The Challenge of Forecasting Significant Tornadoes from June to October Using Convective Parameters

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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The Statistical Severe Convective Risk Assessment Model

Weather and Forecasting ◽

10.1175/waf-d-16-0004.1 ◽

2016 ◽

Vol 31 (5) ◽

pp. 1697-1714 ◽

Cited By ~ 7

Author(s):

John A. Hart ◽

Ariel E. Cohen

Keyword(s):

Risk Assessment ◽

Assessment Model ◽

Vertical Shear ◽

Lightning Flash ◽

Risk Assessment Model ◽

Conditional Probabilities ◽

Severe Thunderstorm ◽

Systematic Assessment ◽

Analysis System ◽

Range Of Values

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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Examination of the Predictability of Nocturnal Tornado Events in the Southeastern United States

Weather and Forecasting ◽

10.1175/waf-d-18-0162.1 ◽

2019 ◽

Vol 34 (2) ◽

pp. 467-479 ◽

Cited By ~ 1

Author(s):

Ryan C. Bunker ◽

Ariel E. Cohen ◽

John A. Hart ◽

Alan E. Gerard ◽

Kim E. Klockow-McClain ◽

...

Keyword(s):

United States ◽

Predictive Power ◽

Meteorological Parameters ◽

Southeastern United States ◽

Time Frame ◽

Assessment Model ◽

The Other ◽

Risk Assessment Model ◽

Multiple Parameters ◽

Effective Layer

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.

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Quantitative Risk Assessment Model of Human Salmonellosis Resulting from Consumption of Broiler Chicken

Diseases ◽

10.3390/diseases7010019 ◽

2019 ◽

Vol 7 (1) ◽

pp. 19 ◽

Cited By ~ 4

Author(s):

Luma Akil ◽

H. Anwar Ahmad

Keyword(s):

United States ◽

Risk Assessment ◽

Broiler Chicken ◽

The United States ◽

Assessment Model ◽

Quantitative Risk Assessment ◽

United States Department ◽

Risk Assessment Model ◽

Broiler Meat ◽

Salmonella Infections

(1) Background: Salmonella infections are a major cause of illnesses in the United States. Each year around 450 people die from the disease and more than 23,000 people are hospitalized. Salmonella outbreaks are commonly associated with eggs, meat and poultry. In this study, a quantitative risk assessment model (QRAM) was developed to determine Salmonella infections in broiler chicken. (2) Methods: Data of positive Salmonella infections were obtained from the United States Department of Agriculture (USDA) and the Centers for Disease Control and Prevention (CDC) Foodborne Disease Outbreak Surveillance System, in addition to published literature. The Decision Tools @RISK add-in software was used for various analyses and to develop the QRAM. The farm-to-fork pathway was modeled as a series of unit operations and associated pathogen events that included initial contamination at the broiler house (node 1), contamination at the slaughter house (node 2), contamination at retail (node 3), cross-contamination during serving and cooking (node 4), and finally the dose–response model after consumption. (3) Results: QRAM of Salmonella infections from broiler meat showed highest contribution of infection from the retail node (33.5%). (4) Conclusions: This QRAM that predicts the risk of Salmonella infections could be used as a guiding tool to manage the Salmonella control programs

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Environmental Impact Assessment of Transporting Hazardous Waste Generated by Maquiladora Industry in U.S.-Mexico Border Region

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1602-13 ◽

1997 ◽

Vol 1602 (1) ◽

pp. 84-92 ◽

Cited By ~ 3

Author(s):

Suleiman A. Ashur ◽

M. Hadi Baaj ◽

K. David Pijawka ◽

Derar S. Serhan

Keyword(s):

United States ◽

Risk Assessment ◽

Hazardous Waste ◽

The United States ◽

Assessment Model ◽

Border Region ◽

Risk Assessment Model ◽

Border Area ◽

Mexico Border ◽

United States Mexico

Hazardous waste shipments from U.S.-owned industries in the northern part of Mexico near the border with the United States are a growing problem. Today, the Mexican Environmental Agency requires all U.S. industries to return the waste produced by their plants to the United States. Currently, there is no database on the amount of hazardous waste transported from these firms, the pattern of shipments (from what origins to what destinations), and the nature of the risks to the population and environment along the shipment routes. In addition, there is a growing need to develop a risk assessment model and framework to focus on the transport of hazardous waste in the United States–Mexico border region, given the anticipated changes resulting from implementation of the North American Free Trade Agreement. Results of the data collection and analysis task and the risk assessment model formulation task are presented. The methodology is demonstrated in a case-study area of the United States–Mexico border region, namely, the Arizona-Sonora border area, and should be a valuable tool for evaluating various transport risk management scenarios of importance to the border area.

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Risk assessment model for venous thromboembolism in hospitalized surgical and non-surgical patients in the 4th Hungarian Antithrombotic Guidelines entitled “Diminution and Treatment of Venous Thromboembolism”

Orvosi Hetilap ◽

10.1556/oh.2010.28944 ◽

2010 ◽

Vol 151 (34) ◽

pp. 1365-1374 ◽

Cited By ~ 1

Author(s):

Marianna Dávid ◽

Hajna Losonczy ◽

Miklós Udvardy ◽

Zoltán Boda ◽

György Blaskó ◽

...

Keyword(s):

Risk Assessment ◽

Venous Thromboembolism ◽

Assessment Model ◽

Surgical Patients ◽

Risk Assessment Model

A kórházban kezelt sebészeti és belgyógyászati betegekben jelentős a vénásthromboembolia-rizikó. Profilaxis nélkül, a műtét típusától függően, a sebészeti beavatkozások kapcsán a betegek 15–60%-ában alakul ki mélyvénás trombózis vagy tüdőembólia, és az utóbbi ma is vezető kórházi halálok. Bár a vénás thromboemboliát leggyakrabban a közelmúltban végzett műtéttel vagy traumával hozzák kapcsolatba, a szimptómás thromboemboliás események 50–70%-a és a fatális tüdőembóliák 70–80%-a nem a sebészeti betegekben alakul ki. Nemzetközi és hazai felmérések alapján a nagy kockázattal rendelkező sebészeti betegek többsége megkapja a szükséges trombózisprofilaxist. Azonban profilaxis nélkül marad a rizikóval rendelkező belgyógyászati betegek jelentős része, a konszenzuson alapuló nemzetközi és hazai irányelvi ajánlások ellenére. A belgyógyászati betegek körében növelni kell a profilaxisban részesülők arányát és el kell érni, hogy trombózisrizikó esetén a betegek megkapják a hatásos megelőzést. A beteg trombóziskockázatának felmérése fontos eszköze a vénás thromboembolia által veszélyeztetett betegek felderítésének, megkönnyíti a döntést a profilaxis elrendeléséről és javítja az irányelvi ajánlások betartását. A trombózisveszély megállapításakor, ha nem ellenjavallt, profilaxist kell alkalmazni. „A thromboemboliák kockázatának csökkentése és kezelése” című, 4. magyar antithromboticus irányelv felhívja a figyelmet a vénástrombózis-rizikó felmérésének szükségességére, és elsőként tartalmazza a kórházban fekvő belgyógyászati és sebészeti betegek kockázati kérdőívét. Ismertetjük a kockázatbecslő kérdőíveket és áttekintjük a kérdőívekben szereplő rizikófaktorokra vonatkozó bizonyítékokon alapuló adatokat.

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Signature Analysis: Statistical Models and Their Application to FA

ISTFA 1996: Conference Proceedings from the 22nd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa1996p0183 ◽

1996 ◽

Author(s):

C.K. Lakshminarayan ◽

S. Pabbisetty ◽

O. Adams ◽

F. Pires ◽

M. Thomas ◽

...

Keyword(s):

Risk Assessment ◽

Failure Analysis ◽

Statistical Models ◽

Essential Elements ◽

Assessment Model ◽

Signature Analysis ◽

Risk Assessment Model ◽

Sample Sizes ◽

Reliability Engineering ◽

Semiconductor Chips

Abstract This paper deals with the basic concepts of Signature Analysis and the application of statistical models for its implementation. It develops a scheme for computing sample sizes when the failures are random. It also introduces statistical models that comprehend correlations among failures that fail due to the same failure mechanism. The idea of correlation is important because semiconductor chips are processed in batches. Also any risk assessment model should comprehend correlations over time. The statistical models developed will provide the required sample sizes for the Failure Analysis lab to state "We are A% confident that B% of future parts will fail due to the same signature." The paper provides tables and graphs for the evaluation of such a risk assessment. The implementation of Signature Analysis will achieve the dual objective of improved customer satisfaction and reduced cycle time. This paper will also highlight it's applicability as well as the essential elements that need to be in place for it to be effective. Different examples have been illustrated of how the concept is being used by Failure Analysis Operations (FA) and Customer Quality and Reliability Engineering groups.

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