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A test for network-wide trends in rainfall extremes
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.
Swedish University of Agricultural Sciences, Uppsala.
2012 (English)In: International Journal of Climatology, ISSN 0899-8418, E-ISSN 1097-0088, ISSN 0899-8418, Vol. 32, no 1, 86-94 p.Article in journal (Refereed) Published
Abstract [en]

Temporal trends in meteorological extremes are often examined by first reducing daily data to annual index values, such as the 95th or 99th percentiles. Here, we report how this idea can be elaborated to provide an efficient test for trends at a network of stations. The initial step is to make separate estimates of tail probabilities of precipitation amounts for each combination of station and year by fitting a generalised Pareto distribution (GPD) to data above a user-defined threshold. The resulting time series of annual percentile estimates are subsequently fed into a multivariate Mann-Kendall (MK) test for monotonic trends. We performed extensive simulations using artificially generated precipitation data and noted that the power of tests for temporal trends was substantially enhanced when ordinary percentiles were substituted for GPD percentiles. Furthermore, we found that the trend detection was robust to misspecification of the extreme value distribution. An advantage of the MK test is that it can accommodate non-linear trends, and it can also take into account the dependencies between stations in a network. To illustrate our approach, we used long time series of precipitation data from a network of stations in The Netherlands.

Place, publisher, year, edition, pages
Wiley , 2012. Vol. 32, no 1, 86-94 p.
Keyword [en]
climate extremes; precipitation; temporal trend; generalised Pareto distribution; climate indices; global warming
National Category
Climate Research Probability Theory and Statistics
URN: urn:nbn:se:liu:diva-63099DOI: 10.1002/joc.2263ISI: 000298733800007OAI: diva2:376776
funding agencies|Swedish Environmental Protection Agency||Available from: 2010-12-13 Created: 2010-12-10 Last updated: 2012-02-27
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.
Linköping Studies in Arts and Science, ISSN 0282-9800 ; 536Linköping Studies in Statistics, ISSN 1651-1700 ; 13
Klimatförändringar, matematiska modeller
National Category
Probability Theory and Statistics
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)
Available from: 2011-10-10 Created: 2011-08-30 Last updated: 2014-10-07Bibliographically approved

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