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首页> 外文期刊>Quarterly Journal of the Royal Meteorological Society >Is unequal weighting significantly better than equal weighting for multi-model forecasting?
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Is unequal weighting significantly better than equal weighting for multi-model forecasting?

机译：对于多模型预测，不平等权重是否明显优于平等权重？

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This article proposes a statistical test for whether a multi-model combination with unequal weights has significantly smaller errors than a combination with equal weights. A combination with equal weights includes the case of a no-skill model, in which all weights equal zero, and the multi-model mean, in which all weights equal 1/M, where M is the number of models. The test is applied to seasonal hindcasts of 2 m temperature and precipitation generated by five state-of-the-art coupled atmosphere-ocean models. The hypothesis of equal weights could not be rejected over 75% the globe for temperature and 90% of the land for precipitation, implying that strategies for unequal weighting of forecasts may be of value only over a relatively small fraction ofthe globe. The fact that the test does not require pre-specifying a specific strategy for weighting forecasts suggests that it should be useful for exploring a wide range of multi-model strategies.
机译：本文提出了一种统计检验，用于确定权重不相等的多模型组合是否比权重相等的组合具有显着小的错误。具有相等权重的组合包括无技能模型（所有权重等于零）和多模型均值（其中所有权重等于1 / M）的情况，其中M是模型数。该测试适用于温度为2 m的季节性后遗症和五个最先进的大气海洋耦合模型产生的降水。在全球温度的75％以上和降水的土地90％上，均等权重的假设不能被拒绝，这意味着对预测权重不均等的策略可能仅在全球相对较小的一部分中具有价值。该测试不需要预先指定权重预测的特定策略这一事实表明，它对于探索各种多模型策略应该是有用的。

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