Probabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial ...conditions on the quality of such forecasts can be mitigated by calibration. This paper shows that Bayesian methods currently used to incorporate prior information can be written as special cases of a beta-binomial model and correspond to a linear calibration of the relative frequencies. These methods are compared with a nonlinear calibration technique (i.e., logistic regression) using real precipitation forecasts. Calibration is found to be advantageous in all cases considered, and logistic regression is preferable to linear methods. PUBLICATION ABSTRACT
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract
This article proposes a method for verifying deterministic forecasts of rare, extreme events defined by exceedance above a high threshold. A probability model for the joint distribution of ...forecasts and observations, and based on extreme-value theory, characterizes the quality of forecasting systems with two key parameters. This enables verification measures to be estimated for any event rarity and helps to reduce the uncertainty associated with direct estimation. Confidence regions are obtained and the method is used to compare daily precipitation forecasts from two operational numerical weather prediction models.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
34.
Predictability of Cold Spring Seasons in Europe SHONGWE, Mxolisi E; FERRO, Christopher A. T; COELHO, Caio A. S ...
Monthly weather review,
12/2007, Letnik:
135, Številka:
12
Journal Article
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The seasonal predictability of cold spring seasons (March-May) in Europe from hindcasts/forecasts of three operational coupled general circulation models (CGCMs) is investigated. The models used in ...the investigation are the Met Office Global Seasonal Forecast System (GloSea), the ECMWF System-2 (S2), and the NCEP Climate Forecast System (CFS). Using the relative operating characteristic score and the Brier skill score the long-term prediction skill for spring 2-m temperature in the lower quintile (20%) is assessed. Over much of central and eastern Europe the predictive skill is found to be high. The skill of the Met Office GloSea and ECMWF S2 models significantly surpasses that of damped persistence over much of Europe but the NCEP CFS model outperforms this reference forecast only over a small area. The higher potential predictability of cold spring seasons in eastern relative to southwestern Europe can be attributed to snow effects as areas of high skill closely correspond with the climatological snow line, and snow is shown in this paper to be linked to cold spring 2-m temperatures in eastern Europe. The ability of the models to represent snow cover during the melt season is also investigated. The Met Office GloSea and the ECMWF S2 models are able to accurately mimic the observed pattern of monthly snow-cover interannual variability, but the NCEP CFS model predicts too short a snow season. Improvements in the snow analysis and land surface parameterizations could increase the skill of seasonal forecasts for cold spring temperatures. PUBLICATION ABSTRACT
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This paper introduces the notion of robust extremes in deterministic chaotic systems, presents initial theoretical results, and outlines associated inferential techniques. A chaotic deterministic ...system is said to exhibit robust extremes under a given observable when the associated statistics of extreme values depend smoothly on the system's control parameters. Robust extremes are here illustrated numerically for the flow of the Lorenz model E. N. Lorenz, J. Atmos. Sci. 20, 130 (1963). Robustness of extremes is proved for one-dimensional Lorenz maps with two distinct types of observables for which conditions guaranteeing robust extremes are formulated explicitly. Two applications are shown: improving the precision of the statistical estimator for extreme value distributions and predicting future extremes in nonstationary systems. For the latter, extreme wind speeds are examined in a simple quasigeostrophic model with a robust chaotic attractor subject to nonstationary forcing.
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.
Discussion on the paper by Johnstone Satchell, Steve; Ferro, Christopher A. T.; Smith, Peter W. F. ...
Journal of the Royal Statistical Society. Series A, Statistics in society,
07/2012, Letnik:
175, Številka:
3
Journal Article
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.
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.
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