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P. Grady Dixon and Andrew E. Mercer

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Andrew E. Mercer and Michael B. Richman

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Three common synoptic storm tracks observed throughout the United States are the Alberta Clipper, the Colorado cyclone, and the East Coast storm. Numerous studies have been performed on individual storm tracks analyzing quasigeostrophic dynamics, stability, and moisture profiles in each. This study evaluated storms in each track to help diagnose patterns and magnitudes of the aforementioned quantities, documenting how they compare from track to track. Six diagnostic variables were computed to facilitate the comparison of the storm tracks: differential geostrophic absolute vorticity advection, temperature advection, Q-vector divergence, mean layer specific humidity, low-level stability, and midlevel stability. A dataset was compiled, consisting of 101 Alberta Clippers, 165 Colorado cyclones, and 159 East Coast cyclones and mean fields were generated for this comparison. Maxima and minima of the 25th and 75th percentiles were generated to diagnose magnitudes and patterns of strong versus weak cyclones and measure their similarities and differences to the mean patterns. Alberta Clippers were found to show the weakest magnitude of quasigeostrophic variables, while East Coast storms had the strongest magnitudes. Alberta Clippers maintained the lowest moisture content through their life cycle as well. However, East Coast storms were the most stable of the three tracks. Typically, correlations between storm tracks were high; suggesting that storm evolution is similar between tracks, in terms of the patterns of diagnostic variables measured. However, significant magnitude differences in the quasigeostrophic variables distinguished the storms in each track.

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Andrew E. Mercer, Alexandria D. Grimes, and Kimberly M. Wood

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Tropical cyclone (TC) track forecasts have improved in recent decades while intensity forecasts, particularly predictions of rapid intensification (RI), continue to show low skill. Many statistical methods have shown promise in predicting RI using environmental fields, though these methods heavily rely upon supervised learning techniques such as classification. Advances in unsupervised learning techniques, particularly those that integrate nonlinearity into the class separation problem, can improve discrimination ability for difficult tasks such as RI prediction. This study quantifies separability between RI and non-RI environments for 2004 – 2016 Atlantic TCs using an unsupervised learning method that blends principal component analysis with k-means cluster analysis. Input fields consisted of TC-centered 1° Global Forecast System analysis (GFSA) grids (170 different variables and isobaric levels) for 3605 TC samples and five domain sizes. Results are directly compared against separability offered by operational RI forecast predictors for eight RI definitions. The unsupervised learning procedure produced improved separability over operational predictors for all eight RI definitions, five of which showed statistically significant improvement. Composites from these best-separating GFSA fields highlighted the importance of mid- and upper-level relative humidity in identifying the onset of short-term RI, while long-term, higher-magnitude RI was generally associated with weaker absolute vorticity. Other useful predictors included optimal thermodynamic RI ingredients along the mean trajectory of the TC. The results suggest that the orientation of a more favorable thermodynamic environment relative to the TC and mid-level vorticity magnitudes could be useful predictors for RI.

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P. Grady Dixon, Andrew E. Mercer, Jinmu Choi, and Jared S. Allen

The term “Tornado Alley” is a gross approximation of the most tornado-prone region in the United States. Depending on calculation methods, Tornado Alley can vary dramatically across the area between the Rocky and Appalachian Mountains. There is some evidence that multiple alleys of peak tornado activity exist around the country, including “Dixie Alley” in the Southeast. Therefore, we assess the spatial tornado risk and seek any regions of elevated tornado risk that are distinctly separate from the traditional Tornado Alley of the Great Plains. Results show there are no tornado risk areas statistically separate from Tornado Alley, but there are large portions of the Southeast that experience more tornadoes than the rest of the country. It appears that Tornado Alley and Dixie Alley are part of a single large region of high tornado risk with a relative minimum near the middle due to the Ozark and Ouachita Mountains. Placement of the maximum tornado density in Mississippi, along with other regions of relative maxima across the Southeast, may warrant modification of the traditional tornado risk map that focuses only on the Great Plains. Understanding such patterns is important for preparing the public and mitigating tornado hazards.

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Andrew E. Mercer, Chad M. Shafer, Charles A. Doswell III, Lance M. Leslie, and Michael B. Richman

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Tornadic and nontornadic outbreaks occur within the United States and elsewhere around the world each year with devastating effect. However, few studies have considered the physical differences between these two outbreak types. To address this issue, synoptic-scale pattern composites of tornadic and nontornadic outbreaks are formulated over North America using a rotated principal component analysis (RPCA). A cluster analysis of the RPC loadings group similar outbreak events, and the resulting map types represent an idealized composite of the constituent cases in each cluster. These composites are used to initialize a Weather Research and Forecasting Model (WRF) simulation of each hypothetical composite outbreak type in an effort to determine the WRF’s capability to distinguish the outbreak type each composite represents.

Synoptic-scale pattern analyses of the composites reveal strikingly different characteristics within each outbreak type, particularly in the wind fields. The tornado outbreak composites reveal a strong low- and midlevel cyclone over the eastern Rockies, which is likely responsible for the observed surface low pressure system in the plains. Composite soundings from the hypothetical outbreak centroids reveal significantly greater bulk shear and storm-relative environmental helicity values in the tornado outbreak environment, whereas instability fields are similar between the two outbreak types. The WRF simulations of the map types confirm results observed in the composite soundings.

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Andrew E. Mercer, Michael B. Richman, Howard B. Bluestein, and John M. Brown

Abstract

Downslope windstorms are of major concern to those living in and around Boulder, Colorado, often striking with little warning, occasionally bringing clear-air wind gusts of 35–50 m s−1 or higher, and producing widespread damage. Historically, numerical models used for forecasting these events had lower than desired accuracy. This observation provides the motivation to study the potential for improving windstorm forecasting through the use of linear and nonlinear statistical modeling techniques with a perfect prog approach. A 10-yr mountain-windstorm dataset and a set of 18 predictors are used to train and test the models. For the linear model, a stepwise regression is applied. It is difficult to determine which predictor is the most important, although significance testing suggests that 700-hPa flow is selected often. The nonlinear techniques employed, feedforward neural networks (NN) and support vector regression (SVR), do not filter out predictors as the former uses a hidden layer to account for the nonlinearities in the data, whereas the latter fits a kernel function to the data to optimize prediction. The models are evaluated using root-mean-square error (RMSE) and median residuals. The SVR model has the lowest forecast errors, consistently, and is not prone to creating outlier forecasts. Stepwise linear regression (LR) yielded results that were accurate to within an RMSE of 8 m s−1; whereas an NN had errors of 7–9 m s−1 and SVR had errors of 4–6 m s−1. For SVR, 85% of the forecasts predicted maximum wind gusts with an RMSE of less than 6 m s−1 and all forecasts predicted wind gusts with an RMSE of below 12 m s−1. The LR method performed slightly better in most evaluations than NNs; however, SVR was the optimal technique.

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Andrew E. Mercer, Chad M. Shafer, Charles A. Doswell III, Lance M. Leslie, and Michael B. Richman

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Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

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Chad M. Shafer, Andrew E. Mercer, Lance M. Leslie, Michael B. Richman, and Charles A. Doswell III

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Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes.

This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

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Chad M. Shafer, Andrew E. Mercer, Michael B. Richman, Lance M. Leslie, and Charles A. Doswell III

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The areal extent of severe weather parameters favorable for significant severe weather is evaluated as a means of identifying major severe weather outbreaks. The first areal coverage method uses kernel density estimation (KDE) to identify severe weather outbreak locations. A selected severe weather parameter value is computed at each grid point within the region identified by KDE. The average, median, or sum value is used to diagnose the event’s severity. The second areal coverage method finds the largest contiguous region where a severe weather parameter exceeds a specified threshold that intersects the KDE region. The severe weather parameter values at grid points within the parameter exceedance region are computed, with the average, median, or sum value used to diagnose the event’s severity. A total of 4057 severe weather outbreaks from 1979 to 2008 are analyzed. An event is considered a major outbreak if it exceeds a selected ranking index score (developed in previous work), and is a minor event otherwise. The areal coverage method is also compared to Storm Prediction Center (SPC) day-1 convective outlooks from 2003 to 2008. Comparisons of the SPC forecasts and areal coverage diagnoses indicate the areal coverage methods have similar skill to SPC convective outlooks in discriminating major and minor severe weather outbreaks. Despite a seemingly large sample size, the rare-events nature of the dataset leads to sample size sensitivities. Nevertheless, the findings of this study suggest that areal coverage should be tested in a forecasting environment as a means of providing guidance in future outbreak scenarios.

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P. Grady Dixon, Andrew E. Mercer, Katarzyna Grala, and William H. Cooke

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The fundamental purpose of this research is to highlight the spatial seasonality of tornado risk. This requires the use of objective methods to determine the appropriate spatial extent of the bandwidth used to calculate tornado density values (i.e., smoothing the raw tornado data). With the understanding that a smoothing radius depends partially upon the period of study, the next step is to identify objectively ideal periods of tornado analysis. To avoid decisions about spatial or temporal boundaries, this project makes use of storm speed and tornado pathlength data, along with statistical cluster analysis, to establish tornado seasons that display significantly different temporal and spatial patterns. This method yields four seasons with unique characteristics of storm speed and tornado pathlength.

The results show that the ideal bandwidth depends partially upon the temporal analysis period and the lengths of the tornadoes studied. Hence, there is not a “one size fits all,” but the bandwidth can be quantitatively chosen for a given dataset. Results from this research, based upon tornado data for 1950–2011, yield ideal bandwidths ranging from 55 to 180 km. The ideal smoothing radii are then applied via a kernel density analysis of each new tornado season.

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