Trends in the predictive performance of raw ensemble weather forecasts

Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

doi:10.1002/2014gl062472

Cited by 86 publications

(111 citation statements)

References 29 publications

(42 reference statements)

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“…In NR, the forecast probability distribution F is supposed to be some known distribution: here the square root of forecast wind speed follows a truncated normal distribution whose mean and variance depend on the ensemble forecast. This is similar to the work of Hemri et al (2014), who also gives the closed form expression of the instantaneous CRPS for this case. QRF is nonparametric and yields a set of quantiles x i with chosen orders τ i .…”

Section: Raw and Calibrated Ensemble Forecast Data Setssupporting

confidence: 82%

“…with Grimit et al (2006) p i=1 ω i = 1, ω i=1,..., p > 0 Generalized extreme value: Y ∼ G E V (μ, σ, ξ ) Friederichs and Thorarinsdottir (2012) Generalized Pareto: Y ∼ G P D(μ, σ, ξ ) Friederichs and Thorarinsdottir (2012) Log-normal: ln(Y ) ∼ N (μ, σ ) Baran and Lerch (2015) Normal: Y ∼ N (μ, σ ) Gneiting et al (2005) Square-root truncated normal: √ Y ∼ N 0 (μ, σ ) Hemri et al (2014) Truncated normal: Y ∼ N 0 (μ, σ ) Thorarinsdottir and Gneiting (2010) The reference of the original article where to find the formula is also given. Taillardat et al (2016) gathers the closed form expression of the CRPS for these and other distributions in Appendix A, this score for an ensemble is minimized if all the members x i equal the median of F, which is obviously not the purpose of an ensemble.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Zamo

Naveau

2017

Math Geosci

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The continuous ranked probability score (CRPS) is a much used measure of performance for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference between the forecast cumulative distribution function (CDF) and the empirical CDF of the observation. Analytic formulations of the CRPS can be derived for most classical parametric distributions, and be used to assess the efficiency of different CRPS estimators. When the true forecast CDF is not fully known, but represented as an ensemble of values, the CRPS is estimated with some error. Thus, using the CRPS to compare parametric probabilistic forecasts with ensemble forecasts may be misleading due to the unknown error of the estimated CRPS for the ensemble. With simulated data, the impact of the type of the verified ensemble (a random sample or a set of quantiles) on the CRPS estimation is studied. Based on these simulations, recommendations are issued to choose the most accurate CRPS estimator according to the type of ensemble. The interest of these recommendations is illustrated with real ensemble weather forecasts. Also, relationships between several estimators of the CRPS are demonstrated and used to explain the differences of accuracy between the estimators.

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Section: Raw and Calibrated Ensemble Forecast Data Setssupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Zamo

Naveau

2017

Math Geosci

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“…Several other approaches for post-processing and bias correction exist, for instance, based on MOS techniques, spacetime disaggregation schemes or Bayesian model averaging Raftery et al, 2005;Liu et al, 2013;Hemri et al, 2014). These could be investigated to contribute to the comprehensive comparison of options for bias correcting precipitation and temperature forecasts prior to seasonal streamflow forecasting.…”

Section: Discussionmentioning

confidence: 99%

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Crochemore

Ramos²,

Pappenberger

2016

Hydrol. Earth Syst. Sci.

176

166

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Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

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“…It fits a single parametric predictive probability density function (pdf) using summary statistics from the (multi-model) ensemble and corrects simultaneously for biases and dispersion errors. Also, NGR has been applied many times successfully for calibrating and combining hydro-meteorological ensemble forecasts (see for example Hemri et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Technical note: Combining quantile forecasts and predictive distributions of streamflows

Bogner¹,

Liechti²,

Zappa³

2017

Hydrol. Earth Syst. Sci.

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Abstract. The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the nonhomogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

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Trends in the predictive performance of raw ensemble weather forecasts

Cited by 86 publications

References 29 publications

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Technical note: Combining quantile forecasts and predictive distributions of streamflows

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