The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statistics

Maraun, Douglas; Rust, Henning W.; Osborn, Timothy J.

doi:10.1002/joc.1811

Cited by 54 publications

(64 citation statements)

References 31 publications

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“…Like most climatic variables, however, precipitation undergoes an annual cycle. In the present work, seasonality is taken into account by dividing the year in 3-month bins, and considering them separately, rather than modeling it explicitly, as done in several recent studies (Katz et al 2002;Maraun et al 2009;Rust et al 2009;Umbricht et al 2013). No attempt was made to account for the daily cycle of hourly precipitation.…”

Section: Imt Selection and Gpd Estimatesmentioning

confidence: 99%

Automatic threshold and run parameter selection: a climatology for extreme hourly precipitation in Switzerland

Fukutome

Liniger

Süveges

2014

Theor Appl Climatol

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Extreme value analyses of a large number of relatively short time series are in increasing demand in environmental sciences and design. Here, we present an automated procedure for the peaks-over-threshold (POT) approach to extreme value theory and use it to provide a climatology of extreme hourly precipitation in Switzerland. The POT approach fits the generalized Pareto distribution (GPD) to independent exceedances above some high threshold. To guarantee independence, the time series is pruned: exceedances separated by less than a fixed interval called the run parameter are considered a cluster, and all but the cluster maxima are discarded. We propose the automation of an existing graphical method for joint selection of threshold and run parameter. Hourly precipitation is analyzed at 59 stations of the MeteoSwiss observational network over the period 1981-2010. The four seasons are considered separately. When necessary, a simple detrending is applied. Results suggest that unnecessarily large run parameters have adverse effects on the estimation of the GPD parameters. The proposed method yields mean cluster sizes that reflect the seasonal and geographical variation of lag dependence of hourly precipitation. The climatology, as represented by the return level maps and Alpine cross-section, mirror known aspects of the Swiss climate. Unlike for daily precipitation, summer thunderstorm tracks are visible in the seasonal frequency of events, rather than in the amplitude of rare events.

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Section: Imt Selection and Gpd Estimatesmentioning

confidence: 99%

Automatic threshold and run parameter selection: a climatology for extreme hourly precipitation in Switzerland

Fukutome

Liniger

Süveges

2014

Theor Appl Climatol

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“…the precipitation or discharge values associated to relevant and unfrequent events, will likely be dependent on the Correspondence to: P. Allamano (paola.allamano@polito.it) non-homogeneity of their respective date of occurrence (e.g. Maraun et al, 2009). The seasonal variability of extreme events represents an issue for the standard applications of the frequency analysis, because the presence of seasonality can make some commonly used assumptions inappropriate.…”

Section: Introductionmentioning

confidence: 99%

Effects of disregarding seasonality on the distribution of hydrological extremes

Allamano

Laio

Claps

2011

Hydrol. Earth Syst. Sci.

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Abstract. This paper deals with the seasonality of hydroclimatic extremes and with the problem of accounting for their non-homogeneous character in determining the design value. To this aim we devise a simple stochastic experiment in which extremes are produced by a non-homogeneous extreme value generation process. The design values are estimated in closed analytical form both in a peak over threshold framework and by using the standard annual maxima approach. In this completely controlled world of generated hydrological extremes, a statistical measure of the error associated to the adoption of a homogeneous model is introduced. The sensitivity of this measure, named return period ratio, to the typology and strength of seasonality is investigated. We find that neglecting seasonality induces a downward bias in design value estimators. The magnitude of the bias may be large when the peak over threshold approach is adopted, while the return period distortion is limited when the annual maxima are considered. An application to rainfall data from a 30 000 km 2 region located in North-Western Italy is presented to better clarify the effects of disregarding seasonality in a real case.

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“…Furthermore, if applied to more than one gauge within a grid box, the predicted subgrid spatial structure is completely deterministic. As a consequence, the spatial extent of dry areas and extreme events might be heavily overestimated as well (Maraun 2013).…”

Section: General Approachmentioning

confidence: 99%

“…This representativeness problem between grid and point scale cannot be overcome by traditional bias correction methods, because they are deterministic (i.e., they only correct systematic biases but do not add random smallscale variability). Any deterministic MOS approach aiming to correct the simulated variability, such as variance correction or quantile mapping (Piani et al 2010), if used for downscaling below gridbox scale may consequently produce wrong variability and trends (Maraun 2013). For PP downscaling, von Storch (1999) suggested randomization to add the necessary subgrid small-scale variability to a downscaled time series.…”

Section: Introductionmentioning

confidence: 99%

“…Precipitation intensities are described by a mixture model (Frigessi et al 2002;Vrac and Naveau 2007); that is, moderate precipitation is modeled by a gamma distribution and the extreme tail is modeled by a generalized Pareto (GP) distribution. The dependence of the model parameters on the simulated RCM precipitation is modeled by a vector generalized linear model (Yee and Wild 1996;Yee and Stephenson 2007;Maraun et al 2010aMaraun et al , 2011. In its current version, our model does not explicitly account for spatial dependencies (beyond the dependency imprinted by the RCM); that is, it is a single site model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes

et al. 2014

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Precipitation is highly variable in space and time; hence, rain gauge time series generally exhibit additional random small-scale variability compared to area averages. Therefore, differences between daily precipitation statistics simulated by climate models and gauge observations are generally not only caused by model biases, but also by the corresponding scale gap. Classical bias correction methods, in general, cannot bridge this gap; they do not account for small-scale random variability and may produce artifacts. Here, stochastic model output statistics is proposed as a bias correction framework to explicitly account for random small-scale variability. Daily precipitation simulated by a regional climate model (RCM) is employed to predict the probability distribution of local precipitation. The pairwise correspondence between predictor and predictand required for calibration is ensured by driving the RCM with perfect boundary conditions. Wet day probabilities are described by a logistic regression, and precipitation intensities are described by a mixture model consisting of a gamma distribution for moderate precipitation and a generalized Pareto distribution for extremes. The dependence of the model parameters on simulated precipitation is modeled by a vector generalized linear model. The proposed model effectively corrects systematic biases and correctly represents local-scale random variability for most gauges. Additionally, a simplified model is considered that disregards the separate tail model. This computationally efficient model proves to be a feasible alternative for precipitation up to moderately extreme intensities. The approach sets a new framework for bias correction that combines the advantages of weather generators and RCMs.

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The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statistics

Cited by 54 publications

References 31 publications

Automatic threshold and run parameter selection: a climatology for extreme hourly precipitation in Switzerland

Automatic threshold and run parameter selection: a climatology for extreme hourly precipitation in Switzerland

Effects of disregarding seasonality on the distribution of hydrological extremes

Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes

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