Málstofa í tölfræði
Speaker: Óli Páll Geirsson, Doctoral student, Department of Mathematics, Faculty of Physical Sciences, University of Iceland.
Title: Modelling annual maximum 24 hour precipitation in Iceland using the SPDE approach and split sampling MCMC strategy
Location: Room V-157 in building VR-II on the UI campus
Time: Thursday, March 20th 2014, at 12:00 to 13:00.
Abstract: To obtain predictions of distributional properties of extreme precipitation on a high resolution grid both observations and outputs from climate models are relied upon. A method for quantile predictions of extreme precipitation which combines these two sources is developed. The method is based on observations of annual maximum 24 hour precipitation from 86 observation sites in Iceland and outputs from a climate model on a 1 km by 1 km regular grid.
A covariate based on the climate model which represents the intensity of precipitation can be computed at each grid point. A latent Gaussian model is built for the observations with the intensity measure as a covariate which in turn allows for spatial predictions on the high resolution grid. The observations are assumed to follow the generalized extreme value distribution. In order to make fast continuous spatial predictions, SPDE spatial models are implemented at the latent level of the model for both the location and scale parameters of the proposed data distribution.
A novel MCMC strategy called split sampling, is implemented for model inference. The strategy greatly improves the mixing and convergence properties of the MCMC sampler. Inferring the parameters of the model yields posterior estimates for the distribution of maximum annual 24 hour precipitation in every grid point. In particular, a quantile estimate can be given at every grid point.Málstofa í tölfræði
Speaker: Óli Páll Geirsson, Doctoral student, Department of Mathematics, Faculty of Physical Sciences, University of Iceland.
Title: Modelling annual maximum 24 hour precipitation in Iceland using the SPDE approach and split sampling MCMC strategy
Location: Room V-157 in building VR-II on the UI campus
Time: Thursday, March 20th 2014, at 12:00 to 13:00.
Abstract: To obtain predictions of distributional properties of extreme precipitation on a high resolution grid both observations and outputs from climate models are relied upon. A method for quantile predictions of extreme precipitation which combines these two sources is developed. The method is based on observations of annual maximum 24 hour precipitation from 86 observation sites in Iceland and outputs from a climate model on a 1 km by 1 km regular grid.
A covariate based on the climate model which represents the intensity of precipitation can be computed at each grid point. A latent Gaussian model is built for the observations with the intensity measure as a covariate which in turn allows for spatial predictions on the high resolution grid. The observations are assumed to follow the generalized extreme value distribution. In order to make fast continuous spatial predictions, SPDE spatial models are implemented at the latent level of the model for both the location and scale parameters of the proposed data distribution.
A novel MCMC strategy called split sampling, is implemented for model inference. The strategy greatly improves the mixing and convergence properties of the MCMC sampler. Inferring the parameters of the model yields posterior estimates for the distribution of maximum annual 24 hour precipitation in every grid point. In particular, a quantile estimate can be given at every grid point.