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CRiSM Seminar

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Location: MSB2.22

Prof. Karla Hemming, University of Birmingham, UK (15:00-16:00)

Speaker: Clair Barnes, University College London, UK

Death & the Spider: postprocessing multi-ensemble weather forecasts with uncertainty quantification

Ensemble weather forecasts often under-represent uncertainty, leading to overconfidence in their predictions. Multi-model forecasts combining several individual ensembles have been shown to display greater skill than single-ensemble forecasts in predicting temperatures, but tend to retain some bias in their joint predictions. Established postprocessing techniques are able to correct bias and calibration issues in univariate forecasts, but are generally not designed to handle multivariate forecasts (of several variables or at several locations, say) without separate specification of the structure of the inter-variable dependence.

We propose a flexible multivariate Bayesian postprocessing framework, developed around a directed acyclic graph representing the relationships between the ensembles and the observed weather. The posterior forecast is inferred from the ensemble forecasts and an estimate of their shared discrepancy, which is obtained from a collection of past forecast-observation pairs. The approach is illustrated with an application to forecasts of UK surface temperatures during the winter period from 2007-2013.

Speaker: Karla Hemming, University of Birmingham (1500-1600)

The I-squared-CRT statistic to describe treatment effect heterogeneity in cluster randomized trials.

K Hemming (Birmingham) and A Forbes (Monash)

Treatment effect heterogeneity is commonly investigated in meta-analyses of treatment effects across different studies. The effect of a treatment might also vary across clusters in a cluster randomized trial, and it can be of interest to explore this at the analysis stage. In stepped-wedge designs and other cluster randomized designs, in which clusters are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. When conducting a meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. Here we derive and evaluate a comparable measure of the description of the magnitude of heterogeneity in treatment effects across clusters in cluster randomized trials, the I-squared-CRT.

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