Andrew E. Mercer

Tornado Risk Analysis: Is Dixie Alley an Extension of Tornado Alley?

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...

Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

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 differentthreats thando thoseassociated with atornadooutbreak. Themaingoalof this workisto 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 synopticscale 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.

Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale Input

Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.

Synoptic Composites of Tornadic and Nontornadic Outbreaks

AbstractTornadic 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 compo...

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.

Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

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 stormrelative 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.

Statistical Modeling of Downslope Windstorms in Boulder, Colorado

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.

Objective Identification of Tornado Seasons and Ideal Spatial Smoothing Radii

AbstractThe 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 b...

Statistical Differences of Quasigeostrophic Variables, Stability, and Moisture Profiles in North American Storm Tracks

Abstract 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 cyclo...

Assessment of Spatial Rainfall Variability over the Lower Mississippi River Alluvial Valley

AbstractA large portion of the lower Mississippi River alluvial valley (LMRAV) relies on irrigation from the regional alluvial aquifer for crop sustainability, which is expensive both in terms of water resources and farmer expenditures because of the large volume of water necessary to maintain crop production. As a result, knowledge of the seasonal frequency and distribution of precipitation over the LMRAV is critical for water resources management, the development of irrigation strategies, and economic planning. This project addresses the need for a detailed assessment of regional precipitation patterns through the use of rotated principal component analysis (RPCA) of high-resolution gridded radar-derived rainfall data, which provides quantification of the spatial and temporal characteristics of rainfall over the LMRAV from 1996 to 2011. Results of the project show that precipitation depths over the LMRAV are generally lower and more variable than adjacent eastern areas throughout the year, although ther...