Computationally efficient spatial modeling of annual maximum 24‐h precipitation on a fine grid

Geirsson, Óli Páll; Hrafnkelsson, Birgir; Simpson, Daniel

doi:10.1002/env.2343

Cited by 21 publications

(34 citation statements)

References 26 publications

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“…To accomplish that the following smoothing method was applied, see Geirsson et al. (). For each site, the grid points contained in a circle around the site with radius r = 1 km were found.…”

Section: Data and Covariatesmentioning

confidence: 99%

“…With the sample mean and the logarithm of the sample standard deviation of the Box-Cox transformed monthly precipitation, based on the MM, available in each grid point, covariates based on those values were constructed for each of the 40 sites across Iceland. To accomplish that the following smoothing method was applied, see Geirsson et al (2015). For each site, the grid points contained in a circle around the site with radius r D 1 km were found.…”

Section: Meteorological Covariatesmentioning

confidence: 99%

“…Geirsson et al. () proposed a statistical model with a spatial structure for extreme precipitation that relies on a covariate based on the MM. This covariate represents the annual mean precipitation and serves as an informative predictor.…”

Section: Introductionmentioning

confidence: 99%

“…Cooley et al (2007) presented a method that yields spatial predictions of the distributional properties of extreme precipitation by using a geographical covariate (elevation) and a climatological covariate (mean precipitation) on a grid. Geirsson et al (2015) proposed a statistical model with a spatial structure for extreme precipitation that relies on a covariate based on the MM. This covariate represents the annual mean precipitation and serves as an informative predictor.…”

Section: Introductionmentioning

confidence: 99%

“…This covariate represents the annual mean precipitation and serves as an informative predictor. Here, an approach similar to Cooley et al (2007) and Geirsson et al (2015) is used to model monthly precipitation, namely, meteorological covariates based on the MM are included in the spatial latent fields of the location and scale parameters. The advantage of the proposed BHM lies in the quality of the covariates, which take into account factors such as topology, airflow dynamics, condensed water advection, and downslope evaporation and are available on a fine grid.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian prediction of monthly precipitation on a fine grid using covariates based on a regional meteorological model

Sigurðarson

Hrafnkelsson

2015

Environmetrics

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In this article a Bayesian hierarchical model (BHM) for observed monthly precipitation is proposed. This BHM incorporates covariates based on an output on a fine grid from a regional meteorological model. At the data level of the BHM, the observed monthly precipitation is transformed using the Box–Cox transformation, and each month is modeled separately. To capture spatial correlation at the data level, a Gaussian field with Matérn correlation function is used. It is assumed that the data are subject to measurement error. The location and log‐scale parameters at the latent level are also modeled with Gaussian fields with Matérn correlation functions. An output from a regional meteorological model on a fine grid is used to construct spatial covariates for the latent parameters of the BHM for each month of the year. These covariates are then projected onto each of the observed sites for each month and incorporated into the BHM. Markov chain Monte Carlo simulation is used for posterior inference and Bayesian kriging is used to predict the latent parameters on the grid. This BHM was applied to observed data on monthly precipitation, which come from forty sites across Iceland from the years 1958 to 2006. The data were corrected for wind, wetting, and evaporation loss. An output from a linear model of orographic precipitation defined on a 1 km by 1 km grid over Iceland was used to construct the covariates for the BHM. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Data and Covariatesmentioning

confidence: 99%

Section: Meteorological Covariatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian prediction of monthly precipitation on a fine grid using covariates based on a regional meteorological model

Sigurðarson

Hrafnkelsson

2015

Environmetrics

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Fast and scalable inference for spatial extreme value models

Chen,

Ramezan,

Lysy

2024

Can J Statistics

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The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process (GP) on the GEV parameters. Inference for GEV‐GP models is typically carried out using Markov Chain Monte Carlo (MCMC) methods, or using approximate inference methods such as the integrated nested Laplace approximation (INLA). However, MCMC becomes prohibitively slow as the number of spatial locations increases, whereas INLA is applicable in practice only to a limited subset of GEV‐GP models. In this article, we revisit the original Laplace approximation for fitting spatial GEV models. In combination with a popular sparsity‐inducing spatial covariance approximation technique, we show through simulations that our approach accurately estimates the Bayesian predictive distribution of extreme weather events, is scalable to several thousand spatial locations, and is several orders of magnitude faster than MCMC. A case study in forecasting extreme snowfall across Canada is presented.

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Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change

et al. 2016

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dThis paper describes a statistical modelling framework for the characterisation of rainfall extremes over a region of interest. Using a Bayesian hierarchical approach, the data are assumed to follow the generalised extreme value distribution, whose parameters are modelled as spatial Gaussian processes in the latent process layer. We also integrate a parametric relationship between precipitation maxima accumulated over increasing durations. The inference of the model parameters is thus improved by pooling information across both space and accumulation duration. In addition, we propose and investigate two different approaches for the integration of daily and sub-daily rainfall data within the framework. We also demonstrate how information from a regional climate model can be integrated to enable the investigation of future projections of extreme rainfall characteristics. We apply the proposed methodology to precipitation datasets from two large-scale study regions located on the east coast of Australia. The models are fitted using Markov chain Monte Carlo simulations, and we present estimated model parameters and posterior inferences of return levels at various durations and sites of interest. We demonstrate the effectiveness of the framework in spatially extrapolating the inference to locations other than those at which direct rainfall measurements are available. We also provide comparisons between rainfall extremes at various durations obtained for the current climate and those based on future projections from a regional climate model. Both methods proposed for the integration of daily and sub-daily records were found to yield similar results in terms of model performance and computational requirements.

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Computationally efficient spatial modeling of annual maximum 24‐h precipitation on a fine grid

Cited by 21 publications

References 26 publications

Bayesian prediction of monthly precipitation on a fine grid using covariates based on a regional meteorological model

Bayesian prediction of monthly precipitation on a fine grid using covariates based on a regional meteorological model

Fast and scalable inference for spatial extreme value models

Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change

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