Improvement of the Multimodel Superensemble Technique for Seasonal Forecasts

Yun, Won-Tae; Stefanova, Lydia; Krishnamurti, T. N.

doi:10.1175/1520-0442(2003)016<3834:iotmst>2.0.co;2

Cited by 129 publications

(115 citation statements)

References 19 publications

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“…The intent is that some of the biases inherent in the different models will offset so that the superensemble produces more accurate predictions than those generated by any individual model. Such improvement has indeed been observed [6][7][8].…”

Section: Introductionsupporting

confidence: 59%

Superensemble forecasts of dengue outbreaks

Yamana¹,

Kandula²,

Shaman³

2016

J. R. Soc. Interface.

111

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In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.

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Section: Introductionsupporting

confidence: 59%

Superensemble forecasts of dengue outbreaks

Yamana¹,

Kandula²,

Shaman³

2016

J. R. Soc. Interface.

111

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“…The simplest multimodel technique is the "Poor Man Ensemble", which is an average of different models, without any bias correction or weighting ("equal weighting"), while more sophisticated approaches suggest applying model weights according to some measure of performance ("optimum weighting"). The results confirm that equally weighted multimodels on average outperform the single models (Krishnamurti et al, 1999(Krishnamurti et al, , 2000Yun et al, 2003), and that projection errors can in principle be further reduced by optimum weighting. However, this not only requires accurate knowledge of the single model skill, but the relative contributions of the joint model error and unpredictable noise also need to be known to avoid biased weights (Weigel et al, 2010).…”

Section: Introductionsupporting

confidence: 64%

Regional climate models downscaling in the Alpine area with multimodel superensemble

Cane

Barbarino

Renier

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization.Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area.For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique.Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period.We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.

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“…However, the simple multi-model average does not take into account the quality differences between the models; therefore, it is expected that a weighted average, with weights based on the past performances of the models, will provide better predictions than the simple average. As expected, it was shown that the weighted average of climate models can improve predictions when using ensembles of AGCMs (Rajagopalan et al, 2002;Robertson et al, 2004;Yun et al, 2003), AOGCMs (Yun et al, 2005;Pavan and Doblas-Reyes, 2000;Chakraborty and Krishnamurti, 2009) and regional climate models (Feng et al,…”

Section: Introductionsupporting

confidence: 52%

Improvement of climate predictions and reduction of their uncertainties using learning algorithms

Strobach

Bel

2015

Atmos. Chem. Phys.

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Abstract. Simulated climate dynamics, initialized with observed conditions, is expected to be synchronized, for several years, with the actual dynamics. However, the predictions of climate models are not sufficiently accurate. Moreover, there is a large variance between simulations initialized at different times and between different models. One way to improve climate predictions and to reduce the associated uncertainties is to use an ensemble of climate model predictions, weighted according to their past performances. Here, we show that skillful predictions, for a decadal time scale, of the 2 m temperature can be achieved by applying a sequential learning algorithm to an ensemble of decadal climate model simulations. The predictions generated by the learning algorithm are shown to be better than those of each of the models in the ensemble, the better performing simple average and a reference climatology. In addition, the uncertainties associated with the predictions are shown to be reduced relative to those derived from an equally weighted ensemble of bias-corrected predictions. The results show that learning algorithms can help to better assess future climate dynamics.

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Improvement of the Multimodel Superensemble Technique for Seasonal Forecasts

Cited by 129 publications

References 19 publications

Superensemble forecasts of dengue outbreaks

Superensemble forecasts of dengue outbreaks

Regional climate models downscaling in the Alpine area with multimodel superensemble

Improvement of climate predictions and reduction of their uncertainties using learning algorithms

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