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Characterizing Temporal Changes and Inter-Site Correlations in Daily and Sub-Daily Precipitation Extremes
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
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 [sv]
Klimatförändringar, matematiska modeller
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-70282ISBN: 978-91-7393-110-6 (print)OAI: oai:DiVA.org:liu-70282DiVA: diva2:444192
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
List of papers
1. A test for network-wide trends in rainfall extremes
Open this publication in new window or tab >>A test for network-wide trends in rainfall extremes
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
Keyword
climate extremes; precipitation; temporal trend; generalised Pareto distribution; climate indices; global warming
National Category
Climate Research Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-63099 (URN)10.1002/joc.2263 (DOI)000298733800007 ()
Note
funding agencies|Swedish Environmental Protection Agency||Available from: 2010-12-13 Created: 2010-12-10 Last updated: 2017-12-11
2. Statistical framework for assessing trends in sub-daily and daily precipitation extremes
Open this publication in new window or tab >>Statistical framework for assessing trends in sub-daily and daily precipitation extremes
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Extreme precipitation events vary with regard to duration, and hence sub-daily data do not necessarily exhibit the same trends as daily data. Here, we present a framework for a comprehensive yet easily undertaken statistical analysis of long-term trends in daily and sub-daily extremes. A parametric peaks-over-threshold model is employed to estimate annual percentiles for data of different temporal resolution. Moreover, a trend-durationfrequency table is used to summarize how the statistical significance of trends in annual percentiles varies with the temporal resolution of the underlying data and the severity of the extremes. The proposed framework also includes nonparametric tests that can integrate information about nonlinear monotonic trends at a network of stations. To illustrate our methodology, we use climate model output data from Kalmar, Sweden, and observational data from Vancouver, Canada. In both these cases, the results show different trends for moderate and high extremes, and also a clear difference in the statistical evidence of trends for daily and sub-daily data.

Keyword
Rainfall extremes; precipitation; sub-daily, temporal trend; generalized Pareto distribution; climate indices; global warming
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-71296 (URN)
Available from: 2011-10-10 Created: 2011-10-10 Last updated: 2011-10-10Bibliographically approved
3. Characterizing and visualizing spatio-temporal patterns in hourly precipitation records
Open this publication in new window or tab >>Characterizing and visualizing spatio-temporal patterns in hourly precipitation records
Show others...
2012 (English)In: Journal of Theoretical and Applied Climatology, ISSN 0177-798X, E-ISSN 1434-4483, Vol. 109, no 3-4, 333-343 p.Article in journal (Refereed) Published
Abstract [en]

We develop new techniques to summarize and visualize spatial patterns of coincidence in weather events such as more or less heavy precipitation at a network of meteorological stations. The cosine similarity measure, which has a simple probabilistic interpretation for vectors of binary data, is generalized to characterize spatial dependencies of events that may reach different stations with a variable time lag. More specifically, we reduce such patterns into three parameters (dominant time lag, maximum cross-similarity, and window-maximum similarity) that can easily be computed for each pair of stations in a network. Furthermore, we visualize such threeparameter summaries by using colour-coded maps of dependencies to a given reference station and distance-decay plots for the entire network. Applications to hourly precipitation data from a network of 93 stations in Sweden illustrate how this method can be used to explore spatial patterns in the temporal synchrony of precipitation events.

Place, publisher, year, edition, pages
Springer, 2012
Keyword
precipitation; hourly rainfall records; spatial dependence; time lag; cosine similarity
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-71297 (URN)10.1007/s00704-011-0574-x (DOI)000307243900002 ()
Note

funding agencies|Swedish Research Council (VR)||Gothenburg Atmospheric Science Centre (GAC)||FORMAS|2007-1048-8700*51|

Available from: 2011-10-10 Created: 2011-10-10 Last updated: 2017-12-08Bibliographically approved
4. Diagnostics for tail dependence in time-lagged random fields of precipitation
Open this publication in new window or tab >>Diagnostics for tail dependence in time-lagged random fields of precipitation
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.

Keyword
Precipitation; Sub-daily; Tail dependence; Spatial dependence; Time lag
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-71298 (URN)10.1007/s00704-012-0748-1 (DOI)000318246300022 ()
Available from: 2011-10-10 Created: 2011-10-10 Last updated: 2017-12-08Bibliographically approved

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