Editorial Type:
Article
Article Type:
Research Article

A Quantitative Analysis of the Enhanced-V Feature in Relation to Severe Weather

Jason C. Brunner Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Steven A. Ackerman Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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A. Scott Bachmeier Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Robert M. Rabin National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Print Publication:
01 Aug 2007
DOI:
https://doi.org/10.1175/WAF1022.1
Page(s):
853–872
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Abstract

Early enhanced-V studies used 8-km ground-sampled distance and 30-min temporal-sampling Geostationary Operational Environmental Satellite (GOES) infrared (IR) imagery. In contrast, the ground-sampled distance of current satellite imagery is 1 km for low earth orbit (LEO) satellite IR imagery. This improved spatial resolution is used to detect and investigate quantitative parameters of the enhanced-V feature. One of the goals of this study is to use the 1-km-resolution LEO data to help understand the impact of higher-resolution GOES data (GOES-R) when it becomes available. A second goal is to use the LEO data available now to provide better severe storm information than GOES when it is available. This study investigates the enhanced-V feature observed with 1-km-resolution satellite imagery as an aid for severe weather warning forecasters by comparing with McCann’s enhanced-V study. Therefore, verification statistics such as the probability of detection, false alarm ratio, and critical success index were calculated. Additionally, the importance of upper-level winds to severe weather occurrence will be compared with that of the quantitative parameters of the enhanced-V feature. The main goal is to provide a basis for the development of an automated detection algorithm for enhanced-V features from the results in this study. Another goal is to examine daytime versus nighttime satellite overpass distributions with the enhanced-V feature.

Corresponding author address: Jason C. Brunner, CIMSS, University of Wisconsin—Madison, Madison, WI 53706. Email: jasonb@ssec.wisc.edu

Abstract

Early enhanced-V studies used 8-km ground-sampled distance and 30-min temporal-sampling Geostationary Operational Environmental Satellite (GOES) infrared (IR) imagery. In contrast, the ground-sampled distance of current satellite imagery is 1 km for low earth orbit (LEO) satellite IR imagery. This improved spatial resolution is used to detect and investigate quantitative parameters of the enhanced-V feature. One of the goals of this study is to use the 1-km-resolution LEO data to help understand the impact of higher-resolution GOES data (GOES-R) when it becomes available. A second goal is to use the LEO data available now to provide better severe storm information than GOES when it is available. This study investigates the enhanced-V feature observed with 1-km-resolution satellite imagery as an aid for severe weather warning forecasters by comparing with McCann’s enhanced-V study. Therefore, verification statistics such as the probability of detection, false alarm ratio, and critical success index were calculated. Additionally, the importance of upper-level winds to severe weather occurrence will be compared with that of the quantitative parameters of the enhanced-V feature. The main goal is to provide a basis for the development of an automated detection algorithm for enhanced-V features from the results in this study. Another goal is to examine daytime versus nighttime satellite overpass distributions with the enhanced-V feature.

Corresponding author address: Jason C. Brunner, CIMSS, University of Wisconsin—Madison, Madison, WI 53706. Email: jasonb@ssec.wisc.edu

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