Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Statistical methods for assessing and analysing the building performance in respect to the future climate
SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.ORCID-id: 0000-0002-6495-1038
2012 (engelsk)Inngår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 53, s. 107-118Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways. Increased structural loads from wind and water, changes in energy need and decreased moisture durability of materials are some examples of the consequences. Future climate conditions are simulated by global climate models (GCMs). Downscaling by regional climate models (RCMs) provides weather data with suitable temporal and spatial resolutions for direct use in building simulations. There are two major challenges when the future climate data are used in building simulations. The first is to handle and analyse the huge amount of data. The second challenge is to assess the uncertainties in building simulations as a consequence of uncertainties in the future climate data. In this paper two statistical methods, which have been adopted from climatology, are introduced. Applications of the methods are illustrated by looking into two uncertainty factors of the future climate; operating RCMs at different spatial resolutions and with boundary data from different GCMs. The Ferro hypothesis is introduced as a nonparametric method for comparing data at different spatial resolutions. The method is quick and subtle enough to make the comparison. The parametric method of decomposition of variabilities is described and its application in data assessment is shown by considering RCM data forced by different GCMs. The method enables to study data and its variations in different time scales. It provides a useful summary about data and its variations which makes the comparison between several data sets easier. (C) 2012 Elsevier Ltd. All rights reserved.

sted, utgiver, år, opplag, sider
2012. Vol. 53, s. 107-118
Emneord [en]
Climate change, Building simulation, Statistical methods, Climate uncertainties, Decomposition of variabilities
HSV kategori
Forskningsprogram
Klimat
Identifikatorer
URN: urn:nbn:se:smhi:diva-456DOI: 10.1016/j.buildenv.2012.01.015ISI: 000302436200012OAI: oai:DiVA.org:smhi-456DiVA, id: diva2:806297
Tilgjengelig fra: 2015-04-20 Laget: 2015-04-14 Sist oppdatert: 2017-12-04bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Kjellström, Erik

Søk i DiVA

Av forfatter/redaktør
Kjellström, Erik
Av organisasjonen
I samme tidsskrift
Building and Environment

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 109 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.7
|