Peter Guttorp

Sea level rise poses a threat to the Norwegian coastal city of Bergen and its historic harbour. The threat could be reduced, but greater flood protection comes at greater cost. And, of course, no one knows for certain how far sea level will rise in future. Decision-makers must therefore decide what to do, and how much to spend, without knowing exactly how bad things could get. Peter Guttorp and Thordis L. Thorarinsdottir explain the problem, and how to deal with the uncertainty

How do people look at art? Are there any differences between how experienced and inexperienced art viewers look at a painting? We approach these questions by analyzing and modeling eye movement data from a cognitive art research experiment, where the eye movements of twenty test subjects, ten experienced and ten inexperienced art viewers, were recorded while they were looking at paintings. Eye movements consist of stops of the gaze as well as jumps between the stops. Hence, the observed gaze stop locations can be thought as a spatial point pattern, which can be modeled by a spatio-temporal point process. We introduce some statistical tools to analyze the spatio-temporal eye movement data, and compare the eye movements of experienced and inexperienced art viewers. In addition, we develop a stochastic model, which is rather simple but fits quite well to the eye movement data, to further investigate the differences between the two groups through functional summary statistics.

Ranking years based on statistical estimates of regional and temporal averages is subject to uncertainty. This uncertainty can in fact be quite substantial and can be described by the rank distribution of an ensemble of such averages. The authors develop a method for estimating it using simulation. The effect of temporal correlation is quite limited in the case studied in this paper: the contiguous United States' annual-mean temperature. The method also allows assessment of derived quantities such as the probability of a given year being one of the 10 warmest in the historical record.

Wind direction plays an important role in the spread of pollutant levels over a geographical region. We discuss how to include wind directional information in the covariance function of spatial models. We follow the spatial convolution approach initially proposed by Higdon and co-authors, wherein a spatial process is described by a convolution between a smoothing kernel and a white noise process. We propose two different ways of accounting for wind direction in the kernel function. For comparison purposes, we also consider a more flexible kernel parametrization, that makes use of latent processes which vary smoothly across the region. Inference procedure follows the Bayesian paradigm, and uncertainty about parameter estimation is naturally accounted for when performing spatial interpolation. We analyze ozone levels observed at a monitoring network in the Northeast of the USA. Sam- ples from the posterior distribution under our proposed models are obtained much faster when compared to the kernel based on latent processes. Our models provide better results, in terms of model fitting and spatial interpolation, when compared to simple isotropic and geometrical anisotropic models. Despite the small number of parameters, our proposed models provide fits which are comparable to those obtained under the kernel based on latent processes.

We compare some different approaches to estimating daily mean temperature (DMT). In many countries, the routine approach is to calculate the average of the directly measured minimum and maximum daily temperature. In some, the maximum and minimum are obtained from hourly measurements. In other countries, temperature readings at specific times throughout the day are taken into account. For example, the Swedish approach uses a linear combination of five temperature readings, including the minimum and the maximum, with coefficients that depend on longitude and month. We first look at data with very high temporal resolution, and compare some different approaches to estimating DMT. Then, we compare the Swedish formula to various averages of the daily minimum and maximum, finding the latter method being substantially less precise. We finally compare the Swedish formula to hourly averages, and find that a recalibrated linear combination improves estimation accuracy. Copyright © 2012 Royal Meteorological Society