The rationale behind the success of multi-model ensembles in seasonal
                        forecasting – I. Basic concept

Hagedorn, Renate; Doblas‐Reyes, Francisco J.; Palmer, Tim

doi:10.3402/tellusa.v57i3.14657

Cited by 226 publications

(275 citation statements)

References 20 publications

Supporting

Mentioning

263

Contrasting

Unclassified

Order By: Relevance

“…On the basis of these results, a user might decide to discount the forecast quality estimates obtained with a set of ensemble hindcasts of a much smaller ensemble size than the size used for the forecasts. A simplification has also been used in the past to compare the forecast quality of systems having different ensemble sizes (Mullen and Buizza, 2002;Hagedorn et al, 2005). If the same set of probability categories is used for both systems to estimate the BSS and its reliability and resolution terms, the results in Figure 3 suggest that the system with the largest ensemble size will be favoured in the case of small ensembles, regardless of the number of categories.…”

mentioning

confidence: 99%

How much does simplification of probability forecasts reduce forecast quality?

Doblas‐Reyes

Coelho²,

Stephenson

2008

Meteorological Applications

View full text Add to dashboard Cite

Probability forecasts from an ensemble are often discretized into a small set of categories before being distributed to the users. This study investigates how such simplification can affect the forecast quality of probabilistic predictions as measured by the Brier score (BS). An example from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational seasonal ensemble forecast system is used to show that the simplification of the forecast probabilities reduces the Brier skill score (BSS) by as much as 57% with respect to the skill score obtained with the full set of probabilities issued from the ensemble. This is more obvious for a small number of probability categories and is mainly due to a decrease in forecast resolution of up to 36%. The impact of the simplification as a function of the ensemble size is also discussed. The results suggest that forecast quality should be made available for the set of probabilities that the forecast user has access to as well as for the complete set of probabilities issued by the ensemble forecasting system.

show abstract

mentioning

confidence: 99%

How much does simplification of probability forecasts reduce forecast quality?

Doblas‐Reyes

Coelho²,

Stephenson

2008

Meteorological Applications

View full text Add to dashboard Cite

show abstract

“…To our knowledge, this approximation in literature is seldom used deterministically, because the verification process is mainly limited either to the mean/median maps (e.g. Hagedorn et al, 2005;Johnson and Bowler, 2009) or to extremes (Lim et al, 2010). Here, we also retain the other percentiles to develop a deterministic approach and provide information about deficiencies in the original ensemble such as limited variability and the existence of systematic biases in the shape of the pdf.…”

Section: S4 Ensemble Recombinationmentioning

confidence: 99%

Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean

Marcos-Matamoros

Llasat

Quintana-Seguí

et al. 2018

Science of The Total Environment

View full text Add to dashboard Cite

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.

show abstract

“…A question arising from this is whether the multi-model superiority over the single-model ensemble is only due to the larger ensemble size. Hagedorn et al (2005) discussed in detail the rationale behind the multi-model concept and demonstrated that the superiority is not only caused by the increased ensemble size. To illustrate to what extent the multi-model can be better than a single model, a set of 54-member ensemble hindcasts were carried out over the period 1987-1999 with the ECMWF coupled model.…”

Section: Multi-model Seasonal Predictionmentioning

confidence: 99%

“…However, in this contribution only dynamical methods will be considered. Palmer et al (2004), Hagedorn et al (2005) and Saha et al (2006), among many others, show and describe results on the seasonal forecasting problem from the meteorological point of view. The present study offers an overview of recent improvements and future devel-opments in seasonal forecasting that are relevant for agricultural management.…”

Section: Introductionmentioning

confidence: 99%

Developments in dynamical seasonal forecasting relevant to agricultural management

Doblas‐Reyes¹,

Hagedorn²,

Palmer³

2006

Clim. Res.

View full text Add to dashboard Cite

Recent developments in dynamical seasonal forecasting of potential relevance to agricultural management are discussed. These developments emphasize the importance of using a fully probabilistic approach at all stages of the forecasting process, from the dynamical ocean-atmosphere models used to predict climate variability at seasonal and interannual time scales, through the models used to downscale the global output to finer scales, to the end-user forecast models. The final goal is to create an end-to-end multi-scale (both in space and time) integrated prediction system that provides skilful, useful predictions of variables with socio-economic interest. Multi-model ensemble predictions made with the leading European global coupled climate models as part of the DEMETER (Development of a European Multi-model Ensemble system for seasonal to inTERannual prediction) project are used as an example to illustrate the potential of producing useful probabilistic predictions of seasonal climate fluctuations and of applying them to crop yield forecasting.

show abstract

The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept

Cited by 226 publications

References 20 publications

How much does simplification of probability forecasts reduce forecast quality?

How much does simplification of probability forecasts reduce forecast quality?

Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean

Developments in dynamical seasonal forecasting relevant to agricultural management

Contact Info

Product

Resources

About