arXiv subjects

Christopher A. T. Ferro

Publications and source records attributed to Christopher A. T. Ferro.

4 recordsLinked to original sources

arXiv2025

Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules

Accurate forecasts of extreme wind speeds are of high importance for many applications. Such forecasts are usually generated by ensembles of numerical weather prediction (NWP) models, which however can be biased and have errors in dispersion, thus necessitating the application of statistical post-processing techniques. In this work we aim to improve statistical post-processing models for probabilistic predictions of extreme wind speeds. We do this by adjusting the training procedure used to fit ensemble model output statistics (EMOS) models - a commonly applied post-processing technique - and propose estimating parameters using the so-called threshold-weighted continuous ranked probability score (twCRPS), a proper scoring rule that places special emphasis on predictions over a threshold. We show that training using the twCRPS leads to improved extreme event performance of post-processing models for a variety of thresholds. We find a distribution body-tail trade-off where improved performance for probabilistic predictions of extreme events comes with worse performance for predictions of the distribution body. However, we introduce strategies to mitigate this trade-off based on weighted training and linear pooling. Finally, we consider some synthetic experiments to explain the training impact of the twCRPS and derive closed-form expressions of the twCRPS for a number of distributions, giving the first such collection in the literature. The results will enable researchers and practitioners alike to improve the performance of probabilistic forecasting models for extremes and other events of interest.

cs.LG↗

arXiv2015

Evaluating ensemble forecasts by the Ignorance score -- Correcting the finite-ensemble bias

This study considers the application of the Ignorance Score (also known as the Logarithmic Score) in the context of ensemble verification. In particular, we consider the case where an ensemble forecast is transformed to a Normal forecast distribution, and this distribution is evaluated by the Ignorance Score. It is shown that the standard Ignorance score is biased with respect to the ensemble size, such that larger ensembles yield systematically better expected scores. A new estimator of the Ignorance score is derived which is unbiased with respect to the ensemble size. In an application to seasonal climate predictions it is shown that the standard Ignorance score assigns better expected scores to simple climatological ensembles or biased ensembles that have many members, than to physical dynamical and unbiased ensembles with fewer members. By contrast, the new bias-corrected Ignorance score ranks the physical dynamical and unbiased ensembles better than the climatological and biased ones, independent of ensemble size. It is shown that the unbiased estimator has smaller estimator variance and error than the standard estimator, and that it is a fair verification score, which is optimized if the ensemble members are statistically consistent with the observations. The finite ensemble bias of ensemble verification scores is discussed more broadly. It is argued that a bias-correction is appropriate when forecast systems with different ensemble sizes are compared, and when an evaluation of the underlying distribution of the ensemble is of interest; possible applications to unbiased parameter estimation are discussed.

stat.ME↗

arXiv2015

Spatio-temporal modelling of extreme storms

A flexible spatio-temporal model is implemented to analyse extreme extra-tropical cyclones objectively identified over the Atlantic and Europe in 6-hourly re-analyses from 1979-2009. Spatial variation in the extremal properties of the cyclones is captured using a 150 cell spatial regularisation, latitude as a covariate, and spatial random effects. The North Atlantic Oscillation (NAO) is also used as a covariate and is found to have a significant effect on intensifying extremal storm behaviour, especially over Northern Europe and the Iberian peninsula. Estimates of lower bounds on minimum sea-level pressure are typically 10-50 hPa below the minimum values observed for historical storms with largest differences occurring when the NAO index is positive.

stat.AP↗

arXiv2008

A Sliding Blocks Estimator for the Extremal Index

In extreme value statistics for stationary sequences, blocks estimators are usually constructed by using disjoint blocks because exceedances over high thresholds of different blocks can be assumed asymptotically independent. In this paper we focus on the estimation of the extremal index which measures the degree of clustering of extremes. We consider disjoint and sliding blocks estimators and compare their asymptotic properties. In particular we show that the sliding blocks estimator is more efficient than the disjoint version and has a smaller asymptotic bias. Moreover we propose a method to reduce its bias when considering sufficiently large block sizes.

math.ST↗