlated with underlying convective motions. The analysis indicates that the INCUS mission radar footprint and along track sampling are adequate to capture most of the desirable ΔZsignals. This unique demonstration of reflectivity time‐lapse provides the framework for estimating convective mass flux independent from Doppler techniques with future radar observations. 

   more » « less
5. Connections between ZDR Columns and Mesovortices in Quasi-Linear Convective Systems: Impacts of Low-Level Hodograph Curvature and Shear

   https://doi.org/10.1175/MWR-D-25-0103.1  

   Healey, Devon J; Van_Den_Broeke, Matthew S (December 2025, Monthly Weather Review)

   Recent work has shown that the differential reflectivity (ZDR) column may be useful in anticipating severe weather development in deep convective storms, though little prior work has examined polarimetric radar signatures such as the ZDR column within quasi-linear convective systems (QLCSs). Furthermore, the ZDR column is an updraft-associated feature, and updrafts (particularly low-level updrafts) are critical for mesovortex production within QLCSs. Since these mesovortices are often responsible for severe weather occurrences, we are motivated to examine the associations between ZDR columns and mesovortices within QLCSs. In six simulations of QLCSs with varying degrees of low-level hodograph curvature and size, larger ZDR columns were found in environments with greater low-level shear, yet there was not a systematic change in ZDR column depth or size with decreasing shear. Mesovortices in straight hodograph environments with large low-level shear often had a ZDR column signature immediately rearward of the mesovortex; however, in curved hodographs, there was little to no high ZDR manifestation aloft in the vicinity of the mesovortex. Furthermore, ZDR columns away from mesovortex locations were often more pronounced, suggesting that while ZDR columns may be associated with mesovortices in some environments, the deepest and highest magnitude ZDR columns may not always be in the location of mesovortex formation. 

   more » « less