观测范围调整法:在模型评估中考虑观测不确定性的新方法

IF 2.5 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
J P Evans and H M Imran
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引用次数: 0

摘要

模型评估是通过比较模型量和观测量来进行的,与观测量的任何偏差都被视为误差。我们知道,所有观测系统都有不确定性,对同一数量的多个观测产品可以提供同样可信的 "真相"。因此,模型误差取决于评估工作中对观测数据的选择。我们提出的方法是,当模型在观测范围内时,认为模型与观测结果没有区别,因此不分配任何误差。当模型超出观测范围时,就会产生误差。以这种方法计算出的误差可用于传统统计中,计算出该统计的观测范围调整(ORA)版本。观测范围调整统计量突出了模型的可测量误差,提供了更可靠的模型性能排名,并确定了进一步模型开发可能导致模型持续改进的模型区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation
Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.
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来源期刊
Environmental Research Communications
Environmental Research Communications ENVIRONMENTAL SCIENCES-
CiteScore
3.50
自引率
0.00%
发文量
136
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