Updraft Helicity Research Articles

Moisture Attribution and Sensitivity Analysis of a Winter Tornado Outbreak

AbstractThe tornado outbreak of 21–23 January 2017 caused 20 fatalities, more than 200 injuries, and over a billion dollars in damage in the Southeast United States. The event occurred concurrently with a record-breaking warm Gulf of Mexico (GoM) basin. This article explores the influence that warm GoM sea surface temperatures (SSTs) had on the tornado outbreak. Backward trajectory analysis, combined with a Lagrangian-based moisture-attribution algorithm, reveals that the tornado outbreak’s moisture predominantly originated from the southeast GoM and the northwest Caribbean Sea. We used the WRF Model to generate a control simulation of the event and explore the response to perturbed SSTs. With the aid of a tornadic storm proxy derived from updraft helicity, we show that the 21–23 January 2017 tornado outbreak exhibits sensitivity to upstream SSTs during the first day of the event. Warmer SSTs across remote moisture sources and adjacent waters increase tornado frequency, in contrast to cooler SSTs, which reduce tornado activity. Upstream SST sensitivity is reduced once convection is ongoing and modifying local moisture and instability availability. Our results highlight the importance of air–sea interactions before airmass advection toward the continental United States. The complex and nonlinear nature of the relationship between upstream SSTs and local precursor environments is also discussed.

Are Supercells Resistant to Entrainment because of Their Rotation?

AbstractThis research investigates a hypothesis posed by previous authors, which argues that the helical nature of the flow in supercell updrafts makes them more resistant to entrainment than nonsupercellular updrafts because of the suppressed turbulence in purely helical flows. It was further supposed that this entrainment resistance contributes to the steadiness and longevity of supercell updrafts. A series of idealized large-eddy simulations were run to address this idea, wherein the deep-layer shear and hodograph shape were varied, resulting in supercells in the strongly sheared runs, nonsupercells in the weakly sheared runs, and variations in the percentage of streamwise vorticity in updrafts among runs. Fourier energy spectrum analyses show well-developed inertial subranges in all simulations, which suggests that the percentages of streamwise and crosswise vorticity have little effect on turbulence in convective environments. Additional analyses find little evidence of updraft-scale centrifugally stable flow within updrafts, which has also been hypothesized to limit horizontal mass flux across supercell updrafts. Results suggest that supercells do have smaller fractional entrainment rates than nonsupercells, but these differences are consistent with theoretical dependencies of entrainment on updraft width, and with supercells being wider than nonsupercells. Thus, while supercells do experience reduced fractional entrainment rates and entrainment-driven dilution, this advantage is primarily attributable to increased supercell updraft width relative to ordinary convection, and has little to do with updraft helicity and rotation.

Hindcasting the First Tornado Forecast in Europe: 25 June 1967

Abstract The tornado outbreak of 24–25 June 1967 was the most damaging in the history of western Europe, producing 7 F2–F5 tornadoes, 232 injuries, and 15 fatalities across France, Belgium, and the Netherlands. Following tornadoes in France on 24 June, the Royal Netherlands Meteorological Institute (KNMI) issued a tornado forecast for 25 June, which became the first ever—and first verified—tornado forecast in Europe. Fifty-two years later, tornadoes are still not usually forecast by most European national meteorological services, and a pan-European counterpart to the NOAA/NWS/Storm Prediction Center (SPC) does not exist to provide convective outlook guidance; yet, tornadoes remain an extant threat. This article asks, “What would a modern-day forecast of the 24–25 June 1967 outbreak look like?” To answer this question, a model simulation of the event is used in three ways: 20-km grid-spacing output to produce a SPC-style convective outlook provided by the European Storm Forecast Experiment (ESTOFEX), 800-m grid-spacing output to analyze simulated reflectivity and surface winds in a nowcasting analog, and 800-m grid-spacing output to produce storm-total footprints of updraft helicity maxima to compare to observed tornado tracks. The model simulates a large supercell on 24 June and weaker embedded mesocyclones on 25 June forming along a stationary front, allowing the ESTOFEX outlooks to correctly identify the threat. Updraft helicity footprints indicate multiple mesocyclones on both days within 40–50 km and 3–4 h of observed tornado tracks, demonstrating the ability to hindcast a large European tornado outbreak.

Ensemble-Based Targeted Observation Method Applied to Radar Radial Velocity Observations on Idealized Supercell Low-Level Rotation Forecasts: A Proof of Concept

Abstract The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are “collected” and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy.

Optimal Temporal Frequency of NSSL Phased Array Radar Observations for an Experimental Warn-on-Forecast System

Abstract A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.

A multiscale analysis of the tornadoes of 30–31 May 2019 in south-central Chile

Abstract On 30 and 31 May 2019, tornadoes occurred in the cities of Los Angeles and Talcahuano/Concepcion in south-central Chile, in a region where tornado activity is not common. The main goal of this study was to analyze these tornadoes across multiple scales: synoptic, mesoscale, and subseasonal. On the synoptic scale, the tornadoes were associated with an anomalous 500-hPa trough and associated surface cyclone to the west of Chile. A strong (20+ m s−1) low-level jet accompanied this trough, potentially enhanced by flow blocking by the Andes. A relatively warm and saturated surface layer combined with cold upper-level temperatures in the trough to yield 200–500 J kg−1 of CAPE on both days. This CAPE was accompanied by high levels of both deep-layer and low-level shear. Storm motions inferred by lightning swaths and GOES-IR imagery, along with estimates of storm motion and updraft helicity from a high-resolution WRF simulation, suggested this CAPE-shear combination was sufficient for the tornadic thunderstorms to be supercells. Finally, anomalies of sea level pressure, 500-hPa height, and surface dew point temperature from 27 to 31 May 2019 resembled long-term composite anomalies for MJO phases 1 and 2, suggesting a subseasonal link between the extreme event in Chile and convection in the tropics.

Test of a Weather-Adaptive Dual-Resolution Hybrid Warn-on-Forecast Analysis and Forecast System for Several Severe Weather Events

Abstract A real-time, weather adaptive, dual-resolution, hybrid Warn-on-Forecast (WoF) analysis and forecast system using the WRF-ARW forecast model has been developed and implemented. The system includes two components, an ensemble analysis and forecast component, and a deterministic hybrid three-dimensional ensemble–variational (3DEnVAR) analysis and forecast component. The goal of the system is to provide on-demand, ensemble-based, and physically consistent gridded analysis and forecast products to forecasters for making warning decisions. Both components, the WRF-DART system with 36 ensemble members and the hybrid 3DEnVAR system, assimilate radar data, satellite-retrieved cloud water path, and surface observations at 15-min intervals with dual-resolution capability. In the current hybrid configuration, one-way coupling of the two analysis systems is performed: ensemble covariances derived from the WRF-DART system are incorporated into the hybrid 3DEnVAR system with each data assimilation (DA) cycle. This study examines deterministic, 3-h forecasts launched from the hybrid 3DEnVAR analyses every 30 min for three severe weather events in 2017. The performance of the deterministic component is evaluated for four configurations: dual-resolution coupling, single-resolution coupling, forecasts initialized using a cloud analysis for reflectivity assimilation, and forecasts initialized from the WRF-DART ensemble mean. Quantitative and subjective evaluation of composite reflectivity and updraft helicity (UH) swath forecasts for the three events indicate that the dual-resolution strategy without the cloud analysis performs best among the four configurations and provides the most realistic prediction of reflectivity patterns and UH tracks.

Object-Based Verification of Short-Term, Storm-Scale Probabilistic Mesocyclone Guidance from an Experimental Warn-on-Forecast System

Abstract An object-based verification method for short-term, storm-scale probabilistic forecasts was developed and applied to mesocyclone guidance produced by the experimental Warn-on-Forecast System (WoFS) in 63 cases from 2017 to 2018. The probabilistic mesocyclone guidance was generated by calculating gridscale ensemble probabilities from WoFS forecasts of updraft helicity (UH) in layers 2–5 km (midlevel) and 0–2 km (low-level) above ground level (AGL) aggregated over 60-min periods. The resulting ensemble probability swaths are associated with individual thunderstorms and treated as objects with a single, representative probability value prescribed. A mesocyclone probability object, conceptually, is a region bounded by the ensemble forecast envelope of a mesocyclone track for a given thunderstorm over 1 h. The mesocyclone probability objects were matched against rotation track objects in Multi-Radar Multi-Sensor data using the total interest score, but with the maximum displacement varied between 0, 9, 15, and 30 km. Forecast accuracy and reliability were assessed at four different forecast lead time periods: 0–60, 30–90, 60–120, and 90–150 min. In the 0–60-min forecast period, the low-level UH probabilistic forecasts had a POD, FAR, and CSI of 0.46, 0.45, and 0.31, respectively, with a probability threshold of 22.2% (the threshold of maximum CSI). In the 90–150-min forecast period, the POD and CSI dropped to 0.39 and 0.27 while FAR remained relatively unchanged. Forecast probabilities > 60% overpredicted the likelihood of observed mesocyclones in the 0–60-min period; however, reliability improved when allowing larger maximum displacements for object matching and at longer lead times.

Forecasting the 8 May 2017 Severe Hail Storm in Denver, Colorado, at a Convection-Allowing Resolution: Understanding Rimed Ice Treatments in Multimoment Microphysics Schemes and Their Effects on Hail Size Forecasts

AbstractDay-ahead (20–22 h) 3-km grid spacing convection-allowing model forecasts are performed for a severe hail event that occurred in Denver, Colorado, on 8 May 2017 using six different multimoment microphysics (MP) schemes including: the Milbrandt–Yau double-moment (MY2), Thompson (THO), NSSL double-moment (NSSL), Morrison double-moment graupel (MOR-G) and hail (MOR-H), and Predicted Particle Properties (P3) schemes. Hail size forecasts diagnosed using the Thompson hail algorithm and storm surrogates predict hail coverage. For this case hail forecasts predict the coverage of hail with a high level of skill but underpredict hail size. The storm surrogate updraft helicity predicts the coverage of severe hail with the most skill for this case. Model data are analyzed to assess the effects of microphysical treatments related to rimed ice. THO uses diagnostic equations to increase the size of graupel within the hail core. MOR-G and MOR-H predict small rimed ice aloft; excessive size sorting and increased fall speeds cause MOR-H to predict more and larger surface hail than MOR-G. The MY2 and NSSL schemes predict large, dense rimed ice particles because both schemes predict separate hail and graupel categories. The NSSL scheme predicts relatively little hail for this case; however, the hail size forecast qualitatively improves when the maximum size of both hail and graupel is considered. The single ice category P3 scheme only predicts dense hail near the surface while above the melting layer large concentrations of low-density ice dominate.

Assimilation of Radar Radial Velocity, Reflectivity, and Pseudo–Water Vapor for Convective-Scale NWP in a Variational Framework

Abstract To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis.

PostProcessing and Visualization Techniques for Convection-Allowing Ensembles

AbstractSince the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.

Processes Associated with Convection Initiation in the North American Mesoscale Forecast System, Version 3 (NAMv3)

Abstract In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.

Explicit Prediction of Continental Convection in a Skillful Variable‐Resolution Global Model

AbstractWe present a new global‐to‐regional model, cfvGFS, able to explicitly (without parameterization) represent convection over part of the Earth. This model couples the Geophysical Fluid Dynamics Laboratory Finite‐Volume Cubed‐Sphere Dynamical Core (FV3) to the Global Forecast System physics and initial conditions, augmented with a six‐category microphysics and a modified planetary boundary layer scheme. We examine the characteristics of cfvGFS on a 3‐km continental U. S. domain nested within a 13‐km global model. The nested cfvGFS still has good hemispheric skill comparable to or better than the operational Global Forecast System, while supercell thunderstorms, squall lines, and derechos are explicitly represented over the refined region. In particular, cfvGFS has excellent representations of fine‐scale updraft helicity fields, an important proxy for severe weather forecasting. Precipitation biases are found to be smaller than in uniform‐resolution global models and competitive with operational regional models; the 3‐km domain also improves upon the global models in 2‐m temperature and humidity skill. We discuss further development of cfvGFS and the prospects for a unified global‐to‐regional prediction system.

Tornado-Resolving Ensemble and Probabilistic Predictions of the 20 May 2013 Newcastle–Moore EF5 Tornado

AbstractAn ensemble of 10 forecasts is produced for the 20 May 2013 Newcastle–Moore EF5 tornado and its parent supercell using a horizontal grid spacing of 50 m, nested within ensemble forecasts with 500-m horizontal grid spacing initialized via ensemble Kalman filter data assimilation of surface and radar observations. Tornadic circulations are predicted in all members, though the intensity, track, and longevity of the predicted tornado vary substantially among members. Overall, tornadoes in the ensemble forecasts persisted longer and moved to the northeast faster than the observed tornado. In total, 8 of the 10 ensemble members produce tornadoes with winds corresponding to EF2 intensity or greater, with maximum instantaneous near-surface horizontal wind speeds of up to 130 m s−1 and pressure drops of up to 120 hPa; values similar to those reported in observational studies of intense tornadoes. The predicted intense tornadoes all acquire well-defined two-cell vortex structure, and exhibit features common in observed tornadic storms, including a weak-echo notch and low reflectivity within the mesocyclone. Ensemble-based probabilistic tornado forecasts based upon near-surface wind and/or vorticity fields at 10 m above the surface produce skillful forecasts of the tornado in terms of area under the relative operating characteristic curve, with probability swaths extending along and to the northeast of the observed tornado path. When probabilistic swaths of 0–3- and 2–5-km updraft helicity are compared to the swath of wind at 10 m above the surface exceeding 29 m s−1, a slight northwestward bias is present, although the pathlength, orientation, and the placement of minima and maxima show very strong agreement.

Incorporating UH Occurrence Time to Ensemble-Derived Tornado Probabilities

Abstract Probabilistic ensemble-derived tornado forecasts generated from convection-allowing models often use hourly maximum updraft helicity (UH) alone or in combination with environmental parameters as a proxy for right-moving (RM) supercells. However, when UH occurrence is a condition for tornado probability generation, false alarm areas can occur from UH swaths associated with nocturnal mesoscale convective systems, which climatologically produce fewer tornadoes than RM supercells. This study incorporates UH timing information with the forecast near-storm significant tornado parameter (STP) to calibrate the forecast tornado probability. To generate the probabilistic forecasts, three sets of observed climatological tornado frequencies given an RM supercell and STP value are incorporated with the model output, two of which use UH timing information. One method uses the observed climatological tornado frequency for a given 3-h window to generate the probabilities. Another normalizes the observed climatological tornado frequency by the number of hail, wind, and tornado reports observed in that 3-h window compared to the maximum number of reports in any 3-h window. The final method is independent of when UH occurs and uses the observed climatological tornado frequency encompassing all hours. The normalized probabilities reduce the false alarm area compared to the other methods but have a smaller area under the ROC curve and require a much higher percentile of the STP distribution to be used in probability generation to become reliable. Case studies demonstrate that the normalized probabilities highlight the most likely area for evening RM supercellular tornadoes, decreasing the nocturnal false alarm by assuming a linear convective mode.

Diagnosing Convective Dependencies on Near-Storm Environments Using Ensemble Sensitivity Analyses

AbstractConvection intensity and longevity is highly dependent on the surrounding environment. Ensemble sensitivity analysis (ESA), which quantitatively and qualitatively interprets impacts of initial conditions on forecasts, is applied to very short-term (1–2 h) convective-scale forecasts for three cases during the Mesoscale Predictability Experiment (MPEX) in 2013. The ESA technique reveals several dependencies of individual convective storm evolution on their nearby environments. The three MPEX cases are simulated using a previously verified 36-member convection-allowing model (Δx = 3 km) ensemble created via the Weather Research and Forecasting (WRF) Model. Radar and other conventional observations are assimilated using an ensemble adjustment Kalman filter. The three cases include a mesoscale convective system (MCS) and both nontornadic and tornadic supercells. Of the many ESAs applied in this study, one of the most notable is the positive sensitivity of supercell updraft helicity to increases in both storm inflow region deep and shallow vertical wind shear. This result suggests that larger values of vertical wind shear within the storm inflow yield higher values of storm updraft helicity. Results further show that the supercell storms quickly enhance the environmental vertical wind shear within the storm inflow region. Application of ESA shows that these storm-induced perturbations then affect further storm evolution, suggesting the presence of storm–environment feedback cycles where perturbations affect future mesocyclone strength. Overall, ESA can provide insight into convection dependencies on the near-storm environment.

High-resolution simulation of an isolated tornadic supercell in Poland on 20 June 2016

On 20 June 2016, a convective cell, which developed on the Polish-Slovakian border, under favorable kinematic and thermodynamic conditions, evolved into a supercell. It moved for 500 km, heading northeast, and remained isolated during its entire lifecycle. The storm was responsible for producing multiple large hail (up to 7.5 cm) and severe wind gust events. At around 1700 UTC (+/− 5 min), a tornado was reported near Sulów, and after moving for 21 km it dissipated near Skawinek (Lubelskie Voivodeship). Based on the damage survey, the peak intensity of the tornado was estimated at F1/T3 in the Fujita/TORRO scale. In this paper, we perform two simulations using the WRF model to evaluate the possibilities of predicting this event. The first simulation is a WRF base run, while the second version uses well established data assimilation techniques applied to the model domain with high spatial resolution. The surface observational data, together with the atmospheric soundings, are assimilated in the second model run. Three one-way nested domains were defined, with spatial resolutions of 9 km, 3 km and 1 km. Observations from meteorological stations, severe weather reports, and radar reflectivity from the POLRAD network are compared with the simulations to assess their quality. An additional parameter of an updraft helicity track is used to analyze the lifecycle of a simulated supercell. Results indicate a slight overestimation of temperature, dew point and wind speed. Our findings confirm that downscaling to convection-permitting resolution makes it possible to successfully predict tornadic supercells. Data assimilation, applied to the second model run, improved the model's performance. The model's errors, calculated by comparing the results with meteorological observation, were reduced if compared to the base run. Additionally, the number of convective cells and storm tracks was appropriate.

Object-Based Verification of a Prototype Warn-on-Forecast System

An object-based verification methodology for the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) has been developed and applied to 32 cases between December 2015 and June 2017. NEWS-e forecast objects of composite reflectivity and 30-minute rotation tracks of updraft helicity are matched to corresponding objects in Multi-Radar Multi-Sensor data on space and time scales typical of a National Weather Service warning. Object matching allows contingency table-based verification statistics to be used to establish baseline performance metrics for NEWS-e thunderstorm and mesocyclone forecasts. NEWS-e critical Success Index (CSI) scores of reflectivity (updraft helicity) forecasts decrease from approximately 0.7 (0.4) to 0.4 (0.2) over 3 hours of forecast time. CSI scores decrease through the forecast period, indicating that errors have not saturated and skill is retained at 3 hours of forecast time. Lower verification scores for rotation track forecasts are primarily a result of a high frequency bias. Comparison of different system configurations used in 2016 and 2017 show an increase in skill for 2017 reflectivity forecasts, attributable mainly to improvements in the forecast initial condition. A small decrease in skill in 2017 rotation track forecasts is likely a result of sample differences between 2016 and 2017. Although large case-to-case variation is present, evidence is found that NEWS-e forecast skill improves with increasing object size and intensity, as well as in mesoscale environments in which an enhanced or higher risk of severe thunderstorms was forecast.

Influence of a Great Plains Urban Environment on a Simulated Supercell

Abstract The effect of urban areas on weakly forced precipitation systems has been studied extensively. However, interactions between urban areas and strongly forced convection, such as supercells, remain relatively unexamined. The present study uses simulations of a supercell to quantify the impacts of a large plains urban area on the evolution and strength of a supercell. An initial ensemble of simulations (CTRLE) of a supercell over homogeneous land use is performed using the WRF-ARW Model. Additionally, 108 simulations are conducted in which the land-use pattern of Dallas–Ft. Worth, Texas, is placed inside the model domain, with the city center shifted to be in or near the path of the supercell. Simulations with urban areas are compared to CTRLE, with the aid of hierarchical clustering analysis to form statistically similar groups of simulations. Clustering analyses identify groups of ensemble members with closely located urban areas that have similar patterns of 0–1-km updraft helicity and near-surface minimum temperature and maximum wind speeds. Comparison of these groups of ensemble members to CTRLE suggests the urban area has a significant impact on storm characteristics, particularly on low-level rotation and mesocyclone track. Simulations where the storm passes to the north of or directly over the city center late in its life cycle deviate most significantly from CTRLE. In these members, low-and midlevel mesocyclone strength increases, and the mesocyclone tracks farther south when compared to CTRLE.

Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 μm) Radiances into a Warn-on-Forecast System

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution.