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COMMENTS In general, kernel density estimation is a non- Comments on “Tornado parametric method of estimating the underlying probability density function (PDF) of a finite dataset. Risk Analysis: Is Dixie This is done by choosing a weighting function, or kernel, a measure of an area subject to the weight- Alley an Extension of ing function (this measure is often called the kernel Tornado Alley? ” bandwidth or simply bandwidth) and then applying the weighting function to the finite dataset over the prescribed area. A kernel can be any function K(u) —P tricka t . Marsh that is nonnegative, real valued, integrable, and satis- School of Meteorology, University of Oklahoma, fies (Wilks 2006) Norman, Oklahoma Cooperative Institute for Mesoscale Meteorological Studies, ∫ K(u) du = 1 (1) Norman, Oklahoma and NOAA/National Severe Storms Laboratory, Norman, Oklahoma K(–u) = K(u), "u. (2) —h oa lrd E. B oork s Several common kernels exist and include those NOAA/National Severe Storms Laboratory, c ho s e n by BDK03 a nd DMC A11. BDK03 u s e d Norman, Oklahoma a Gaussia n kernel w it h a ba ndw idt h of 120 k m; DMCA11, however, used an Epanechnikov kernel ixon et http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
eISSN
1520-0477
DOI
10.1175/1520-0477-93.3.405
Publisher site
See Article on Publisher Site

Abstract

In general, kernel density estimation is a non- Comments on “Tornado parametric method of estimating the underlying probability density function (PDF) of a finite dataset. Risk Analysis: Is Dixie This is done by choosing a weighting function, or kernel, a measure of an area subject to the weight- Alley an Extension of ing function (this measure is often called the kernel Tornado Alley? ” bandwidth or simply bandwidth) and then applying the weighting function to the finite dataset over the prescribed area. A kernel can be any function K(u) —P tricka t . Marsh that is nonnegative, real valued, integrable, and satis- School of Meteorology, University of Oklahoma, fies (Wilks 2006) Norman, Oklahoma Cooperative Institute for Mesoscale Meteorological Studies, ∫ K(u) du = 1 (1) Norman, Oklahoma and NOAA/National Severe Storms Laboratory, Norman, Oklahoma K(–u) = K(u), "u. (2) —h oa lrd E. B oork s Several common kernels exist and include those NOAA/National Severe Storms Laboratory, c ho s e n by BDK03 a nd DMC A11. BDK03 u s e d Norman, Oklahoma a Gaussia n kernel w it h a ba ndw idt h of 120 k m; DMCA11, however, used an Epanechnikov kernel ixon et

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Mar 1, 2012

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