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首页> 外文期刊>Journal of Climate >Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging.
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Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging.

机译：通过贝叶斯模型平均合并来自多个统计模型的季节性降雨预测。

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Merging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for a BMA method that is capable of producing relatively stable weights in the presence of significant sampling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. The BMA method outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance.
机译：与使用单个模型相比，合并来自多个模型的预测具有合并单个模型的优势并更好地表示预测不确定性的潜力。这项研究开发了一种贝叶斯模型平均（BMA）方法，用于合并来自多个模型的预测，从而为性能更好的模型赋予更大的权重。这项研究的目标是一种BMA方法，该方法能够在存在明显的采样变异性的情况下产生相对稳定的权重，从而为未来事件提供可靠的预测。 BMA方法适用于使用气候指数作为预测因子，合并来自多个统计模型的预测，用于澳大利亚的季节性降雨预测。结果表明，完全合并的预测有效地结合了模型的最佳技能，从而最大化了积极技能的空间覆盖范围。总体而言，该技能在上半年度较低，但在下半年度较高。太平洋地区的模型贡献了最多的技能，而印度和温带地区的模型也产生了有用的，有时是截然不同的技能。发现完全合并的概率预测在表示预测不确定性范围方面是可靠的。当预测提前期从0个月增加到1个月时，预测技能将保持良好状态。 BMA方法优于使用具有两个先验选择的固定预测变量的模型的方法以及基于预测性能选择最佳模型的方法。

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