Accounting for Observational Uncertainty in Forecast Verification: An Information-Theoretical View on Forecasts, Observations, and Truth
Accounting for Observational Uncertainty in Forecast Verification: An Information-Theoretical View on Forecasts, Observations, and Truth
复制标题
预测验证中观测不确定性的解释：关于预测、观测和真相的信息理论观点
DOI：
10.1175/2011mwr3573.1
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发表时间：
2011
影响因子：
3.2
通讯作者：
N. Giesen
中科院分区：
文献类型：
--
作者：
S. Weijs;N. Giesen
AbstractRecently, an information-theoretical decomposition of Kullback–Leibler divergence into uncertainty, reliability, and resolution was introduced. In this article, this decomposition is generalized to the case where the observation is uncertain. Along with a modified decomposition of the divergence score, a second measure, the cross-entropy score, is presented, which measures the estimated information loss with respect to the truth instead of relative to the uncertain observations. The difference between the two scores is equal to the average observational uncertainty and vanishes when observations are assumed to be perfect. Not acknowledging for observation uncertainty can lead to both overestimation and underestimation of forecast skill, depending on the nature of the noise process.
