Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia
Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia
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合并澳大利亚运营统计和动态季节性径流预测的模型平均方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Quan J. Wang
中科院分区：
文献类型：
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作者：
A. Schepen;Quan J. Wang
澳大利亚气象局提供统计和动态的季节性径流预报。统计预报和动态预报在总体扩散方面同样可靠；然而，预报技巧因流域和季节而异。因此，有可能通过加权和合并统计预测和动态预测来优化预测技能。对两种模型平均方法对12个地点的合并预报进行了评估。第一种方法，贝叶斯模型平均(BMA)，对给定的预测变量值应用平均来预测概率密度(从而累积概率)。第二种方法，分位数模型平均(QMA)，对给定的累积概率(分位数分数)应用平均来预测变量值(分位数)。BMA和QMA在总体技能得分和合奏传播的可靠性方面表现相似。这两种方法都提高了跨流域和跨季节的预报技能。然而，当统计和动态预测方法都很熟练，但在特殊情况下产生非常不同的事件预测时，BMA对这些事件的合并预测可能具有异常广泛的双峰分布。相比之下，这些事件的QMA合并预报的分布更窄、单峰，通常形状更平滑，可能更容易与预报用户沟通和解释。人们发现，这样的特殊场合很少见。然而，在业务服务中，每个预测都很重要，因此，两种方法之间合并预测的偶尔对比可能比总体技能和可靠性表现显示的无差异更重要。
The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.