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Burkely T. Gallo, Adam J. Clark, and Scott R. Dembek
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Burkely T. Gallo, Adam J. Clark, and Scott R. Dembek

Abstract

Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.

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Katie A. Wilson, Burkely T. Gallo, Patrick Skinner, Adam Clark, Pamela Heinselman, and Jessica J. Choate

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Convection-allowing model ensemble guidance, such as that provided by the Warn-on-Forecast System (WoFS), is designed to provide predictions of individual thunderstorm hazards within the next 0–6 h. The WoFS web viewer provides a large suite of storm and environmental attribute products, but the applicability of these products to the National Weather Service forecast process has not been objectively documented. Therefore, this study describes an experimental forecasting task designed to investigate what WoFS products forecasters accessed and how they accessed them for a total of 26 cases (comprising 13 weather events, each worked by two forecasters). Analysis of web access log data revealed that, in all 26 cases, product accesses were dominated in the reflectivity, rotation, hail, and surface wind categories. However, the number of different product types viewed and the number of transitions between products varied in each case. Therefore, the Levenshtein (edit distance) method was used to compute similarity scores across all 26 cases, which helped to identify what it meant for relatively similar versus dissimilar navigation of WoFS products. The Spearman’s rank correlation coefficient R results found that forecasters working the same weather event had higher similarity scores for events that produced more tornado reports and for events in which forecasters had higher performance scores. The findings from this study will influence subsequent efforts for further improving WoFS products and developing an efficient and effective user interface for operational applications.

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Burkely T. Gallo, Adam J. Clark, Bryan T. Smith, Richard L. Thompson, Israel Jirak, and Scott R. Dembek

Abstract

Attempts at probabilistic tornado forecasting using convection-allowing models (CAMs) have thus far used CAM attribute [e.g., hourly maximum 2–5-km updraft helicity (UH)] thresholds, treating them as binary events—either a grid point exceeds a given threshold or it does not. This study approaches these attributes probabilistically, using empirical observations of storm environment attributes and the subsequent climatological tornado occurrence frequency to assign a probability that a point will be within 40 km of a tornado, given the model-derived storm environment attributes. Combining empirical frequencies and forecast attributes produces better forecasts than solely using mid- or low-level UH, even if the UH is filtered using environmental parameter thresholds. Empirical tornado frequencies were derived using severe right-moving supercellular storms associated with a local storm report (LSR) of a tornado, severe wind, or severe hail for a given significant tornado parameter (STP) value from Storm Prediction Center (SPC) mesoanalysis grids in 2014–15. The NSSL–WRF ensemble produced the forecast STP values and simulated right-moving supercells, which were identified using a UH exceedance threshold. Model-derived probabilities are verified using tornado segment data from just right-moving supercells and from all tornadoes, as are the SPC-issued 0600 UTC tornado probabilities from the initial day 1 forecast valid 1200–1159 UTC the following day. The STP-based probabilistic forecasts perform comparably to SPC tornado probability forecasts in many skill metrics (e.g., reliability) and thus could be used as first-guess forecasts. Comparison with prior methodologies shows that probabilistic environmental information improves CAM-based tornado forecasts.

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Burkely T. Gallo, Adam J. Clark, Bryan T. Smith, Richard L. Thompson, Israel Jirak, and Scott R. Dembek

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.

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Brett Roberts, Burkely T. Gallo, Israel L. Jirak, Adam J. Clark, David C. Dowell, Xuguang Wang, and Yongming Wang

Abstract

The High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data Assimilation and Predictability (MAP) ensemble, two experimental CAM ensembles that ran during the 5-week Spring Forecasting Experiment (SFE) in spring 2018. The HRRRE and MAP are formally designed ensembles with spread achieved primarily through perturbed ICs. Verification in this study focuses on composite radar reflectivity and updraft helicity to assess ensemble performance in forecasting convective storms. The HREFv2.1 shows the highest overall skill for these forecasts, matching subjective real-time impressions from SFE participants. Analysis of the skill and variance of ensemble member forecasts suggests that the HREFv2.1 exhibits greater spread and more effectively samples model uncertainty than the HRRRE or MAP. These results imply that to optimize skill in forecasting convective storms at 1–2-day lead times, future CAM ensembles should employ either diverse membership designs or sophisticated perturbation schemes capable of representing model uncertainty with comparable efficacy.

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Burkely T. Gallo, Christina P. Kalb, John Halley Gotway, Henry H. Fisher, Brett Roberts, Israel L. Jirak, Adam J. Clark, Curtis Alexander, and Tara L. Jensen
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William J. S. Miller, Corey K. Potvin, Montgomery L. Flora, Burkely T. Gallo, Louis J. Wicker, Thomas A. Jones, Patrick S. Skinner, Brian C. Matilla, and Kent H. Knopfmeier

Abstract

The National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using 11 case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved midlevel mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic midlevel rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0–3-km storm-relative helicity.

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Burkely T. Gallo, Christina P. Kalb, John Halley Gotway, Henry H. Fisher, Brett Roberts, Israel L. Jirak, Adam J. Clark, Curtis Alexander, and Tara L. Jensen

Abstract

Evaluation of numerical weather prediction (NWP) is critical for both forecasters and researchers. Through such evaluation, forecasters can understand the strengths and weaknesses of NWP guidance, and researchers can work to improve NWP models. However, evaluating high-resolution convection-allowing models (CAMs) requires unique verification metrics tailored to high-resolution output, particularly when considering extreme events. Metrics used and fields evaluated often differ between verification studies, hindering the effort to broadly compare CAMs. The purpose of this article is to summarize the development and initial testing of a CAM-based scorecard, which is intended for broad use across research and operational communities and is similar to scorecards currently available within the enhanced Model Evaluation Tools package (METplus) for evaluating coarser models. Scorecards visualize many verification metrics and attributes simultaneously, providing a broad overview of model performance. A preliminary CAM scorecard was developed and tested during the 2018 Spring Forecasting Experiment using METplus, focused on metrics and attributes relevant to severe convective forecasting. The scorecard compared attributes specific to convection-allowing scales such as reflectivity and surrogate severe fields, using metrics like the critical success index (CSI) and fractions skill score (FSS). While this preliminary scorecard focuses on attributes relevant to severe convective storms, the scorecard framework allows for the inclusion of further metrics relevant to other applications. Development of a CAM scorecard allows for evidence-based decision-making regarding future operational CAM systems as the National Weather Service transitions to a Unified Forecast system as part of the Next-Generation Global Prediction System initiative.

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Burkely T. Gallo, Katie A. Wilson, Jessica Choate, Kent Knopfmeier, Patrick Skinner, Brett Roberts, Pamela Heinselman, Israel Jirak, and Adam J. Clark

Abstract

During the 2019 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, two NWS forecasters issued experimental probabilistic forecasts of hail, tornadoes, and severe convective wind using NSSL’s Warn-on-Forecast System (WoFS). The aim was to explore forecast skill in the time frame between severe convective watches and severe convective warnings during the peak of the spring convective season. Hourly forecasts issued during 2100–0000 UTC, valid from 0100 to 0200 UTC demonstrate how forecasts change with decreasing lead time. Across all 13 cases in this study, the descriptive outlook statistics (e.g., mean outlook area, number of contours) change slightly and the measures of outlook skill (e.g., fractions skill score, reliability) improve incrementally with decreasing lead time. WoFS updraft helicity (UH) probabilities also improve slightly and less consistently with decreasing lead time, though both the WoFS and the forecasters generated skillful forecasts throughout. Larger skill differences with lead time emerge on a case-by-case basis, illustrating cases where forecasters consistently improved upon WoFS guidance, cases where the guidance and the forecasters recognized small-scale features as lead time decreased, and cases where the forecasters issued small areas of high probabilities using guidance and observations. While forecasts generally “honed in” on the reports with slightly smaller contours and higher probabilities, increased confidence could include higher certainty that severe weather would not occur (e.g., lower probabilities). Long-range (1–5 h) WoFS UH probabilities were skillful, and where the guidance erred, forecasters could adjust for those errors and increase their forecasts’ skill as lead time decreased.

Significance Statement

Forecasts are often assumed to improve as an event approaches and uncertainties resolve. This work examines the evolution of experimental forecasts valid over one hour with decreasing lead time issued using the Warn-on-Forecast System (WoFS). Because of its rapidly updating ensemble data assimilation, WoFS can help forecasters understand how thunderstorm hazards may evolve in the next 0–6 h. We found slight improvements in forecast and WoFS performance as a function of lead time over the full experiment; the first forecasts issued and the initial WoFS guidance performed well at long lead times, and good performance continued as the event approached. However, individual cases varied and forecasters frequently combined raw model output with observed mesoscale features to provide skillful small-scale forecasts.

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