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Christopher A. Kerr, Louis J. Wicker, and Patrick S. Skinner

Abstract

The Warn-on-Forecast system (WoFS) provides short-term, probabilistic forecasts of severe convective hazards including tornadoes, hail, and damaging winds. WoFS initial conditions are created through frequent assimilation of radar (reflectivity and radial velocity), satellite, and in situ observations. From 2016 to 2018, 5-km radial velocity Cressman superob analyses were created to reduce the observation counts and subsequent assimilation computational costs. The superobbing procedure smooths the radial velocity and subsequently fails to accurately depict important storm-scale features such as mesocyclones. This study retrospectively assimilates denser, 3-km radial velocity analyses in lieu of the 5-km analyses for eight case studies during the spring of 2018. Although there are forecast improvements during and shortly after convection initiation, 3-km analyses negatively impact forecasts initialized when convection is ongoing, as evidenced by model failure and initiation of spurious convection. Therefore, two additional experiments are performed using adaptive assimilation of 3-km radial velocity observations. Initially, an updraft variance mask is applied that limits radial velocity assimilation to areas where the observations are more likely to be beneficial. This experiment reduces spurious convection as well as the number of observations assimilated, in some cases even below that of the 5-km analysis experiments. The masking, however, eliminates an advantage of 3-km radial velocity assimilation for convection initiation timing. This problem is mitigated by additionally assimilating 3-km radial velocity observations in locations where large differences exist between the observed and ensemble-mean reflectivity fields, which retains the benefits of the denser radial velocity analyses while reducing the number of observations assimilated.

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Patrick S. Skinner, Christopher C. Weiss, John L. Schroeder, Louis J. Wicker, and Michael I. Biggerstaff

Abstract

In situ data collected within a weakly tornadic, high-precipitation supercell occurring on 23 May 2007 near Perryton, Texas, are presented. Data were collected using a recently developed fleet of 22 durable, rapidly deployable probes dubbed “StickNet” as well as four mobile mesonet probes. Kinematic and thermodynamic observations of boundaries within the supercell are described in tandem with an analysis of data from the Shared Mobile Atmospheric Research and Teaching Radar.

Observations within the rear-flank downdraft of the storm exhibit large deficits of both virtual potential temperature and equivalent potential temperature, with a secondary rear-flank downdraft gust front trailing the mesocyclone. A primarily thermodynamic boundary resided across the forward-flank reflectivity gradient of the supercell. This boundary is characterized by small deficits in virtual potential temperature coupled with positive perturbations of equivalent potential temperature. The opposing thermodynamic perturbations appear to be representative of modified storm inflow, with a flux of water vapor responsible for the positive perturbations of the equivalent potential temperature. Air parcels exhibiting negative perturbations of virtual potential temperature and positive perturbations of equivalent potential temperature have the ability to be a source of both baroclinically generated streamwise horizontal vorticity and greater potential buoyancy if ingested by the low-level mesocyclone.

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Kelsey C. Britt, Patrick S. Skinner, Pamela L. Heinselman, and Kent H. Knopfmeier

Abstract

Cyclic mesocyclogenesis is the process by which a supercell produces multiple mesocyclones with similar life cycles. The frequency of cyclic mesocyclogenesis has been linked to tornado potential, with higher frequencies decreasing the potential for tornadogenesis. Thus, the ability to predict the presence and frequency of cycling in supercells may be beneficial to forecasters for assessing tornado potential. However, idealized simulations of cyclic mesocyclogenesis have found it to be highly sensitive to environmental and computational parameters. Thus, whether convective-allowing models can resolve and predict cycling has yet to be determined. This study tests the capability of a storm-scale, ensemble prediction system to resolve the cycling process and predict its frequency. Forecasts for three cyclic supercells occurring in May 2017 are generated by NSSL’s Warn-on-Forecast System (WoFS) using 3- and 1-km grid spacing. Rare cases of cyclic-like processes were identified at 3 km, but cycling occurred more frequently at 1 km. WoFS predicted variation in cycling frequencies for the storms that were similar to observed variations in frequency. Object-based identification of mesocyclones was used to extract environmental parameters from a storm-relative inflow sector from each mesocyclone. Lower magnitudes of 0–1-km storm-relative helicity and significant tornado parameter are present for the two more frequently cycling supercells, and higher values are present for the case with the fewest cycles. These results provide initial evidence that high-resolution ensemble forecasts can potentially provide useful guidance on the likelihood and cycling frequency of cyclic supercells.

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Patrick S. Skinner, Christopher C. Weiss, Louis J. Wicker, Corey K. Potvin, and David C. Dowell

Abstract

The forcing and origins of an internal rear-flank downdraft (RFD) momentum surge observed by the second Verification of the Origin of Rotation in Tornadoes Experiment (VORTEX2) within a supercell occurring near Dumas, Texas, on 18 May 2010 is assessed through ensemble Kalman filter (EnKF) storm-scale analyses. EnKF analyses are produced every 2 min from mobile Doppler velocity data collected by the Doppler on Wheels and Shared Mobile Atmospheric Research and Teaching radars, as well as radial velocity and reflectivity data from the KAMA (Amarillo, Texas) WSR-88D. EnKF analyses are found to reproduce the structure and evolution of an internal RFD momentum surge observed in independent mobile Doppler radar observations.

Pressure retrievals of EnKF analyses reveal that the low-level RFD outflow structure is primarily determined through nonlinear dynamic perturbation pressure gradient forcing. Horizontal acceleration into a trough of low perturbation pressure between the low-level mesocyclone and mesoanticyclone and trailing the primary RFD gust front is followed by an abrupt deceleration of air parcels crossing the trough axis. This deceleration and associated strong convergence downstream of the pressure trough and horizontal velocity maximum are indicative of an internal RFD momentum surge. Backward trajectory analyses reveal that air parcels within the RFD surge originate from two source regions: near the surface to the north of the low-level mesocyclone, and in the ambient flow outside of the storm environment at a height of approximately 2 km.

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John R. Lawson, Corey K. Potvin, Patrick S. Skinner, and Anthony E. Reinhart

Abstract

Tornadoes have Lorenzian predictability horizons O(10 min), and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1 km) length-scale of tornadoes and O(1 km) scale of mesocyclones, operational models running at horizontal grid-spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeast United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of three. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 km versus 1 km), for four low-CAPE, high-shear cases. Verification was grouped as (1) deterministic, traditional methods using point-wise evaluation; (2) a scale-aware probabilistic metric, and (3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead-time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.

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Patrick S. Skinner, Louis J. Wicker, Dustan M. Wheatley, and Kent H. Knopfmeier

Abstract

Two spatial verification methods are applied to ensemble forecasts of low-level rotation in supercells: a four-dimensional, object-based matching algorithm and the displacement and amplitude score (DAS) based on optical flow. Ensemble forecasts of low-level rotation produced using the National Severe Storms Laboratory (NSSL) Experimental Warn-on-Forecast System are verified against WSR-88D single-Doppler azimuthal wind shear values interpolated to the model grid. Verification techniques are demonstrated using four 60-min forecasts issued at 15-min intervals in the hour preceding development of the 20 May 2013 Moore, Oklahoma, tornado and compared to results from two additional forecasts of tornadic supercells occurring during the springs of 2013 and 2014.

The object-based verification technique and displacement component of DAS are found to reproduce subjectively determined forecast characteristics in successive forecasts for the 20 May 2013 event, as well as to discriminate in subjective forecast quality between different events. Ensemble-mean, object-based measures quantify spatial and temporal displacement, as well as storm motion biases in predicted low-level rotation in a manner consistent with subjective interpretation. Neither method produces useful measures of the intensity of low-level rotation, owing to deficiencies in the verification dataset and forecast resolution.

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Katie A. Wilson, Pamela L. Heinselman, Patrick S. Skinner, Jessica J. Choate, and Kim E. Klockow-McClain

Abstract

During the 2017 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, 62 meteorologists completed a survey designed to test their understanding of forecast uncertainty. Survey questions were based on probabilistic forecast guidance provided by the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e). A mix of 20 multiple-choice and open-ended questions required participants to explain basic probability and percentile concepts, extract information using graphical representations of uncertainty, and determine what type of weather scenario the graphics depicted. Multiple-choice questions were analyzed using frequency counts, and open-ended questions were analyzed using thematic coding methods. Of the 18 questions that could be scored, 60%–96% of the participants’ responses aligned with the researchers’ intended response. Some of the most challenging questions proved to be those requiring qualitative explanations, such as to explain what the 70th-percentile value of accumulated rainfall represents in an ensemble-based probabilistic forecast. Additionally, participants providing answers not aligning with the intended response oftentimes appeared to consider the given information with a deterministic rather than probabilistic mindset. Applications of a deterministic mindset resulted in tendencies to focus on the worst-case scenario and to modify understanding of probabilistic concepts when presented with different variables. The findings from this survey support the need for improved basic and applied training for the development, interpretation, and use of probabilistic ensemble forecast guidance. Future work should collect data for a larger sample size to examine the knowledge gaps across specific user groups and to guide development of probabilistic forecast training tools.

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Michael M. French, Patrick S. Skinner, Louis J. Wicker, and Howard B. Bluestein

Abstract

Unique observations of the interaction and likely merger of two cyclonic tornadoes are documented. One of the tornadoes involved in the interaction was the enhanced Fujita scale (EF5) El Reno–Piedmont, Oklahoma, tornado from 24 May 2011 and the other was a previously undocumented tornado. Data from three S-band radars: Twin Lakes, Oklahoma (KTLX); Norman, Oklahoma (KOUN); and the multifunction phased-array radar (MPAR), are used to detail the formation of the second tornado, which occurred to the northwest of the original tornado in an area of strong radial convergence. Radar data and isosurfaces of azimuthal shear provide evidence that both tornadoes formed within an elongated area of mesocyclone-scale cyclonic rotation. The path taken by the primary tornado and the formation location of the second tornado are different from previous observations of simultaneous cyclonic tornadoes, which have been most often observed in the cyclic tornadogenesis process. The merger of the two tornadoes occurred during the sampling period of a mobile phased-array radar—the Mobile Weather Radar, 2005 X-Band, Phased Array (MWR-05XP). MWR-05XP electronic scanning in elevation allowed for the merger process to be examined up to 4 km above radar level every 11 s. The tornadic vortex signatures (TVSs) associated with the tornadoes traveled around each other in a counterclockwise direction then merged in a helical manner up through storm midlevels. Upon merging, both the estimated intensity and size of the TVS associated with the resulting tornado increased dramatically. Similarities between the merger observed in this case and in previous cases also are discussed.

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Corey K. Potvin, Chris Broyles, Patrick S. Skinner, Harold E. Brooks, and Erik Rasmussen

Abstract

The Storm Prediction Center (SPC) tornado database, generated from NCEI’s Storm Data publication, is indispensable for assessing U.S. tornado risk and investigating tornado–climate connections. Maximizing the value of this database, however, requires accounting for systemically lower reported tornado counts in rural areas owing to a lack of observers. This study uses Bayesian hierarchical modeling to estimate tornado reporting rates and expected tornado counts over the central United States during 1975–2016. Our method addresses a serious solution nonuniqueness issue that may have affected previous studies. The adopted model explains 73% (>90%) of the variance in reported counts at scales of 50 km (>100 km). Population density explains more of the variance in reported tornado counts than other examined geographical covariates, including distance from nearest city, terrain ruggedness index, and road density. The model estimates that approximately 45% of tornadoes within the analysis domain were reported. The estimated tornado reporting rate decreases sharply away from population centers; for example, while >90% of tornadoes that occur within 5 km of a city with population > 100 000 are reported, this rate decreases to <70% at distances of 20–25 km. The method is directly extendable to other events subject to underreporting (e.g., severe hail and wind) and could be used to improve climate studies and tornado and other hazard models for forecasters, planners, and insurance/reinsurance companies, as well as for the development and verification of storm-scale prediction systems.

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Montgomery L. Flora, Corey K. Potvin, Patrick S. Skinner, Shawn Handler, and Amy McGovern

Abstract

A primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Post-processing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for post-processing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output.

Our dataset includes WoFS ensemble forecasts available every 5 minutes out to 150 min of lead time from the 2017-2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm track identification method, we extracted three sets of predictors from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based post-processing of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance.

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