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Brett Roberts and Ming Xue

Abstract

The idealized supercell simulations in a previous study by Roberts et al. are further analyzed to clarify the physical mechanisms leading to differences in mesocyclone intensification between an experiment with surface friction applied to the full wind (FWFRIC) and an experiment with friction applied to the environmental wind only (EnvFRIC). The low-level mesocyclone intensifies rapidly during the 3 min preceding tornadogenesis in FWFRIC, while the intensification during the same period is much weaker in EnvFRIC, which fails to produce a tornado. To quantify the mechanisms responsible for this discrepancy in mesocyclone evolution, material circuits enclosing the low-level mesocyclone are initialized and traced back in time, and circulation budgets for these circuits are analyzed. The results show that in FWFRIC, surface drag directly generates a substantial proportion of the final circulation around the mesocyclone, especially below 1 km AGL; in EnvFRIC, circulation budgets indicate the mesocyclone circulation is overwhelmingly barotropic. It is proposed that the import of near-ground, frictionally generated vorticity into the low-level mesocyclone in FWFRIC is a key factor causing the intensification and lowering of the mesocyclone toward the ground, creating a large upward vertical pressure gradient force that leads to tornadogenesis. Similar circulation analyses are also performed for circuits enclosing the tornado at its genesis stage. The frictionally generated circulation component is found to contribute more than half of the final circulation for circuits enclosing the tornado vortex below 400 m AGL, and the frictional contribution decreases monotonically with the height of the final circuit.

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Daniel T. Dawson II, Brett Roberts, and Ming Xue

Abstract

In idealized, horizontally homogeneous, cloud model simulations of convective storms, the action of surface friction can substantially modify the near-ground environmental wind profile over time owing to the lack of a large-scale pressure gradient force to balance the frictional force together with the Coriolis force. This situation is undesirable for many applications where the impact of an unchanging environmental low-level wind shear on the simulated storm behavior is the focus of investigation, as it introduces additional variability in the experiment and accordingly complicates interpretation of the results. Partly for this reason, many researchers have opted to perform simulations with free-slip lower boundary conditions, which with appropriate boundary conditions allows for more precise control of the large-scale environmental wind profile. Yet, some recent studies have advocated important roles of surface friction in storm dynamics. Here, a simple method is introduced to effectively maintain any chosen environmental wind profile in idealized storm simulations in the presence of surface friction and both resolved and subgrid-scale turbulent mixing. The method is demonstrated through comparisons of simulations of a tornadic supercell with and without surface friction and with or without invoking the new method. The method is compared with similar techniques in the literature and potential extensions and other applications are discussed.

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Brett Roberts, Ming Xue, and Daniel T. Dawson II

Abstract

A suite of six idealized supercell simulations is performed in which the surface drag coefficient C d is varied over a range of values from 0 to 0.05 to represent a variety of water and land surfaces. The experiments employ a new technique for enforcing a three-force balance among the pressure gradient, Coriolis, and frictional forces so that the environmental wind profile can remain unchanged throughout the simulation. The initial low-level mesocyclone lowers toward the ground, intensifies, and produces a tornado in all experiments with C d ≥ 0.002, with the intensification occurring earlier for larger C d. In the experiment with C d = 0, the low-level mesocyclone remains comparatively weak throughout the simulation and does not produce a tornado. Vertical cross sections through the simulated tornadoes reveal an axial downdraft that reaches the ground only in experiments with smaller C d, as well as stronger corner flow in experiments with larger C d. Material circuits are initialized enclosing the low-level mesocyclone in each experiment and traced backward in time. Circulation budgets for these circuits implicate surface drag acting in the inflow sector of the supercell as having generated important positive circulation, and its relative contribution increases with C d. However, the circulation generation is similar in magnitude for the experiments with C d = 0.02 and 0.05, and the tornado in the latter experiment is weaker. This suggests the possible existence of an optimal range of C d values for promoting intense tornadoes within our experimental configuration.

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Brett Roberts, Ming Xue, Alexander D. Schenkman, and Daniel T. Dawson II

Abstract

To investigate the effect of surface drag on tornadogenesis, a pair of idealized simulations is conducted with 50-m horizontal grid spacing. In the first experiment (full-wind drag case), surface drag is applied to the full wind; in the second experiment (environmental drag case), drag is applied only to the background environmental wind, with storm-induced perturbations unaffected. The simulations are initialized using a thermal bubble within a horizontally homogeneous background environment that has reached a balance between the pressure gradient, Coriolis, and frictional forces. The environmental sounding is derived from a prior simulation of the 3 May 1999 Oklahoma tornado outbreak but modified to account for near-ground frictional effects. In the full-wind drag experiment, a tornado develops around 25 min into the simulation and persists for more than 10 min; in the environmental-only drag experiment, no tornado occurs. Three distinct mechanisms are identified by which surface drag influences tornadogenesis. The first mechanism is the creation by drag of near-ground vertical wind shear (and associated horizontal vorticity) in the background environment. The second mechanism is generation of near-ground crosswise horizontal vorticity by drag on the storm scale as air accelerates into the low-level mesocyclone; this vorticity is subsequently exchanged into the streamwise direction and eventually tilted into the vertical. The third mechanism is frictional enhancement of horizontal convergence, which strengthens the low-level updraft and stretching of vertical vorticity. The second and third mechanisms are found to work together to produce a tornado, while baroclinic vorticity plays a negligible role.

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Brett Roberts, Israel L. Jirak, Adam J. Clark, Steven J. Weiss, and John S. Kain

Abstract

Since 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.

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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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Scott M. Steiger, Robert Schrom, Alfred Stamm, Daniel Ruth, Keith Jaszka, Timothy Kress, Brett Rathbun, Jeffrey Frame, Joshua Wurman, and Karen Kosiba

Abstract

The eastern Great Lakes (Erie and Ontario) are often affected by intense lake-effect snowfalls. Lake-effect storms that form parallel to the major axes of these lakes can strongly impact communities by depositing more than 100 cm of snowfall in less than 24 h. Long-lake-axis-parallel (LLAP) storms are significantly different in structure and dynamics compared to the much more studied wind-parallel roll storms that typically form over the western Great Lakes. A Doppler on Wheels (DOW) mobile radar sampled several of these storms at fine spatial and temporal resolutions (and close to the surface) during the winter of 2010–11 over and downwind of Lake Ontario to document and improve understanding of how these storms develop. Over 1100 observations of vortices were catalogued within the 16 December 2010 and 4–5 January 2011 events. The majority of these vortices were less than 1 km in diameter with a statistical modal difference in Doppler velocity (delta-V) value across the vortex of 11 m s−1. Vortices developed along boundaries, which formed within the bands, suggesting horizontal shear instability was the main cause. Other features noted in the DOW observations included bounded weak echo regions, anvils, and horizontal vortices, typically on the south side of west–east-oriented LLAP bands. The reflectivity and velocity structure of LLAP bands were found to be much more complex than previously thought, which may impact localized precipitation amounts and errors in forecast location/intensity.

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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, 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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Burkely T. Gallo, Jamie K. Wolff, Adam J. Clark, Israel Jirak, Lindsay R. Blank, Brett Roberts, Yunheng Wang, Chunxi Zhang, Ming Xue, Tim Supinie, Lucas Harris, Linjiong Zhou, and Curtis Alexander

Abstract

Verification methods for convection-allowing models (CAMs) should consider the finescale spatial and temporal detail provided by CAMs, and including both neighborhood and object-based methods can account for displaced features that may still provide useful information. This work explores both contingency table–based verification techniques and object-based verification techniques as they relate to forecasts of severe convection. Two key fields in severe weather forecasting are investigated: updraft helicity (UH) and simulated composite reflectivity. UH is used to generate severe weather probabilities called surrogate severe fields, which have two tunable parameters: the UH threshold and the smoothing level. Probabilities computed using the UH threshold and smoothing level that give the best area under the receiver operating curve result in very high probabilities, while optimizing the parameters based on the Brier score reliability component results in much lower probabilities. Subjective ratings from participants in the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE) provide a complementary evaluation source. This work compares the verification methodologies in the context of three CAMs using the Finite-Volume Cubed-Sphere Dynamical Core (FV3), which will be the foundation of the U.S. Unified Forecast System (UFS). Three agencies ran FV3-based CAMs during the five-week 2018 SFE. These FV3-based CAMs are verified alongside a current operational CAM, the High-Resolution Rapid Refresh version 3 (HRRRv3). The HRRR is planned to eventually use the FV3 dynamical core as part of the UFS; as such evaluations relative to current HRRR configurations are imperative to maintaining high forecast quality and informing future implementation decisions.

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