Bayesian trend analysis for daily rainfall series of Barcelona

Ortego, M. I.; Gibergans-Báguena, José; Tolosana‐Delgado, Raimon; Egozcue, J. J.; Llasat, M. C.

doi:10.5194/adgeo-26-71-2010

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…The illustration series in this contribution shows no overlap between model hindcast and measured data, and therefore the proposed model does not account for it. An analogous model for overlapping series was presented by Ortego et al (2012), where it was shown that the results are not significantly altered when accounting for overlap, albeit for significant wave height data.…”

Section: Introductionmentioning

confidence: 91%

“…where, for simplicity, {t i }, {τ j } represents all available data on elapsed times between consecutive events. When interruptions of observations are present, as is the case in the buoy data set, some time differences τ j − τ j −1 do not correspond to elapsed times between consecutive events; these time intervals are ignored in the likelihood (see further details in Ortego et al, 2012), i.e. the likelihood (Eq.…”

Section: Occurrence Of Wind Eventsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian trend analysis of extreme wind using observed and hindcast series off Catalan coast, NW Mediterranean Sea

Ortego¹,

Egozcue²,

Tolosana‐Delgado³

2014

Preprint

Self Cite

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Abstract. It has been suggested that climate change might modify the occurrence rate and magnitude of large ocean-wave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere-ocean system. Forecasting models are commonly used to assess these effects, given that good quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behavior, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcasted data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the Peak-Over-Threshold (POT) approach and the Generalized Pareto Distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution. All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcasted and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of 10 min average wind speed at a deep buoy location off the Catalan coast (NE Spain, Western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time are addressed.

show abstract

Section: Introductionmentioning

confidence: 91%

Section: Occurrence Of Wind Eventsmentioning

confidence: 99%

Bayesian trend analysis of extreme wind using observed and hindcast series off Catalan coast, NW Mediterranean Sea

Ortego¹,

Egozcue²,

Tolosana‐Delgado³

2014

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…By the same token, modification of human activities should be considered as an important task for the improvement of flood management. Focusing on another Spanish region (in this case, the coast of Alicante), Olcina Cantos et al (2010) analyze the increased exposure and vulnerability to floods as a result of a major urban occupation of areas that are subject to this hazard, in spite of the existence of flood risk prevention plans. Survey-based research in the areas affected by the most recent episodes reveals a lack of awareness for a large portion of the population, which is particularly evident for the seasonal inhabitants or residents who have not lived in the area for very long.…”

Section: Published By Copernicusmentioning

confidence: 99%

“…As a result, these assessment studies may be highly dependent on the methodologies and data that are used to analyze trends and anomalies, while analyses for different time periods may give different results and lead to ambiguous conclusions. This is the case for the papers by Neppel et al (2011), which deals with a multivariate regional test for the detection of trends in extreme rainfall, and by Ortego et al (2010), which focuses on the application of the Bayesian analysis to detect potential trends in daily rainfall series.…”

Section: Climate Change Impacts On Mediterranean Stormsmentioning

confidence: 99%

Preface: 11th Plinius Conference on Mediterranean Storms

Llasat¹,

Boni²,

Deidda³

et al. 2013

Nat. Hazards Earth Syst. Sci.

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“…In order to take into account the restriction ξ < 0, other parameterisations of GPD in the Weibull domain of attraction have been proposed (Ortego et al, 2010(Ortego et al, , 2012; we adopt here the parameterisation in Ortego et al (2012). We use…”

Section: Event Magnitude Modelmentioning

confidence: 99%

Bayesian trend analysis of extreme wind using observed and hindcast series off the Catalan coast, NW Mediterranean Sea

Ortego

Egozcue

Tolosana‐Delgado

2014

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. It has been suggested that climate change might modify the occurrence rate and magnitude of large oceanwave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere-ocean system. Forecasting models are commonly used to assess these effects, given that good-quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behaviour, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcast data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the peak-over-threshold (POT) approach and the generalized Pareto distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution.All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcast and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of average wind speed at a deep buoy location off the Catalan coast (NE Spain, western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time is addressed.

show abstract

Bayesian trend analysis for daily rainfall series of Barcelona

Cited by 4 publications

References 18 publications

Bayesian trend analysis of extreme wind using observed and hindcast series off Catalan coast, NW Mediterranean Sea

Bayesian trend analysis of extreme wind using observed and hindcast series off Catalan coast, NW Mediterranean Sea

Preface: 11th Plinius Conference on Mediterranean Storms

Bayesian trend analysis of extreme wind using observed and hindcast series off the Catalan coast, NW Mediterranean Sea

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