Variability of horizontal temperature fluxes over the Arctic
We used ERA-Interim reanalysis data to perform a pattern analysis of the tropospheric mean meridional temperature flux in the Northern Hemisphere exploiting an artificial neural network called self organizing map (SOM). The basic explanation of the neural network will be given for a better understan...
Main Authors: | , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Universität Leipzig
2017
|
Subjects: | |
Online Access: | https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-167700 https://ul.qucosa.de/id/qucosa%3A16770 https://ul.qucosa.de/api/qucosa%3A16770/attachment/ATT-0/ |
id |
ftunivleipzig:oai:qucosa:de:qucosa:16770 |
---|---|
record_format |
openpolar |
spelling |
ftunivleipzig:oai:qucosa:de:qucosa:16770 2023-09-05T13:17:09+02:00 Variability of horizontal temperature fluxes over the Arctic Mewes, Daniel Jacobi, Christoph 2017 https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-167700 https://ul.qucosa.de/id/qucosa%3A16770 https://ul.qucosa.de/api/qucosa%3A16770/attachment/ATT-0/ eng eng Universität Leipzig urn:nbn:de:bsz:15-qucosa2-167380 qucosa:16738 urn:nbn:de:bsz:15-qucosa2-167700 https://ul.qucosa.de/id/qucosa%3A16770 https://ul.qucosa.de/api/qucosa%3A16770/attachment/ATT-0/ info:eu-repo/semantics/openAccess Temperaturfluß Nordatlantik Arktis temperature flux North Atlantic Arctic info:eu-repo/classification/ddc/551 ddc:551 info:eu-repo/semantics/acceptedVersion doc-type:article info:eu-repo/semantics/article doc-type:Text 2017 ftunivleipzig 2023-08-11T13:58:26Z We used ERA-Interim reanalysis data to perform a pattern analysis of the tropospheric mean meridional temperature flux in the Northern Hemisphere exploiting an artificial neural network called self organizing map (SOM). The basic explanation of the neural network will be given for a better understanding of the presented result. The neural network provides an analyses of the given data in terms of a decomposition into distinct patterns. The results confirms that the strongest fluxes occur over the North Atlantic. Additionally, the SOM showed that in general fluxes over the North Atlantic are most common over all analyzed winters. Wir verwendeten ERA-Interim Reanalysedaten. Dabei wurde für eine Analyse des über die Troposphäre gemittelten Temperaturflusses ein künstliches neuronales Netzwerks namens Selbstorganisierende Karte (Self Organizing Map, SOM) benutzt. Das neuronale Netzwerk hilft dabei den Datensatz in bestimmte Muster zu unterteilen. Die Ergebnisse bestätigen, dass die größten Flüsse über dem Nordatlantik in die Arktis vordringen. Weiterhin zeigt sich mithilfe der SOM-Methode, dass im Allgemeinen für den analysierten Zeitraum Flüsse über dem Nordatlantik häufiger sind als andere Pfade in Richtung Arktis. Article in Journal/Newspaper Arctic Arktis Arktis* Atlantic Arctic Atlantic-Arctic North Atlantic Universität Leipzig: Qucosa Arctic |
institution |
Open Polar |
collection |
Universität Leipzig: Qucosa |
op_collection_id |
ftunivleipzig |
language |
English |
topic |
Temperaturfluß Nordatlantik Arktis temperature flux North Atlantic Arctic info:eu-repo/classification/ddc/551 ddc:551 |
spellingShingle |
Temperaturfluß Nordatlantik Arktis temperature flux North Atlantic Arctic info:eu-repo/classification/ddc/551 ddc:551 Mewes, Daniel Jacobi, Christoph Variability of horizontal temperature fluxes over the Arctic |
topic_facet |
Temperaturfluß Nordatlantik Arktis temperature flux North Atlantic Arctic info:eu-repo/classification/ddc/551 ddc:551 |
description |
We used ERA-Interim reanalysis data to perform a pattern analysis of the tropospheric mean meridional temperature flux in the Northern Hemisphere exploiting an artificial neural network called self organizing map (SOM). The basic explanation of the neural network will be given for a better understanding of the presented result. The neural network provides an analyses of the given data in terms of a decomposition into distinct patterns. The results confirms that the strongest fluxes occur over the North Atlantic. Additionally, the SOM showed that in general fluxes over the North Atlantic are most common over all analyzed winters. Wir verwendeten ERA-Interim Reanalysedaten. Dabei wurde für eine Analyse des über die Troposphäre gemittelten Temperaturflusses ein künstliches neuronales Netzwerks namens Selbstorganisierende Karte (Self Organizing Map, SOM) benutzt. Das neuronale Netzwerk hilft dabei den Datensatz in bestimmte Muster zu unterteilen. Die Ergebnisse bestätigen, dass die größten Flüsse über dem Nordatlantik in die Arktis vordringen. Weiterhin zeigt sich mithilfe der SOM-Methode, dass im Allgemeinen für den analysierten Zeitraum Flüsse über dem Nordatlantik häufiger sind als andere Pfade in Richtung Arktis. |
format |
Article in Journal/Newspaper |
author |
Mewes, Daniel Jacobi, Christoph |
author_facet |
Mewes, Daniel Jacobi, Christoph |
author_sort |
Mewes, Daniel |
title |
Variability of horizontal temperature fluxes over the Arctic |
title_short |
Variability of horizontal temperature fluxes over the Arctic |
title_full |
Variability of horizontal temperature fluxes over the Arctic |
title_fullStr |
Variability of horizontal temperature fluxes over the Arctic |
title_full_unstemmed |
Variability of horizontal temperature fluxes over the Arctic |
title_sort |
variability of horizontal temperature fluxes over the arctic |
publisher |
Universität Leipzig |
publishDate |
2017 |
url |
https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-167700 https://ul.qucosa.de/id/qucosa%3A16770 https://ul.qucosa.de/api/qucosa%3A16770/attachment/ATT-0/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arktis Arktis* Atlantic Arctic Atlantic-Arctic North Atlantic |
genre_facet |
Arctic Arktis Arktis* Atlantic Arctic Atlantic-Arctic North Atlantic |
op_relation |
urn:nbn:de:bsz:15-qucosa2-167380 qucosa:16738 urn:nbn:de:bsz:15-qucosa2-167700 https://ul.qucosa.de/id/qucosa%3A16770 https://ul.qucosa.de/api/qucosa%3A16770/attachment/ATT-0/ |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1776198436225286144 |