The Skill of Probabilistic Precipitation Forecasts under Observational Uncertainties within the Generalized Likelihood Uncertainty Estimation Framework for Hydrological Applications

Abstract

A methodology for evaluating ensemble forecasts, taking into account observational uncertainties for catchment-based precipitation averages, is introduced. Probability distributions for mean catchment precipitation are derived with the Generalized Likelihood Uncertainty Estimation (GLUE) method. The observation uncertainty includes errors in the measurements, uncertainty as a result of the inhomogeneities in the rain gauge network, and representativeness errors introduced by the interpolation methods. The closeness of the forecast probability distribution to the observed fields is measured using the Brier skill score, rank histograms, relative entropy, and the ratio between the ensemble spread and the error of the ensemblemedian forecast (spread-error ratio). Four different methods have been used to interpolate observations on the catchment regions. Results from a 43-day period (20 July-31 August 2002) show little sensitivity to the interpolation method used. The rank histograms and the relative entropy better show the effect of introducing observation uncertainty, although this effect on the Brier skill score and the spread-error ratio is not very large. The case study indicates that overall observation uncertainty should be taken into account when evaluating forecast skill.

Observational probability method to assess ensemble precipitation forecasts

“…In fact, perfect observations or observations with added noise produce almost identical results. Similar results are discussed in Pappenberger et al (2009), who classify observation uncertainty as a result of measurement errors, inhomogeneous observation density, or model or observation interpolation. Candille and Talagrand (2008;hereafter CT08) validate an ensemble prediction system introducing the 'observational probability' method in the verification process (hereafter referred to as OP).…”

Evaluating multi-scale precipitation forecasts using high resolution analysis

“…Following Pappenberger et al (2009) we roughly estimate the raingauge measurement errors (undercatch) to be of the order of 10% for lowland and valley stations, and 20% for some of the more exposed mountain stations. Note that we are dealing with cases of rainfall only.…”

“…However, the SAL method is equivalent to using spatially aggregated values, which reduces the error. For a comprehensive analysis and discussion of the uncertainty issue we refer to Pappenberger et al (2009) and references therein.…”

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

“…Despite large improvements it is nowadays recognized that many of the processes linked with the triggering of (flash-)floods (local thunderstorms, generation of surface runoff) suffer from low predictability (Collier, 2007;Pappenberger et al, 2009) and there is consequently a serious need for quantifying predictive uncertainty of the model involved (Pappenberger and Beven, 2006;Beven, 2006Beven, , 2009Todini and Mantovan, 2007).…”

“…Since the presentation of the 'Generalized Likelihood Uncertainty Estimation' (GLUE) by Beven and Binley (1992) numerous algorithms have been developed and adopted for estimation uncertainty of environmental models in general and of hydrological models in particular (Beven, 2006(Beven, , 2009Liu and Gupta, 2007;Matott et al, 2009;Montanari et al, 2009). A transfer of these methods for estimating uncertainty in observed precipitation fields has been recently realized by Pappenberger et al (2009).…”