Practical Aspects of Statistical Postprocessing

Hamill, Thomas M.

doi:10.1016/b978-0-12-812372-0.00007-8

Cited by 18 publications

(23 citation statements)

References 60 publications

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“…For example, Hamill () found that light precipitation in operational global ensemble predictions was commonly overforecast and heavy precipitation underforecast. Such biases in precipitation may also change from one season to the next (Hamill, ). For these reasons, statistical postprocessing of the output of deterministic and ensemble prediction systems is commonly an integral part of the numerical weather prediction process.…”

Section: Introductionmentioning

confidence: 99%

Statistical postprocessing of dual‐resolution ensemble precipitation forecasts across Europe

Gascón

Lavers

Hamill

et al. 2019

Quart J Royal Meteoro Soc

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This article verifies 1‐ to 10‐day probabilistic precipitation forecasts in June, July, and August 2016 from an experimental dual‐resolution version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system. Five different ensemble combinations were tested. These comprised subsets of the 51‐member operational ECMWF configuration (18‐km grid) and an experimental 201‐member lower‐resolution configuration (29‐km grid). The motivation of the dual‐resolution ensemble forecast is to trade some higher‐resolution members against a larger number of lower‐resolution members to increase the overall ensemble size at constant overall computational cost. Forecasts were verified against precipitation analyses over Europe. Given substantial systematic errors of precipitation forecasts, both raw and post‐processed dual‐resolution ensemble predictions were evaluated. Postprocessing consisted of quantile mapping, tested with and without an objective weighting of sorted ensemble members using closest‐member histogram statistics. Reforecasts and retrospective precipitation analyses were used as training data. However, the reforecast ensemble size and the dual‐resolution ensemble sizes differed, which motivated the development of a novel approach for developing closest‐member histogram statistics for the larger real‐time ensemble from the smaller reforecast ensemble. Results show that the most skilful combination was generally 40 ensemble members from the operational configuration and 40 from the lower‐resolution ensemble, evaluated by continuous ranked probability scores, Brier Scores at various thresholds, and reliability diagrams. This conclusion was generally valid with and without postprocessing. Reliability was improved by postprocessing, though the improvement of the resolution component is not so clear. The advantages of many members at higher resolution was diminished at longer lead times; predictability of smaller scale features was lost, and there is more benefit in increasing the ensemble size to reduce sampling uncertainty. This article evaluates only one aspect in deciding on any future ensemble configuration, and other skill‐related considerations need to be taken into account.

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Section: Introductionmentioning

confidence: 99%

Statistical postprocessing of dual‐resolution ensemble precipitation forecasts across Europe

Gascón

Lavers

Hamill

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

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“…How to evaluate and select the best strategy among the proliferation of techniques and the necessity of a thorough understanding and training by operational forecasters are among the challenges related to the regular operational use of statistical postprocessors in hydrological ensemble prediction, identified in a blog post by Voisin et al (). Moreover, Hamill () emphasizes the problem related to the lengthy and high‐quality data sets and the need for postprocessors suitable for environment where training data are limited.…”

Section: Introductionmentioning

confidence: 99%

Comparing Hydrological Postprocessors Including Ensemble Predictions Into Full Predictive Probability Distribution of Streamflow

Biondi

Todini²

2018

Water Resources Research

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Although not matching the formal definition of the predictive probability distribution, meteorological and hydrological ensembles have been frequently interpreted and directly used to assess flood-forecasting predictive uncertainty. With the objective of correctly assessing the predictive probability of floods, this paper introduces ways of taking into account the measures of uncertainty provided in the form of ensemble forecasts by modifying a number of well-established uncertainty postprocessors, such as Bayesian Model Averaging and Model Conditional Processor. The uncertainty postprocessors were developed on the assumption that the future unknown quantity (predictand) is uncertain while model forecasts (predictors) are given, which imply that they are perfectly known. With this in mind, we propose to relax this assumption by considering ensemble predictions, in analogy to measurement errors, as expressions of errors in model predictions to be integrated in the postprocessors coefficients estimation process. The analyses of the methodologies proposed in this work are conducted on a real case study based on meteorological ensemble predictions for the Po River at Pontelagoscuro in Italy. After showing how improper can be the direct use of ensemble predictions to describe the predictive probability distribution, results from the modified postprocessors are compared and discussed. BIONDI AND TODINI 9860Water Resources Research

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“…Postprocessors dilute errors from model inputs and outputs, model parameters, model initial and boundary conditions and model structures (Buizza, 2018;Ye et al, 2014). Hydrological post-processing methods mostly follow the Model Output Statistics (MOS) approach (Glahn et al, 1972), namely to fit the statistical models using historical predictions and corresponding observations, then apply the fitted model to estimate the conditional predictive uncertainty of future observations (Hamill, 2018). Hydrological post-processing methods have two goals: 1) to estimate the conditional predictive uncertainty of the output of hydrological models with point predictions.…”

Section: Probabilistic Uncertainty Quantificationmentioning

confidence: 99%

“…Although many post-processing methods have been proposed, there has not been sufficient researches that explore and compare new statistical techniques to post-process and quantify predictive uncertainty (Hamill, 2018). For instance, little literature is available on estimating the conditional predictive uncertainty with intractable likelihood, i.e.…”

Section: Main Aims and Scopementioning

confidence: 99%

“…There is a clear need for assessing and comparing post-processing methods to diagnose the most suitable approaches for specific applications (W. . As stated during a 2016 workshop on Statistical post-processing, recommendation # 2: "Performance more comparisons of existing algorithms to determine which are the most skilful and reliable" (Hamill, 2018). Several studies have compared different post-processing methods for hydrological variables.…”

Section: Introductionmentioning

confidence: 99%

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Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows

Cuéllar¹

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Practical Aspects of Statistical Postprocessing

Cited by 18 publications

References 60 publications

Statistical postprocessing of dual‐resolution ensemble precipitation forecasts across Europe

Statistical postprocessing of dual‐resolution ensemble precipitation forecasts across Europe

Comparing Hydrological Postprocessors Including Ensemble Predictions Into Full Predictive Probability Distribution of Streamflow

Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows

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