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Hurricane Florence Makes Landfall in the Southeast USA: Sensitive Dependence on Initial Conditions, Parameterizations, and Integrated Enstrophy

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Atmospheric and Climate Sciences 2020年第2期10卷 101-124页

作者： Sarah S. Balkissoon Jonathan T. Bongard Thomas Cain Dominic M. Candela Corey Clay Bahtiyar Efe Qiuping Ji Emily M. Klaus Alex P. Korner Kenna Mitchell Jane M. Niemeyer Jordan L. Rabinowitz Samuel P. Ritter Katherine Rojas Paula Sumrall Patrick S. Market Anthony R. Lupo Atmospheric Science Program School of Natural Resources University of Missouri Columbia MO USA Department of Meteorological Engineering Samsun University Samsun Turkey

The overall purpose of this paper is to post-evaluate the predictability of Hurricane Florence using the Advanced Research Weather Research Forecast (WRF) (ARW) version of a mesoscale model. This was performed over the period from 0000 UTC 13 September 2018 through 0000 UTC 18 September 2018. The WRF ARW core resolution used here was the 27-km grid spacing chosen to in order to balance finer resolution against in house processing time and storage. The large-scale analysis showed that a change in the Northern Hemisphere flow regime, especially the flow in the western part of the Northern Hemisphere may have contributed partly to the reduced forward speed of the tropical cyclone. In order to measure the predictability of a system, we will use different convective and boundary layer schemes initialized from the same conditions. The results demonstrated that the sign of the local IRE tendency was similar to that of the Northern Hemisphere Integrated Enstrophy. The results also showed that when the boundary layer, convective, and cloud microphysical schemes of the model were varied, the areal coverage of heavy precipitation of Florence was under-forecast by approximately 10% or more, and the heaviest amounts were under-forecast by an average of about 20%.

关键词： Predictability Model Behavior Integrated entrophy Precipitation Tropical Cyclone

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Fingerprint Database Optimization Using Watershed Transformation Algorithm

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Open Journal of Optimization 2014年第4期3卷 59-67页

作者： Owoeye Kolade Ajayi Adedoyin Olayinka Ukorigho Ovie Department of Mathematical Sciences Ekiti State University Ado Ekiti Nigeria

Fingerprints are a unique feature for identification and verification of humans. The need to optimise several databases for storing the images of fingerprints is a major concerning issue. Several segmentation algorithms have been used in the time past but there are still several challenges facing some current segmentation algorithms like computational efficiency. Another challenge is that segmentation procedure can be impractically slow, or requires extremely large amounts of memory. This paper addresses the challenges by employing watershed flooding algorithm on the fingerprint images so as to optimize the sizes of the databases. A pre-processing plug-in that implements this segmentation process is developed using Java. We showed its effectiveness by testing it on fingerprint image dataset and the entropy showed that the segmented images sizes were reduced.

关键词： Watershed Transformation Fingerprint Segmentation entrophy

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