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John A. Hart and Ariel E. Cohen

Abstract

This study is an application of the Statistical Severe Convective Risk Assessment Model (SSCRAM), which objectively assesses conditional severe thunderstorm probabilities based on archived hourly mesoscale data across the United States collected from 2006 to 2014. In the present study, SSCRAM is used to assess the utility of severe thunderstorm parameters commonly employed by forecasters in anticipating thunderstorms that produce significant tornadoes (i.e., causing F2/EF2 or greater damage) from June through October. The utility during June–October is compared to that during other months. Previous studies have identified some aspects of the summertime challenge in severe storm forecasting, and this study provides an in-depth quantification of the within-year variability of severe storms predictability. Conditional probabilities of significant tornadoes downstream of lightning occurrence using common parameter values, such as the effective-layer significant tornado parameter, convective available potential energy, and vertical shear, are found to substantially decrease during the months of June–October compared to other months. Furthermore, conditional probabilities of significant tornadoes during June–October associated with these parameters are nearly invariable regardless of value, highlighting the challenge of using objective environmental data to attempt to forecast significant tornadoes from June through October.

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Ariel E. Cohen and John P. Cangialosi

Abstract

The Tropical Analysis and Forecast Branch (TAFB) of the National Oceanic and Atmospheric Administration’s (NOAA’s) National Hurricane Center in Miami, Florida, provides high-seas forecasts to portions of the eastern Pacific Ocean, including the Gulf of California. These forecasts include wind velocity and significant wave height forecasts and are initiated by forecast winds of at least 20 kt (10.3 m s−1) or significant wave heights of at least 8 ft (2.4 m). The Gulf of California is a commonly traveled area, where winds are highly modulated by nearby terrain variations. This provides a unique forecast challenge, especially in the absence of regular surface observations. In October and November 2008, the NOAA R/V David Starr Jordan was stationed in the Gulf of California and occasionally reported gale force winds [34–47 kt (17.5–24.2 m s−1)], which operational models regularly missed. A ship log of these events provided the basis for determining mean and anomaly fields for a handful of meteorological variables, from which a conceptual model for the synoptic-scale environment supporting these events is presented. An index based on the mean sea level pressure (MSLP) difference between Ely, Nevada, and Yuma, Arizona, was developed to measure the potential for gales, which was found to be statistically significant in discriminating between “gale” and “marginal wind” events. The fifth-generation NCAR–Pennsylvania State University Mesoscale Model (MM5) is used to conduct doubly nested high-resolution simulations centered on the Gulf of California. These simulations appeared to resolve the gales better than traditional global model guidance, lending credence toward the need for high-resolution modeling in areas of highly variable terrain. Relatively small errors were found in MM5 output using the National Aeronautics and Space Administration (NASA) Quick Scatterometer (QuikSCAT) data as verification.

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John A. Hart and Ariel E. Cohen

Abstract

This study introduces a system that objectively assesses severe thunderstorm nowcast probabilities based on hourly mesoscale data across the contiguous United States during the period from 2006 to 2014. Previous studies have evaluated the diagnostic utility of parameters in characterizing severe thunderstorm environments. In contrast, the present study merges cloud-to-ground lightning flash data with both severe thunderstorm report and Storm Prediction Center Mesoscale Analysis system data to create lightning-conditioned prognostic probabilities for numerous parameters, thus incorporating null-severe cases. The resulting dataset and corresponding probabilities are called the Statistical Severe Convective Risk Assessment Model (SSCRAM), which incorporates a sample size of over 3.8 million 40-km grid boxes. A subset of five parameters of SSCRAM is investigated in the present study. This system shows that severe storm probabilities do not vary strongly across the range of values for buoyancy parameters compared to vertical shear parameters. The significant tornado parameter [where “significant” refers to tornadoes producing (Fujita scale) F2/(enhanced Fujita scale) EF2 damage] exhibits considerable skill at identifying downstream tornado events, with higher conditional probabilities of occurrence at larger values, similar to effective storm-relative helicity, both findings being consistent with previous studies. Meanwhile, lifting condensation level heights are associated with conditional probabilities that vary little within an optimal range of values for tornado occurrence, yielding less skill in quantifying tornado potential using this parameter compared to effective storm-relative helicity. The systematic assessment of probabilities using convective environmental information could have applications in present-day operational forecasting duties and the upcoming warn-on-forecast initiatives.

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Daphne S. LaDue and Ariel E. Cohen

Abstract

Professional meteorologists gain a great deal of knowledge through formal education, but two factors require ongoing learning throughout a career: professionals must apply their learning to the specific subdiscipline they practice, and the knowledge and technology they rely on becomes outdated over time. It is thus inherent in professional practice that much of the learning is more or less self-directed. While these principles apply to any aspect of meteorology, this paper applies concepts to weather and climate forecasting, for which a range of resources, from many to few, for learning exist. No matter what the subdiscipline, the responsibility for identifying and pursuing opportunities for professional, lifelong learning falls to the members of the subdiscipline. Thus, it is critical that meteorologists periodically assess their ongoing learning needs and develop the ability to reflectively practice. The construct of self-directed learning and how it has been implemented in similar professions provide visions for how individual meteorologists can pursue—and how the profession can facilitate—the ongoing, self-directed learning efforts of meteorologists.

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Melissa M. Hurlbut and Ariel E. Cohen

Abstract

An investigation of the environments and climatology of severe thunderstorms from 1999 through 2009 across the northeastern United States is presented. A total of 742 severe weather events producing over 12 000 reports were examined. Given the challenges that severe weather forecasting can present in the Northeast, this study is an effort to distinguish between the more prolific severe-weather-producing events and those that produce only isolated severe weather. The meteorological summer months (June–August) are found to coincide with the peak severe season. During this time, 850–500- and 700–500-hPa lapse rates, mixed layer convective inhibition (MLCIN), and downdraft convective available potential energy (DCAPE) are found to be statistically significant in discriminating events with a large number of reports from those producing fewer reports, based on observed soundings. Composite synoptic pattern analyses are also presented to spatially characterize the distribution of key meteorological variables associated with severe weather events of differing magnitudes. The presence of a midlevel trough and particular characteristics of its tilt, along with an accompanying zone of enhanced flow, are found in association with the higher-report severe weather events, along with cooler midlevel temperatures overlaying warmer low-level temperatures (i.e., contributing to the steeper lapse rates). During the meteorological fall and winter months (September–February), large-scale ascent is often bolstered by the presence of a coupled upper-level jet structure.

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Jaret W. Rogers, Ariel E. Cohen, and Lee B. Carlaw

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This comprehensive analysis of convective environments associated with thunderstorms affecting portions of central and southern Arizona during the North American monsoon focuses on both observed soundings and mesoanalysis parameters relative to lightning flash counts and severe-thunderstorm reports. Analysis of observed sounding data from Phoenix and Tucson, Arizona, highlights several moisture and instability parameters exhibiting moderate correlations with 24-h, domain-total lightning and severe thunderstorm counts, with accompanying plots of the precipitable water, surface-based lifted index, and 0–3-km layer mixing ratio highlighting the relationship to the domain-total lightning count. Statistical techniques, including stepwise, multiple linear regression and logistic regression, are applied to sounding and gridded mesoanalysis data to predict the domain-total lightning count and individual gridbox 3-h-long lightning probability, respectively. Applications of these forecast models to an independent dataset from 2013 suggest some utility in probabilistic lightning forecasts from the regression analyses. Implementation of this technique into an operational forecast setting to supplement short-term lightning forecast guidance is discussed and demonstrated. Severe-thunderstorm-report predictive models are found to be less skillful, which may partially be due to substantial population biases noted in storm reports over central and southern Arizona.

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Lee B. Carlaw, Ariel E. Cohen, and Jaret W. Rogers

Abstract

This paper comprehensively analyzes the synoptic and mesoscale environment associated with North American monsoon–related thunderstorms affecting central and southern Arizona. Analyses of thunderstorm environments are presented using reanalysis data, severe thunderstorm reports, and cloud-to-ground lightning information from 2003 to 2013, which serves as a springboard for lightning-prediction models provided in a companion paper. Spatial and temporal analyses of lightning strikes indicate thunderstorm frequencies maximize between 2100 and 0000 UTC, when the greatest frequencies are concentrated over higher terrain. Severe thunderstorm reports typically occur later in the day (between 2300 and 0100 UTC), while reports are maximized in the Tucson and Phoenix metropolitan areas. Composite analyses of the synoptic-scale patterns associated with severe thunderstorm days and nonthunderstorm days during the summer using the North American Regional Reanalysis dataset are presented. Severe thunderstorm cases tend to be associated with a stronger midlevel anticyclone and deep-layer moisture over portions of the southwestern United States. By September, severe weather patterns tend to associate with a midlevel trough along the Pacific coast. Specific parameters associated with severe thunderstorms are analyzed across the Tucson and Phoenix areas, where severe weather reporting is more consistent. Greater convective available potential energy, low-level lapse rates, and downdraft convective available potential energy are associated with severe thunderstorm (especially severe wind) environments compared to those with nonsevere thunderstorms, while stronger effective bulk wind differences (at least 15–20 kt, where 1 kt = 0.51 m s−1) can be used to distinguish severe hail environments.

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Ariel E. Cohen, Joel B. Cohen, Richard L. Thompson, and Bryan T. Smith

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This study presents the development and testing of two statistical models that simulate tornado potential and wind speed. This study reports on the first-ever development of two multiple regression–based models to assist warning forecasters in statistically simulating tornado probability and tornado wind speed in a diagnostic manner based on radar-observed tornado signature attributes and one environmental parameter. Based on a robust database, the radar-based storm-scale circulation attributes (strength, height above ground, clarity) combine with the effective-layer significant tornado parameter to establish a tornado probability. The second model adds the categorical presence (absence) of a tornadic debris signature to derive the tornado wind speed. While the fits of these models are considered somewhat modest, their regression coefficients generally offer physical consistency, based on findings from previous research. Furthermore, simulating these models on an independent dataset and other past cases featured in previous research reveals encouraging signals for accurately identifying higher potential for tornadoes. This statistical application using large-sample-size datasets can serve as a first step to streamlining the process of reproducibly quantifying tornado threats by service-providing organizations in a diagnostic manner, encouraging consistency in messaging scientifically sound information for the protection of life and property.

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Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, and Harold E. Brooks

Abstract

The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.

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Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, Harold E. Brooks, and Israel L. Jirak

Abstract

Southeast U.S. cold season severe weather events can be difficult to predict because of the marginality of the supporting thermodynamic instability in this regime. The sensitivity of this environment to prognoses of instability encourages additional research on ways in which mesoscale models represent turbulent processes within the lower atmosphere that directly influence thermodynamic profiles and forecasts of instability. This work summarizes characteristics of the southeast U.S. cold season severe weather environment and planetary boundary layer (PBL) parameterization schemes used in mesoscale modeling and proceeds with a focused investigation of the performance of nine different representations of the PBL in this environment by comparing simulated thermodynamic and kinematic profiles to observationally influenced ones. It is demonstrated that simultaneous representation of both nonlocal and local mixing in the Asymmetric Convective Model, version 2 (ACM2), scheme has the lowest overall errors for the southeast U.S. cold season tornado regime. For storm-relative helicity, strictly nonlocal schemes provide the largest overall differences from observationally influenced datasets (underforecast). Meanwhile, strictly local schemes yield the most extreme differences from these observationally influenced datasets (underforecast) in a mean sense for the low-level lapse rate and depth of the PBL, on average. A hybrid local–nonlocal scheme is found to mitigate these mean difference extremes. These findings are traced to a tendency for local schemes to incompletely mix the PBL while nonlocal schemes overmix the PBL, whereas the hybrid schemes represent more intermediate mixing in a regime where vertical shear enhances mixing and limited instability suppresses mixing.

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