Project Title

Evaluating Spatiotemporal Patterns in US Tornado Occurrence with Space Time Pattern Mining: 1950-2019 and 1980-2019

Authors' Affiliations

Darrell L. Wiser, Department of Geosciences, East Tennessee State UNiversity, Johnson City, TN Dr. Ingrid Luffman, Department of Geoscience, East Tennessee State University, Johnson City, TN

Location

Culp Forum 311

Start Date

4-6-2022 1:00 PM

End Date

4-6-2022 2:40 PM

Faculty Sponsor’s Department

Geosciences

Name of Project's Faculty Sponsor

Ingrid Luffman

Additional Sponsors

Dr. Andrew Joyner Mr. William Tollefson

Classification of First Author

Graduate Student-Master’s

Competition Type

Competitive

Type

Oral Presentation

Project's Category

Climatology, Meteorology

Abstract or Artist's Statement

This research assesses shifts in tornado occurrence pattens in space and time employing continental United States tornado records with an Enhanced Fujita (EF) rating equal or greater than 1. In similar research, most researchers discard tornado records prior to 1980 due to factors including: magnitude anomalies related to development of the Fujita Scale, unpredictability in tornado reporting (escalating populace, storm spotters, and technologic improvements), and better data records from the Census Bureau. We therefore constructed two datasets using tornados recorded in the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database: 1950-2019 (dataset 1) and 1980-2019 (dataset 2). The goals for this study were to 1) determine whether spatiotemporal patterns of recorded tornado activity have shifted over time, and 2) determine whether inclusion of pre-1980 tornado data changes the findings from 1). This study employed Space-Time Pattern Mining (STPM) to construct four spacetime cubes (STC) in ArcGIS Pro. Emerging Hot Spot Analysis (EHS) was employed to identify the changes in tornado occurrence (number of incidents in a STC cell) and magnitude (sum of tornado EF ratings for all incidents in a STC cell). EHS displayed increased tornado activity in the Southeast and decreased activity for areas in the Great Plains for both occurrence and magnitude in both datasets. This is interpreted as significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift for both datasets. Similar findings for both datasets indicate that inclusion of the less reliable pre-1980’s tornado data does not change the results and we recommend that the practice of discarding pre-1980’s tornado data in tornado occurrence research be reconsidered.

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Apr 6th, 1:00 PM Apr 6th, 2:40 PM

Evaluating Spatiotemporal Patterns in US Tornado Occurrence with Space Time Pattern Mining: 1950-2019 and 1980-2019

Culp Forum 311

This research assesses shifts in tornado occurrence pattens in space and time employing continental United States tornado records with an Enhanced Fujita (EF) rating equal or greater than 1. In similar research, most researchers discard tornado records prior to 1980 due to factors including: magnitude anomalies related to development of the Fujita Scale, unpredictability in tornado reporting (escalating populace, storm spotters, and technologic improvements), and better data records from the Census Bureau. We therefore constructed two datasets using tornados recorded in the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database: 1950-2019 (dataset 1) and 1980-2019 (dataset 2). The goals for this study were to 1) determine whether spatiotemporal patterns of recorded tornado activity have shifted over time, and 2) determine whether inclusion of pre-1980 tornado data changes the findings from 1). This study employed Space-Time Pattern Mining (STPM) to construct four spacetime cubes (STC) in ArcGIS Pro. Emerging Hot Spot Analysis (EHS) was employed to identify the changes in tornado occurrence (number of incidents in a STC cell) and magnitude (sum of tornado EF ratings for all incidents in a STC cell). EHS displayed increased tornado activity in the Southeast and decreased activity for areas in the Great Plains for both occurrence and magnitude in both datasets. This is interpreted as significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift for both datasets. Similar findings for both datasets indicate that inclusion of the less reliable pre-1980’s tornado data does not change the results and we recommend that the practice of discarding pre-1980’s tornado data in tornado occurrence research be reconsidered.