<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zagouras, A.a b</style></author><author><style face="normal" font="default" size="100%">Argiriou, A.A.a</style></author><author><style face="normal" font="default" size="100%">Economou, G.c</style></author><author><style face="normal" font="default" size="100%">Fotopoulos, S.c</style></author><author><style face="normal" font="default" size="100%">Flocas, H.A.d</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weather maps classification over Greek domain based on isobaric line patterns: A pattern recognition approach</style></title><secondary-title><style face="normal" font="default" size="100%">Theoretical and Applied Climatology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">accuracy assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">classification</style></keyword><keyword><style  face="normal" font="default" size="100%">climate modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">data set</style></keyword><keyword><style  face="normal" font="default" size="100%">geopotential</style></keyword><keyword><style  face="normal" font="default" size="100%">Greece</style></keyword><keyword><style  face="normal" font="default" size="100%">histogram</style></keyword><keyword><style  face="normal" font="default" size="100%">image processing</style></keyword><keyword><style  face="normal" font="default" size="100%">numerical model</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">trend analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?eid=2-s2.0-84887318838&amp;partnerID=40&amp;md5=12d56a09898abbf679d51f06a1ea60c2</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3-4</style></number><volume><style face="normal" font="default" size="100%">114</style></volume><pages><style face="normal" font="default" size="100%">691-704</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The paper presents a semi-supervised weather classification method based on 850-hPa isobaric level maps. A preprocessing step is employed, where isolines of geopotential height are extracted from weather map images via an image processing procedure. Α feature extraction stage follows where two techniques are applied. The first technique implements phase space reconstruction, and yields multidimensional delay distributions. The second technique is based on chain code representation of signals, from which histogram features are derived. Similarity measures are used to compare multidimensional data and the k-means algorithm is applied in the final stage. The method is applied over the area of Greece, and the resulting catalogues are compared to a subjective classification for this area. Numerical experiments with datasets derived from the European Meteorological Bulletin archives exhibit an up to 91 % accurate agreement with the subjective weather patterns. © 2013 Springer-Verlag Wien.</style></abstract><notes><style face="normal" font="default" size="100%">cited By (since 1996)0</style></notes></record></records></xml>