Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles

Weisheimer, Antje; Palmer, Tim; Doblas‐Reyes, F. J.

doi:10.1029/2011gl048123

Cited by 79 publications

(78 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ensemble spread) should match the root-mean square error (RMSE) of the ensemble-mean (e.g. Talagrand et al 1999;Buizza et al 2005;Eckel and Mass 2005;Weisheimer et al 2009Weisheimer et al , 2011. Therefore, the dispersion of ensembles can be measured by the difference between ensemble spread and the RMSE of the ensemble-mean.…”

Section: Relation Between Dispersion Of Initial Ensembles and Predictmentioning

confidence: 99%

Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment

et al. 2015

View full text Add to dashboard Cite

ratio method and using ensemble-mean approach, we found that the multi-model mean BSISO predictability estimate and prediction skill with strong initial amplitude (about 10 % higher than the mean initial amplitude) are about 45 and 22 days, respectively, which are comparable with the corresponding counterparts for Madden-Julian Oscillation during boreal winter (Neena et al. in J Clim 27:4531-4543, 2014a). The significantly lower BSISO prediction skill compared with its predictability indicates considerable room for improvement of the dynamical BSISO prediction. The estimated predictability limit is independent on its initial amplitude, but the models' prediction skills for strong initial amplitude is 6 days higher than the corresponding skill with the weak initial condition (about 15 % less than mean initial amplitude), suggesting the importance of using accurate initial conditions. The BSISO predictability and prediction skill are phase and season-dependent, but the degree of dependency varies with the models. It is important to note that the estimation of prediction skill depends on the methods that generate initial ensembles. Our analysis indicates that a better dispersion of ensemble members can considerably improve the ensemble mean prediction skills.

show abstract

Section: Relation Between Dispersion Of Initial Ensembles and Predictmentioning

confidence: 99%

Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The use of ensembles of models and techniques is one way to get a handle on and to represent these uncertainties (e.g. Murphy et al, 2004;Stainforth et al, 2007;Cloke and Pappenberger, 2009;Weisheimer et al, 2011), and an ensemble of ensembles can be termed a grand ensemble .…”

Section: Introductionmentioning

confidence: 99%

Modelling climate impact on floods with ensemble climate projections

Cloke

Wetterhall

et al. 2012

Quart J Royal Meteoro Soc

109

104

View full text Add to dashboard Cite

The evidence provided by modelled assessments of future climate impact on flooding is fundamental to water resources and flood risk decision making. Impact models usually rely on climate projections from global and regional climate models (GCM/RCMs). However, challenges in representing precipitation events at catchment-scale resolution mean that decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs. Here the impacts on projected high flows of differing ensemble approaches and application of Model Output Statistics to RCM precipitation are evaluated while assessing climate change impact on flood hazard in the Upper Severn catchment in the UK. Various ensemble projections are used together with the HBV hydrological model with direct forcing and also compared to a response surface technique. We consider an ensemble of single-model RCM projections from the current UK Climate Projections (UKCP09); multi-model ensemble RCM projections from the European Union's FP6 'ENSEMBLES' project; and a joint probability distribution of precipitation and temperature from a GCM-based perturbed physics ensemble.The ensemble distribution of results show that flood hazard in the Upper Severn is likely to increase compared to present conditions, but the study highlights the differences between the results from different ensemble methods and the strong assumptions made in using Model Output Statistics to produce the estimates of future river discharge. The results underline the challenges in using the current generation of RCMs for local climate impact studies on flooding.

show abstract

“…Probabilistic approaches overcome these limitations through ensemble prediction (Kalnay et al, 2006) using multiple realizations for a single forecast time and location to sample forecast uncertainty. Ensemble generation is achieved by either perturbations of initial conditions, perturbations introduced at each model integration (stochastic physics) or use of multi-model ensembles (Graham et al, 2000;Tebaldi et al, 2004;Thomson et al, 2006;Shutts et al, 2011;Weisheimer et al, 2011;Doblas-Reyes et al, 2013;Weisheimer and Palmer, 2014).…”

Section: Introductionmentioning

confidence: 99%

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Ogutu

Franssen

Supit

et al. 2016

Intl Journal of Climatology

View full text Add to dashboard Cite

This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of precipitation (tp), near-surface air temperature (tas) and surface downwelling shortwave radiation (rsds). We validated the 30-year (1981-2010) hindcast version of S4 against the WFDEI reanalysis (WATCH Forcing Data ERA-Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score (RPSS) and the relative operating curve skill score (ROCSS) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM, JJA and OND).S4 exhibits a wet bias in OND, a dry bias in MAM and a mix of both in JJA. Temperature biases are similar in all seasons, constant with lead-time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills.Predictability of the three variables varies with season, location and lead-time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead-time compared to MAM and JJA. Upper-and lower-tercile tp forecasts are 20-80% better than climatology. Temperature forecasts are skillful for at least 3 months lead-time and they are 40-100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El-Niño Southern Oscillation (ENSO)-related anomalous years with region-dependent skill.

show abstract

Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles

Cited by 79 publications

References 26 publications

Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment

Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment

Modelling climate impact on floods with ensemble climate projections

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Contact Info

Product

Resources

About