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Andrew R. Dean and Brian H. Fiedler

Abstract

In this study, both linear regression and a nonlinear neural network are used to forecast burnoff of low clouds in the warm season at San Francisco International Airport (SFO). Both forecast systems show skill scores between 0.2 and 0.25 in comparison with use of climatological values. The neural network is slightly more skillful. The forecast systems are derived from 45 yr of NCEP–NCAR reanalysis data and SFO surface observations. A forecast is attempted for both the time of burnoff and the probability of being burned off by 1000 Pacific standard time. The lack of significant superiority of the neural network over linear regression is not due to a failing of the neural network as a method. When both methods are applied to a statistical prediction of the afternoon temperature at SFO, based on early morning conditions, the neural network has a skill score of 0.446 and the linear regression has a skill score of 0.290.

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William E. Togstad, Jonathan M. Davies, Sarah J. Corfidi, David R. Bright, and Andrew R. Dean

Abstract

Recent literature has identified several supercell/tornado forecast parameters in common use that are operationally beneficial in assessing environments supportive of supercell tornadoes. These parameters are utilized in the computation of tornado forecast guidance such as the significant tornado parameter (STP), a dimensionless parameter developed at the Storm Prediction Center (SPC) that applies a subjectively chosen scale. The goal of this research is to determine if useful logistic regression equations can be developed to estimate the conditional probability of supercell tornadoes that are categorized as level 2 or stronger on the enhanced Fujita scale (EF) when a similar set of environmental background parameters is applied as variables. A large database of Rapid Update Cycle (RUC) analysis soundings in proximity to a representative sample of tornadic and nontornadic supercells over the central and eastern United States, a number of which were associated with EF2 or stronger tornadoes, was used to compute supercell tornado forecast parameters similar to those in the original version of STP. Three logistic regression equations were developed from this database, two of which are described and analyzed in detail. Statistical verification for both equations was accomplished using independent data from 2008 in proximity to supercell storms identified by staff at SPC. A recent version of the STP was utilized as a comparison diagnostic to accomplish part of the statistical verification. The results of this research suggest that output from both logistic regression equations can provide valuable guidance in a probabilistic sense, when adjustments are made for the ongoing convective mode. Case studies presented also suggest that this guidance can provide information complementary to STP in severe weather situations with potential for supercell tornadoes.

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Roger Edwards, Andrew R. Dean, Richard L. Thompson, and Bryan T. Smith

Abstract

A gridded, hourly, three-dimensional environmental mesoanalysis database at the Storm Prediction Center (SPC), based on objectively analyzed surface observations blended with the Rapid Update Cycle (RUC) model-analysis fields and described in Parts I and II of this series, is applied to a 2003–11 subset of the SPC tropical cyclone (TC) tornado records. Distributions of environmental convective parameters, derived from SPC hourly mesoanalysis fields that have been related to supercells and tornadoes in the midlatitudes, are evaluated for their pertinence to TC tornado occurrence. The main factor differentiating TC from non-TC tornado environments is much greater deep-tropospheric moisture, associated with reduced lapse rates, lower CAPE, and smaller and more compressed distributions of parameters derived from CAPE and vertical shear. For weak and strong TC tornado categories (EF0–EF1 and EF2–EF3 on the enhanced Fujita scale, respectively), little distinction is evident across most parameters. Radar reflectivity and velocity data also are examined for the same subset of TC tornadoes, in order to determine parent convective modes (e.g., discrete, linear, clustered, supercellular vs nonsupercellular), and the association of those modes with several mesoanalysis parameters. Supercellular TC tornadoes are accompanied by somewhat greater vertical shear than those occurring from other modes. Tornadoes accompanying nonsupercellular radar echoes tend to occur closer to the TC center, where CAPE and shear tend to weaken relative to the outer TC envelope, though there is considerable overlap of their respective radial distributions and environmental parameter spaces.

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Bryan T. Smith, Richard L. Thompson, Andrew R. Dean, and Patrick T. Marsh

Abstract

Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009–13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity (V rot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak V rot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak V rot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool.

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Alexandra K. Anderson-Frey, Yvette P. Richardson, Andrew R. Dean, Richard L. Thompson, and Bryan T. Smith

Abstract

In this work, self-organizing maps (SOMs) are used to investigate patterns of favorable near-storm environmental parameters in a 13-yr climatology of 14 814 tornado events and 44 961 tornado warnings across the continental United States. Establishing nine statistically distinct clusters of spatial distributions of the significant tornado parameter (STP) in the 480 km × 480 km region surrounding each tornado event or warning allows for the examination of each cluster in isolation. For tornado events, distinct patterns are associated more with particular times of day, geographical locations, and times of year. For example, the archetypal springtime dryline setup in the Great Plains emerges readily from the data. While high values of STP tend to correspond to relatively high probabilities of detection (PODs) and relatively low false alarm ratios (FARs), the majority of tornado events occur within a pattern of uniformly lower STP, with relatively high FAR and low POD. Overall, the two-dimensional plots produced by the SOM approach provide an intuitive way of creating nuanced climatologies of tornadic near-storm environments.

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Clark Evans, Steven J. Weiss, Israel L. Jirak, Andrew R. Dean, and David S. Nevius

Abstract

This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.

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Alexandra K. Anderson-Frey, Yvette P. Richardson, Andrew R. Dean, Richard L. Thompson, and Bryan T. Smith

Abstract

In this study, a 13-yr climatology of tornado event and warning environments, including metrics of tornado intensity and storm morphology, is investigated with particular focus on the environments of tornadoes associated with quasi-linear convective systems and right-moving supercells. The regions of the environmental parameter space having poor warning performance in various geographical locations, as well as during different times of the day and year, are highlighted. Kernel density estimations of the tornado report and warning environments are produced for two parameter spaces: mixed-layer convective available potential energy (MLCAPE) versus 0–6-km vector shear magnitude (SHR6), and mixed-layer lifting condensation level (MLLCL) versus 0–1-km storm-relative helicity (SRH1). The warning performance is best in environments characteristic of severe convection (i.e., environments featuring large values of MLCAPE and SHR6). For tornadoes occurring during the early evening transition period, MLCAPE is maximized, MLLCL heights decrease, SHR6 and SRH1 increase, tornadoes rated as 2 or greater on the enhanced Fujita scale (EF2+) are most common, the probability of detection is relatively high, and false alarm ratios are relatively low. Overall, the parameter-space distributions of warnings and events are similar; at least in a broad sense, there is no systematic problem with forecasting that explains the high overall false alarm ratio, which instead seems to stem from the inability to know which storms in a given environment will be tornadic.

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Alexandra K. Anderson-Frey, Yvette P. Richardson, Andrew R. Dean, Richard L. Thompson, and Bryan T. Smith

Abstract

Between 2003 and 2015, there were 5343 outbreak tornadoes and 9389 isolated tornadoes reported in the continental United States. Here, the near-storm environmental parameter-space distributions of these two categories are compared via kernel density estimation, and the seasonal, diurnal, and geographical features of near-storm environments of these two sets of events are compared via self-organizing maps (SOMs). Outbreak tornadoes in a given geographical region tend to be characterized by greater 0–1-km storm-relative helicity and 0–6-km vector shear magnitude than isolated tornadoes in the same geographical region and also have considerably higher tornado warning-based probability of detection (POD) than isolated tornadoes. A SOM of isolated tornadoes highlights that isolated tornadoes with higher POD also tend to feature higher values of the significant tornado parameter (STP), regardless of the specific shape of the area of STP. For a SOM of outbreak tornadoes, when two outbreak environments with similarly high magnitudes but different patterns of STP are compared, the difference is primarily geographical, with one environment dominated by Great Plains and Midwest outbreaks and another dominated by outbreaks in the southeastern United States. Two specific tornado outbreaks are featured, and the events are placed into their climatological context with more nuance than typical single proximity sounding-based approaches would allow.

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Alexandra K. Anderson-Frey, Yvette P. Richardson, Andrew R. Dean, Richard L. Thompson, and Bryan T. Smith

Abstract

The southeastern United States has become a prime area of focus in tornado-related literature due, in part, to the abundance of tornadoes occurring in high-shear low-CAPE (HSLC) environments. Through this analysis of 4133 tornado events and 16 429 tornado warnings in the southeastern United States, we find that tornadoes in the Southeast do indeed have, on average, higher shear and lower CAPE than tornadoes elsewhere in the contiguous United States (CONUS). We also examine tornado warning skill in the form of probability of detection (POD; percent of tornadoes receiving warning prior to tornado occurrence) and false alarm ratio (FAR; percent of tornado warnings for which no corresponding tornado is detected), and find that, on average, POD is better and FAR is worse for tornadoes in the Southeast than for the CONUS as a whole. These measures of warning skill remain consistent even when we consider only HSLC tornadoes. The Southeast also has nearly double the CONUS percentage of deadly tornadoes, with the highest percentage of these deadly tornadoes occurring during the spring, the winter, and around local sunset. On average, however, the tornadoes with the lowest POD also tend to be those that are weakest and least likely to be deadly; for the most part, the most dangerous storms are indeed being successfully warned.

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Richard L. Thompson, Bryan T. Smith, Jeremy S. Grams, Andrew R. Dean, and Chris Broyles

Abstract

A sample of 22 901 tornado and significant severe thunderstorm events, filtered on an hourly 40-km grid, was collected for the period 2003–11 across the contiguous United States (CONUS). Convective mode was assigned to each case via manual examination of full volumetric radar data (Part I of this study), and environmental information accompanied each grid-hour event from the hourly objective analyses calculated and archived at the Storm Prediction Center (SPC). Sounding-derived parameters related to supercells and tornadoes formed the basis of this investigation owing to the dominance of right-moving supercells in tornado production and the availability of supercell-related convective parameters in the SPC environmental archive. The tornado and significant severe thunderstorm events were stratified by convective mode and season. Measures of buoyancy discriminated most strongly between supercell and quasi-linear convective system (QLCS) tornado events during the winter, while bulk wind differences and storm-relative helicity were similar for both supercell and QLCS tornado environments within in each season. The larger values of the effective-layer supercell composite parameter (SCP) and the effective-layer significant tornado parameter (STP) favored right-moving supercells that produced significant tornadoes, as opposed to weak tornadoes or supercells that produced only significant hail or damaging winds. Additionally, mesocyclone strength tended to increase with increasing SCP for supercells, and STP tended to increase as tornado damage class ratings increased. The findings underscore the importance of convective mode (discrete or cluster supercells), mesocyclone strength, and near-storm environment (as represented by large values of STP) in consistent, real-time identification of intense tornadoes.

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