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1. How Does the Relationship between Ambient Deep-Tropospheric Vertical Wind Shear and Tropical Cyclone Tornadoes Change between Coastal and Inland Environments?

   https://doi.org/10.1175/WAF-D-20-0127.1  

   Schenkel, Benjamin A.; Coniglio, Michael; Edwards, Roger (April 2021, Weather and Forecasting)

   Abstract This work investigates how the relationship between tropical cyclone (TC) tornadoes and ambient (i.e., synoptic-scale) deep-tropospheric (i.e., 850–200-hPa) vertical wind shear (VWS) varies between coastal and inland environments. Observed U.S. TC tornado track data are used to study tornado frequency and location, while dropsonde and radiosonde data are used to analyze convective-scale environments. To study the variability in the TC tornado–VWS relationship, these data are categorized by both 1) their distance from the coast and 2) reanalysis-derived VWS magnitude. The analysis shows that TCs produce coastal tornadoes regardless of VWS magnitude primarily in their downshear sector, with tornadoes most frequently occurring in strongly sheared cases. Inland tornadoes, including the most damaging cases, primarily occur in strongly sheared TCs within the outer radii of the downshear-right quadrant. Consistent with these patterns, dropsondes and coastal radiosondes show that the downshear-right quadrant of strongly sheared TCs has the most favorable combination of enhanced lower-tropospheric near-surface speed shear and veering, and reduced lower-tropospheric thermodynamic stability for tornadic supercells. Despite the weaker intensity farther inland, these kinematic conditions are even more favorable in inland environments within the downshear-right quadrant of strongly sheared TCs, due to the strengthened veering of the ambient winds and the lack of changes in the TC outer tangential wind field strength. The constructive superposition of the ambient and TC winds may be particularly important to inland tornado occurrence. Together, these results will allow forecasters to anticipate how the frequency and location of tornadoes and, more broadly, convection may change as TCs move inland. 

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2. Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation Using Object-Based Metrics

   https://doi.org/10.1175/WAF-D-22-0223.1  

   Stackhouse, Shakira D.; Zick, Stephanie E.; Matyas, Corene J.; Wood, Kimberly M.; Hazelton, Andrew T.; Alaka, Ghassan J. (October 2023, Weather and Forecasting)

   Abstract Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models. 

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3. Doppler Lidar and Mobile Radiosonde Observation-Based Evaluation of Warn-on-Forecast System Predicted Near-Supercell Environments during TORUS 2019

   https://doi.org/10.1175/WAF-D-21-0190.1  

   Laser, Jordan J.; Coniglio, Michael C.; Skinner, Patrick S.; Smith, Elizabeth N. (October 2022, Weather and Forecasting)

   Abstract Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm. Significance Statement It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms. 

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4. The NCAR GPS Dropwindsonde and Its Impact on Hurricane Operations and Research

   https://doi.org/10.1175/BAMS-D-22-0119.1  

   Aberson, Sim D; Zhang, Jun A; Zawislak, Jonathan; Sellwood, Kathryn; Rogers, Robert; Cione, Joseph J (November 2023, Bulletin of the American Meteorological Society)

   Abstract The global positioning system dropwindsonde has provided thousands of high-resolution kinematic and thermodynamic soundings in and around tropical cyclones (TCs) since 1997. These data have revolutionized the understanding of TC structure, improved forecasts, and validated observations from remote sensing platforms. About 400 peer-reviewed studies on TCs using these data have been published to date. This paper reviews the history of dropwindsonde observations, changes to dropwindsonde technology since it was first used in TCs in 1982, and how the data have improved forecasting and changed our understanding of TCs. 

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5. Hodographs and Skew Ts of Hail-Producing Storms

   https://doi.org/10.1175/WAF-D-23-0031.1  

   Nixon, Cameron J.; Allen, John T.; Taszarek, Mateusz (November 2023, Weather and Forecasting)

   Abstract Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself. 

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