集合预报检验和再校准的贝叶斯框架：NAO的可预报性有多不确定？
A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?
摘要：
集合预报系统的可预报性估计是不确定的，由于过去的预测和观测数量有限。为了解释这种不确定性，本文提出了一个贝叶斯推理框架，提供了一个简单的集合预报系统和相应的观测6参数表示。该框架是概率性的，因此允许量化的可预测性措施，如相关技能和信噪比的不确定性。它还提供了一种自然的方式来从未经校准的集合预测中产生重新校准的概率预测。该框架是用来解决有关的技能，北大西洋涛动1992-2011年发布的气象局GloSea 5气候预测系统的冬季后报的重要问题。虽然集合平均值和观测值之间的相关性有很大的不确定性，但有强有力的证据表明技巧：[0.19，0.68]的相关系数的95%可信区间不与零重叠。还有强有力的证据表明，预测与观测不可互换：在99%以上的确定性下，预测的信噪比小于观测的信噪比，这表明原始预测不应被视为观测的代表性情景。因此，需要重新调整预测，这可以在拟议框架内协调一致地解决。
英文摘要：
Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic, and thus allows for quantifying uncertainty in predictability measures such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts. The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992-2011 issued by the Met Office GloSea5 climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19,0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: With over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.
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