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Diagnostics for tail dependence in time-lagged random fields of precipitation
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
2013 (English)In: Journal of Theoretical and Applied Climatology, ISSN 0177-798X, E-ISSN 1434-4483, Vol. 112, no 3-4, 629-636 p.Article in journal (Refereed) Published
Abstract [en]

Weather extremes often occur along fronts passing different sites with some time lag. Here, we show how such temporal patterns can be taken into account when exploring inter-site dependence of extremes. We incorporate time lags into existing models and into measures of extremal associations and their relation to the distance between the investigated sites. Furthermore, we define summarizing parameters that can be used to explore tail dependence for a whole network of stations in the presence of fixed or stochastic time lags. Analysis of hourly precipitation data from Sweden showed that our methods can prevent underestimation of the strength and spatial extent of tail dependencies.

Place, publisher, year, edition, pages
2013. Vol. 112, no 3-4, 629-636 p.
Keyword [en]
Precipitation; Sub-daily; Tail dependence; Spatial dependence; Time lag
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-71298DOI: 10.1007/s00704-012-0748-1ISI: 000318246300022OAI: oai:DiVA.org:liu-71298DiVA: diva2:447075
Available from: 2011-10-10 Created: 2011-10-10 Last updated: 2017-12-08Bibliographically approved
In thesis
1. Characterizing Temporal Changes and Inter-Site Correlations in Daily and Sub-Daily Precipitation Extremes
Open this publication in new window or tab >>Characterizing Temporal Changes and Inter-Site Correlations in Daily and Sub-Daily Precipitation Extremes
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Information on weather extremes is essential for risk awareness in planning of infrastructure and agriculture, and it may also playa key role in our ability to adapt to recurrent or more or less unique extreme events. This thesis reports new statistical methodologies that can aid climate risk assessment under conditions of climate change. This increasing access to high temporal resolution of data is a central factor when developing novel techniques for this purpose. In particular, a procedure is introduced for analysis of long-term changes in daily and sub-daily records of observed or modelled weather extremes. Extreme value theory is employed to enhance the power of the proposed statistical procedure, and inter-site dependence is taken into account to enable regional analyses. Furthermore, new methods are presented to summarize and visualize spatial patterns in the temporal synchrony and dependence of weather events such as heavy precipitation at a network of meteorological stations. The work also demonstrates the significance of accounting for temporal synchrony in the diagnostics of inter-site asymptotic dependence.

Abstract [sv]

Information om extrema väderhändelser är väsentligt för riskmedveten planering av infrastruktur och jordbruk. Sådan information kan också spela en avgörande roll för vår förmåga att anpassa oss till extremhändelser som är regelbundet återkommande eller mer eller mindre unika. Denna avhandling beskriver nya statistiska metoder som kan bidra till att bedöma klimatrisker när klimatet förändras. Den ökande tillgången till data med hög tidsupplösning är en central faktor när vi utvecklar nya tekniker för detta ändamål. Speciellt introducerar vi metodik för att analysera långsiktiga förändringar i dagliga eller ännu mera högupplösta data avseende observerade eller modellerade väderextremer. Extremvärdesteori utnyttias för att öka styrkan av statistiska tester, och hänsyn tas till beroendet mellan data från olika platser så att regionala bedömningar blir möjliga. Vidare presenteras nya metoder för att sammanfatta och visualisera rumsliga mönster i samstämmigheten av och beroendet mellan extrema väderhändelser såsom hög nederbördsintensitet i ett nätverk av meteorologiska stationer. Arbetet visar också betydelsen av ta hänsyn till den tidsmässiga överensstämmelsen när man undersöker det asymptotiska beroendet mellan extrema händelser vid olika stationer.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2011. 53 p.
Series
Linköping Studies in Arts and Science, ISSN 0282-9800 ; 536Linköping Studies in Statistics, ISSN 1651-1700 ; 13
Keyword
Klimatförändringar, matematiska modeller
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-70282 (URN)978-91-7393-110-6 (ISBN)
Public defence
2011-10-10, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2011-10-10 Created: 2011-08-30 Last updated: 2014-10-07Bibliographically approved

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Burauskaite-Harju, AgneGrimvall, Anders

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