Climatology of Mesoscale Airmasses with High-Theta-E
A component of a larger NSF-funded project led by Dr. Jason Keeler (Central Michigan University), this work involves the development of a climatology of Mesoscale Airmasses with High theta-E (MAHTEs). MAHTEs are unique because, despite cooler temperatures than the warm airmass on the opposite of the parent airmass boundary, CAPE is higher due to higher moisture content. Given their relatively small size and importance for deep convection forecasting, MAHTEs are an important forecast challenge. This research will utilize ASOS/AWOS data along with regional mesonets to characterize the spatiotemporal distribution of MAHTEs.
Project lead: Charles Kropiewnicki
Assessing Deep Convection Initiation in a Mountain-Valley System Using Unoccupied Aircraft System Observations
On 15 July 2018, UAS collected vertical profiles at fixed locations across the San Luis Valley during IOP1 of the LAPSE-RATE field campaign. (SSRG participation in LAPSE-RATE was supported by the NSF-funded CLOUD-MAP project.) The UAS observations revealed that the non-irrigated region had higher heat content and lower moisture content than the irrigated region, and that the differences often extended several hundred meters above the surface. These thermodynamic differences resulted in traditional DCI metrics, calculated from UAS-modified soundings, implying that the non-irrigated region was more favorable for DCI. DCI occurred first over the non-irrigated region, and all instances of DCI within the SLV occurred within an hour of the passage of a low-level convergence zone.
Project lead: Alex Erwin
Assimilating UAS, Radiosonde, and Mobile Mesonet Observations into a High-Resolution WRF Ensemble
This project focuses on assimilating targeted observations from TORUS into a storm-scale model to determine if they improve short-term ensemble forecasts. Severe storms are often affected by mesoscale environmental features on scales smaller than typically resolved by our operational observation network. Assimilating targeted observations collected in and around storms into storm-scale models may thus help improve model forecasts and representations of these features. In this experiment, several subsets of observations collected by TORUS in and around two supercells in NW KS on 8 June 2019 are assimilated into a storm-scale WRF ensemble to determine if this improves short-term forecasts of these storms. A data-denial framework is used to test the impact of assimilating observations from near the surface, within the boundary layer, and from the free atmosphere to see which subset of observations (if any) has the most impact on the resulting forecast quality. A detailed observational case study will also be conducted for the 8 June supercells to determine what environmental factors led to the first storm producing no mesocyclonic tornadoes and the second storm producing several.
Project lead: Matthew Wilson
Using "Big Data" to Better Understand Deep Convection Initiation
This project, supported by the NASA-funded WIND-MAP grant, will analyze over 10 years of deep convection initiation points over the central plains of the United States based on thunderstorm tracks identified by the Thunderstorm Observation by Radar (ThOR) algorithm. ThOR uses a combination of radar and National Lightning Detection Network (NLDN) data to identify and track individual thunderstorms. The initiation points of these thunderstorms will be analyzed both with regard to spatiotemporal occurrence as well as environmental characteristics using a combination of spatial statistical and machine learning methods. Expected results include a comprehensive understanding of the spatial and temporal variations in deep convection initiation across the central plains, as well as an improved understanding of environments which are favorable of deep convection initiation.
Project lead: Stephen Shield
Using Remote and In Situ Observations from TORUS to Investigate a Preexisting Airmass Boundary and its Influence on a Tornadic Supercell on 28 May 2019
The focus of this research is to perform an in-depth investigation of a preexisting boundary that potentially influenced the development and evolution of a tornadic supercell near Tipton, KS on 28 May 2019. In situ instruments (i.e. mobile mesonets and unoccupied aerial vehicles) operated duing TORUS observed higher moisture within the immediate cool side of the boundary resulting in higher equivalent potential temperature compared to the ambient environment. This type of airmass is known as a mesoscale airmass with high theta-E (MAHTE). Due to the enhanced moisture, the cool airmass became more unstable than the warm airmass and provided a localized environment supportive for supercells and tornadoes. Other platforms such as mobile sounding systems, LIDAR, and mobile research radars are being examined to fully understand the thermodynamic and kinematic characteristics of the boundary, including a dual-Doppler analysis, to better understand how the boundary may have contributed to the evolution of the tornadic supercell.
Project lead: Kristen Axon
Ensemble Weighting of Warn-on-Forecast System Guidance for Warning Decision Support
This project, supported by a NOAA VORTEX-SE grant, focuses on using ensemble sensitivity analysis (ESA) as a means of weighting the NSSL Warn-on-Forecast (WoF) ensemble system where significant spatiotemporal mesoscale heterogeneity was observed and may have influenced thunderstorm activity. Being computationally inexpensive, ESA is useful for identifying how features in the flow, such as mesoscale heterogeneities, might affect the relevant forecast features later in time. Since conventional weather observations between initializations of the ensemble often go unused, an ESA based ensemble weighting technique can be implemented, which provides a weighted ensemble that best represents those observations. In regions of high sensitivity, the ensemble members that offer the closest match to conventional and/or new observations (such as those collected as part of VORTEX-SE field campaigns) will be weighted greatest. Derived parameters such as convective available potential energy (CAPE), convective inhibition (CIN), and vertical wind shear will be used in this work as ESA perturbation variables, which has not been done in previous work using ESA.
Project lead: Daniel Butler
An Analysis of the Impact of Urban Heat Islands on Convection Initiation
This research focuses on the impact that Urban Heat Islands (UHIs) have on convection initiation. Using a database developed from the Thunderstorm Observation by Radar (ThOR) algorithm, I will be analyzing the spatial distribution of thunderstorm initiation points in the central United States between 2005 and 2015. Previous research has usually focused on precipitation anomalies or convective impacts of one city, but this wide ranging database will allow for an analysis of many UHIs at once. The variety of UHIs will also shed light on which factors may make a UHI more conducive to initiating thunderstorms.
Project lead: Ryan Martz
An Empirical Examination of the Environmental Factors That Regulated Thunderstorm Longevity on 22 May 2019 in Oklahoma
The goal of this work is to understand the environmental characteristics that resulted in the observed convective evolution during a case from the 2019 TORUS field campaign, particularly why more supercells were unable to develop from the deep convection that did form in what was a seemingly favorable environment (characterized by strong instability and a favorable vertical shear profile, leading to high Supercell Composite and Significant Tornado Parameter values). This favorable environment for intense supercells and tornadoes was in place across a large portion of Oklahoma on 22 May 2019, resulting in the issuance of a PDS Tornado Watch. While deep convection initiated in multiple locations throughout the watch polygon, the majority of the storms that formed were relatively short-lived and non-tornadic. This environment was well-sampled, as there were several radiosondes launched by TORUS , as well as by the NWS offices in Norman, OK and Springfield, MO, and by the ARM Southern Great Plains research facility in Billings, OK. Using the Thunderstorm Observation by Radar (ThOR; Houston et al. 2015) algorithm to track the deep convection that initiated across the domain, the vertical profiles from the inflow region of many of the convective cells that formed can be examined using both the observed soundings and the soundings generated by the 13-km Rapid Refresh (RAP) modeling system’s hourly analysis. By focusing on sounding-derived parameters which have been shown to be important to convective initiation and storm longevity (lift, inhibition, buoyancy, and dilution parameters), it is possible to gain insight into which of these characteristics helped best distinguish the near-storm environments that fostered intense convective development from those that did not.
Project lead: Kyle Pittman
Characteristics of Storm-Generated Convergence Boundaries in the Left and Forward Flanks of Supercells
The NSF/NOAA-funded TORUS project has been collecting observations of supercells for three years, with two of those years containing UAS observations of storm-generated boundaries within supercells. During the 2023 field campaign, a new format of TORUS, TORUS-LItE (left-flank intensive experiment), focused on sampling boundaries and structures identified in past modeling and observational studies including: left flank convergence boundaries, forward flank convergence boundaries, stream-wise vorticity currents, and left flank vertical vorticity sheets. This research utilizes simultaneous, stacked UAS and mobile mesonet observations of a variety of tornadic and non-tornadic supercells to better characterize the thermodynamic and kinematic conditions of these boundaries.
Project lead: Mark De Bruin
Assessing the Impact of Assimilating Observations from the TORUS Campaign on Forecasts of Severe Deep Convection
Due to the resolution of the observing system in the United States, mesoscale heterogeneities that influence severe deep convection may not be accurately depicted in the initial conditions of storm-scale numerical weather prediction. Field campaigns recording observations in the far-field, near-storm, and in-storm environments present an opportunity to test whether assimilating such observations can improve the representation of mesoscale phenomena in initial conditions. This study aims to address this issue by assimilating targeted observations near supercells from the Targeted Observations by Radar and UAS of Supercells (TORUS) campaign into a storm-scale model using a cycled EnKF framework, including data from mobile mesonets, UAS, and radiosondes. Data-denial experiments will be performed on the data across three sections of the atmosphere (surface, planetary boundary layer, and the free atmosphere) in addition to isolating UAS data to determine its impact. This work extends a previous study conducted to assimilate data from the 8 June 2019 TORUS IOP to include two additional TORUS cases from 2019, a case from 2022, and a case from 2023. These four cases will be examined due to data availability, specific features of such cases that were hypothesized to reveal the effectiveness of assimilating observations, and due to the fact that case study analyses have been or are being performed on each case. This study aims to both determine the impact of assimilating the TORUS observing system on convective representation in the storm-scale model and determine whether a specific atmospheric region of the observing system was most important in providing meaningful forecast improvements.
Project lead: Robert Szot
Using Cloud-to-Ground Lightning to Predict Tornadogenesis in Supercell Mergers
Supercell mergers are highly variable and dangerous events that can lead to a wide variety of outcomes, ranging from storm collapse to violent tornado production. There are many factors that can influence these events, including the updraft strength, environmental vorticity, and low-level shear, to name a few. Some of these factors- namely the updraft strength- are often manifested in cloud-to-ground lightning patterns and behavior. This research project aims to identify connections between these lightning properties and tornadogenesis in supercell merger events. This will provide forecasters with a new metric to predict these potentially destructive events and will expand the body of research on supercell mergers, as well as their weakly-understood lightning properties.
Project lead: Amy Conner
Advancing Understanding of Mesoscale Airmasses with High Theta-E through Airmass Boundary Classification
A Mesoscale Airmass with High Theta-E (MAHTE) is a narrow region on the cool side of an airmass boundary that possesses higher equivalent potential temperature (θe) than the warm side. MAHTEs are characteristic of higher CAPE, lower LCLs, and often higher 0-6 km bulk wind differential, making them potentially more favorable for severe convection and tornadoes. Previous work has cultivated a climatology of MAHTEs, however the type of boundary along which a MAHTE forms has not been formally analyzed. Thus, this work will analyze a subset of the climatology to classify the parent boundaries as synoptic, outflow, or lake/sea breeze.
Project lead: Anna James
A Spatiotemporal Distribution of Deep Convection Initiation in the Eastern United States
This project, funded by UCARE, will expand previous research within this group by creating a climatology of deep convection initiation in the eastern United States. Deep convection initiation points spanning an 11-year period will be found by using the Thunderstorm Observation by Radar (ThOR) algorithm, which uses radar and National Lightning Detection Network (NLDN) data to identify thunderstorm tracks. Unsupervised machine learning techniques will be applied to the initiation points to discover hotspots and coldspots for deep convection initiation in this region. The resulting spatiotemporal patterns will aid in exposing potential causes of deep convection initiation due to local influences.
Project lead: Kylee Matousek
The Impact of CIN Depth on Simulated Deep Convection Initiation
(Project Description)
Project lead: Adam Houston
Observing System Simulation Experiments to Investigate Impacts of UAS on Forecasts
(Project Description)
Project lead: Adam Houston