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Mathematical and Statistical Approaches to Climate Modelling and PredictionEpisode from the podcastMathematical and Statistical Approaches to Climate Modelling and Prediction
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Statistical processing for ensembles of numerical weather prediction model

Statistical processing for ensembles of numerical weather prediction model

Released Tuesday, 21st December 2010
Good episode? Give it some love!
Statistical processing for ensembles of numerical weather prediction model

Statistical processing for ensembles of numerical weather prediction model

Statistical processing for ensembles of numerical weather prediction model

Statistical processing for ensembles of numerical weather prediction model

Tuesday, 21st December 2010
Good episode? Give it some love!
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Our best estimates of future climate are based on the use of complex computer models that do not explicitly resolve the wide variety of spatio-temporal scales making up Earth's climate system. The non-linearity of the governing physical processes allows energy transfer between different scales, and many aspects of this complex behaviour can be represented by stochastic models. However, the theoretical basis for so doing is far from complete. Many uncertainties remain in predictions derived from climate models, yet governments are increasingly reliant on model predictions to inform mitigation and adaptation strategies. An overarching aim of climate scientists is to reduce the uncertainty in climate predictions and produce credible assessments of model accuracy. This programme focuses on two key themes that both require the close collaboration of mathematicians, statisticians and climate scientists in order to improve climate models and the interpretation of their output.Read more at http://www.newton.ac.uk/programmes/CLP/index.html

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