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.

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.

Abstract

This paper describes observation of the East African low-level jet stream obtained during June and July 1977 with a long-range research aircraft. We present results based on the real-time display of data on board the aircraft during the research flights. The jet stream core was located at about 40°E between 1 and 2 km altitude. The jet had a very sharp horizontal shear layer to the west of the core, with less pronounced shear to the east extending out over the ocean. Although flowing persistently from the south, it experienced a strong diurnal change over land. In addition, other changes in structure on a longer time scale were observed. This article presents the kinematic and thermal structure of the jet, and the low-level flow further upstream. Based on these data, the estimated cross-equational water vapor flux across the equator was found to be much higher than previously thought.

Abstract

A sample of 413 soundings in close proximity to tornadic and nontornadic supercells is examined. The soundings were obtained from hourly analyses generated by the 40-km Rapid Update Cycle-2 (RUC-2) analysis and forecast system. A comparison of 149 observed soundings and collocated RUC-2 soundings in regional supercell environments reveals that the RUC-2 model analyses were reasonably accurate through much of the troposphere. The largest error tendencies were in temperatures and mixing ratios near the surface, primarily in 1-h forecast soundings immediately prior to the standard rawinsonde launches around 1200 and 0000 UTC. Overall, the RUC-2 analysis soundings appear to be a reasonable proxy for observed soundings in supercell environments.

Thermodynamic and vertical wind shear parameters derived from RUC-2 proximity soundings are evaluated for the following supercell and storm subsets: significantly tornadic supercells (54 soundings), weakly tornadic supercells (144 soundings), nontornadic supercells (215 soundings), and discrete nonsupercell storms (75 soundings). Findings presented herein are then compared to results from previous and ongoing proximity soundings studies. Most significantly, proximity soundings presented here reinforce the findings of previous studies in that vertical shear and moisture within 1 km of the ground can discriminate between nontornadic supercells and supercells producing tornadoes with F2 or greater damage. Parameters that combine measures of buoyancy, vertical shear, and low-level moisture show the strongest ability to discriminate between supercell classes.

Abstract

With a variety of programming languages and data formats available, widespread adoption of computing standards by the atmospheric science community is often difficult to achieve. The Sounding and Hodograph Analysis and Research Program in Python (SHARPpy) is an open-source, cross-platform, upper-air sounding analysis and visualization package. SHARPpy is based on the National Oceanic and Atmospheric Administration/Storm Prediction Center’s (NOAA/SPC) in-house analysis package, SHARP, and is the result of a collaborative effort between forecasters at the SPC and students at the University of Oklahoma’s School of Meteorology. The major aim of SHARPpy is to provide a consistent framework for sounding analysis that is available to all. Nearly all routines are written to be as consistent as possible with the methods researched, tested, and developed in the SPC, which sets this package apart from other sounding analysis tools.

SHARPpy was initially demonstrated and released to the atmospheric community at the American Meteorological Society (AMS) Annual Meeting in 2012, and an updated and greatly expanded version was released at the AMS Annual Meeting in 2015. Since this release, SHARPpy has been adopted by a variety of operational and research meteorologists across the world. In addition, SHARPpy’s open-source nature enables collaborations between other developers, resulting in major additions to the program.

Abstract

The Airborne Cloud–Aerosol Transport System (ACATS) is a Doppler wind lidar system that has recently been developed for atmospheric science capabilities at the NASA Goddard Space Flight Center (GSFC). ACATS is also a high-spectral-resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from a high-altitude aircraft. Thus, ACATS simultaneously measures optical properties and motion of cloud and aerosol layers. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS HSRL retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions, such as the Cloud–Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such as dust or smoke transport and convective outflow in anvil cirrus clouds.

Abstract

Tornadoes that occur at night pose particularly dangerous societal risks, and these risks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics distinguishing the convective environment accompanying these events. This is accomplished by building upon previous research that assesses the predictive power of meteorological parameters. In particular, this study uses the Statistical Severe Convective Risk Assessment Model (SSCRAM) to determine how well convective parameters explain tornado potential across the Southeast during the months of November–May and during the 0300–1200 UTC (nocturnal) time frame. This study compares conditional tornado probabilities across the Southeast during November–May nocturnal hours to those probabilities for all other November–May environments across the contiguous United States. This study shows that effective bulk shear, effective storm-relative helicity, and effective-layer significant tornado parameter yield the strongest predictability for the November–May nocturnal Southeast regime among investigated parameters. This study demonstrates that November–May southeastern U.S. nocturnal predictability is generally similar to that within other regimes across the contiguous United States. However, selected ranges of multiple parameters are associated with slightly better predictability for the nocturnal Southeast regime. Additionally, this study assesses conditional November–May nocturnal tornado probabilities across a coastal domain embedded within the Southeast. Nocturnal coastal tornado predictability is shown to generally be lower than the other regimes. All of the differences highlight several forecast challenges, which this study analyzes in detail.

Abstract

During the 2014–15 academic year, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service Storm Prediction Center (SPC) and the University of Oklahoma (OU) School of Meteorology jointly created the first SPC-led course at OU focused on connecting traditional theory taught in the academic curriculum with operational meteorology. This class, “Applications of Meteorological Theory to Severe-Thunderstorm Forecasting,” began in 2015. From 2015 through 2017, this spring–semester course has engaged 56 students in theoretical skills and related hands-on weather analysis and forecasting applications, taught by over a dozen meteorologists from the SPC, the NOAA National Severe Storms Laboratory, and the NOAA National Weather Service Forecast Offices. Following introductory material, which addresses many theoretical principles relevant to operational meteorology, numerous presentations and hands-on activities focused on instructors’ areas of expertise are provided to students. Topics include the following: storm-induced perturbation pressure gradients and their enhancement to supercells, tornadogenesis, tropical cyclone tornadoes, severe wind forecasting, surface and upper-air analyses and their interpretation, and forecast decision-making. This collaborative approach has strengthened bonds between meteorologists in operations, research, and academia, while introducing OU meteorology students to the vast array of severe thunderstorm forecast challenges, state-of-the-art operational and research tools, communication of high-impact weather information, and teamwork skills. The methods of collaborative instruction and experiential education have been found to strengthen both operational–academic relationships and students’ appreciation of the intricacies of severe thunderstorm forecasting, as detailed in this article.

Abstract

Extratropical transition (ET) is the process by which a tropical cyclone, upon encountering a baroclinic environment and reduced sea surface temperature at higher latitudes, transforms into an extratropical cyclone. This process is influenced by, and influences, phenomena from the tropics to the midlatitudes and from the meso- to the planetary scales to extents that vary between individual events. Motivated in part by recent high-impact and/or extensively observed events such as North Atlantic Hurricane Sandy in 2012 and western North Pacific Typhoon Sinlaku in 2008, this review details advances in understanding and predicting ET since the publication of an earlier review in 2003. Methods for diagnosing ET in reanalysis, observational, and model-forecast datasets are discussed. New climatologies for the eastern North Pacific and southwest Indian Oceans are presented alongside updates to western North Pacific and North Atlantic Ocean climatologies. Advances in understanding and, in some cases, modeling the direct impacts of ET-related wind, waves, and precipitation are noted. Improved understanding of structural evolution throughout the transformation stage of ET fostered in large part by novel aircraft observations collected in several recent ET events is highlighted. Predictive skill for operational and numerical model ET-related forecasts is discussed along with environmental factors influencing posttransition cyclone structure and evolution. Operational ET forecast and analysis practices and challenges are detailed. In particular, some challenges of effective hazard communication for the evolving threats posed by a tropical cyclone during and after transition are introduced. This review concludes with recommendations for future work to further improve understanding, forecasts, and hazard communication.

Abstract

Pan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact-relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly, effects of explicit convection affect not only projected changes in rainfall extremes, dry spells, and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change means that we can provide regional decision-makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the U.K. Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international coordination of such computationally and human-resource expensive simulations as effectively as possible.