A Bayesian approach for multi‐model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

Coelho, Caio A. S.; Stephenson, David B.; Doblas‐Reyes, F. J.; Balmaseda, Magdalena; Guetter, Alex; Oldenborgh, G. J. van

doi:10.1017/s1350482705002045

Cited by 18 publications

(13 citation statements)

References 44 publications

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“…Several studies (Busuioc et al 2006;Coelho et al 2006;Khan et al 2006a,b;Rebora et al 2006) have described upscaling or downscaling methods for precipitation. Uncertainties arising from these scaling methods cannot be ignored.…”

Section: ) Interpolation Uncertaintymentioning

confidence: 99%

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

Pappenberger

Ghelli

Buizza

et al. 2009

Journal of Hydrometeorology

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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.

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Section: ) Interpolation Uncertaintymentioning

confidence: 99%

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

Pappenberger

Ghelli

Buizza

et al. 2009

Journal of Hydrometeorology

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“…Wood et al (2002), Luo and Wood (2008) have applied the physically based forecasting system on the eastern coast of the USA. Coelho et al (2006) have designed the system based on the climatic model in Brazil. In Europe, the abilities of physically based approach were demonstrated, e.g., by Céron et al (2010), Fundel et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

Šípek

Daňhelka

2015

Journal of Hydrology

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“…Examples include simple bias correction (e.g. Johnson and Swinbank, 2009), more detailed quantile mapping (Bremnes, 2007), inflation (Johnson and Swinbank, 2009;Flowerdew and Bowler, 2011), nearby locations and thresholds (Atger, 2001), direct mapping of forecast probabilities to past observed frequencies (Primo et al, 2009), forecast assimilation (Coelho et al, 2006), methods such as Bayesian Model Averaging , Fraley et al, 2010) that dress each ensemble member with a kernel, methods such as Non-homogeneous Gaussian regression (NGR; Gneiting et al, 2005 and logistic regression Wilks, 2009) that map raw forecast quantities to parameters of a fixed output distribution, analogue methods (Hamill and Whitaker, 2006;Stensrud and Yussouf, 2007) and neural networks. Applequist et al (2002) compares a variety of similar methods applied to deterministic input.…”

Section: Introductionmentioning

confidence: 99%

Calibrating ensemble reliability whilst preserving spatial structure

Flowerdew

2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Ensemble forecasts aim to improve decision-making by predicting a set of possible outcomes. Ideally, these would provide probabilities which are both sharp and reliable. In practice, the models, data assimilation and ensemble perturbation systems are all imperfect, leading to deficiencies in the predicted probabilities. This paper presents an ensemble post-processing scheme which directly targets local reliability, calibrating both climatology and ensemble dispersion in one coherent operation. It makes minimal assumptions about the underlying statistical distributions, aiming to extract as much information as possible from the original dynamic forecasts and support statistically awkward variables such as precipitation. The output is a set of ensemble members preserving the spatial, temporal and inter-variable structure from the raw forecasts, which should be beneficial to downstream applications such as hydrological models. The calibration is tested on three leading 15-d ensemble systems, and their aggregation into a simple multimodel ensemble. Results are presented for 12 h, 18 scale over Europe for a range of surface variables, including precipitation. The scheme is very effective at removing unreliability from the raw forecasts, whilst generally preserving or improving statistical resolution. In most cases, these benefits extend to the rarest events at each location within the 2-yr verification period. The reliability and resolution are generally equivalent or superior to those achieved using a Local Quantile-Quantile Transform, an established calibration method which generalises bias correction. The value of preserving spatial structure is demonstrated by the fact that 3)3 averages derived from grid-scale precipitation calibration perform almost as well as direct calibration at 3 )3 scale, and much better than a similar test neglecting the spatial relationships. Some remaining issues are discussed regarding the finite size of the output ensemble, variables such as sea-level pressure which are very reliable to start with, and the best way to handle derived variables such as dewpoint depression.

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A Bayesian approach for multi‐model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

Cited by 18 publications

References 44 publications

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

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

Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

Calibrating ensemble reliability whilst preserving spatial structure

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