Space-Time Modelling of Rainfall for Continuous Simulation

Chandler, Richard E.; Isham, Valerie; Bellone, Enrica; Yang, Chuanguo; Northrop, Paul J.

doi:10.1201/9781420011050.ch5

Cited by 11 publications

(12 citation statements)

References 20 publications

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“…Similar structures arise in the analysis of multiple time series from different spatial locations (e.g. Chandler et al 2006); here, each time point contributes a cluster of dependent observations and, providing the time series structure is modelled appropriately, these clusters can be treated as independent.…”

Section: Introductionmentioning

confidence: 86%

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Inference for clustered data using the independence loglikelihood

2007

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Independence estimating equations (IEEs) are commonly used for the analysis of clustered data. Here, we use the properties of IEEs to adjust the "independence" log-likelihood function in the presence of clustering. The proposed adjustment relies on the robust sandwich estimator of the parameter covariance matrix, which is easy to calculate. We show that the adjusted log-likelihood competes favourably with a number of established techniques based on IEEs, and provide some insight as to why this is so. The adjustment is applied to practical examples relating to the modelling of wind speed in Europe, and annual maximum temperatures in the UK.

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Section: Introductionmentioning

confidence: 86%

“…Suppose now that the data are generated from the distribution with θ = θ 0 and α = α 0 . Then it can be shown (Chandler et al, 2006) that under very general conditions, E U…”

Section: Independence Estimating Equationsmentioning

confidence: 99%

Inference for clustered data using the independence loglikelihood

2007

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“…Wilks and Wilby [] and Chandler et al . [] review the earlier literature. The emphasis on rare events means that extreme‐value statistics [ Coles , ; Beirlant et al ., ] are widely used to estimate return levels and associated quantities.…”

Section: Introductionmentioning

confidence: 99%

Threshold modeling of extreme spatial rainfall

Thibaud

Mutzner

Davison

2013

Water Resources Research

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[1] We propose an approach to spatial modeling of extreme rainfall, based on max-stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models are coherent with classical extreme-value theory and allow the consistent treatment of spatial dependence of rainfall using ideas related to those of classical geostatistics. We illustrate the ideas through data from the Val Ferret watershed in the Swiss Alps, based on daily cumulative rainfall totals recorded at 24 stations for four summers, augmented by a longer series from nearby. We compare the fits of different statistical models appropriate for spatial extremes, select that best fitting our data, and compare return level estimates for the total daily rainfall over the stations. The method can be used in other situations to produce simulations needed for hydrological models, and in particular, for the generation of spatially heterogeneous extreme rainfall fields over catchments.

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“…Detailed descriptions of the Chenies radar data record and of the spatio-temporal model (architecture, estimation of parameters, and simulations of the fitted model) are documented in [5,55,56,.…”

Section: Introductionmentioning

confidence: 99%