Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model
Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model
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DOI：
10.1111/rssc.12309
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发表时间：
2019-01-01
影响因子：
1.6
通讯作者：
Guillas, Serge
中科院分区：
文献类型：
--
作者：
Chang, Kai-Lan;Guillas, Serge
贝叶斯校准的计算机模型调整未知的输入参数，通过比较输出与观察。对于分布在空间上的模型输出，由于输出大小，这在计算上变得昂贵。为了克服这一挑战，我们采用了模型输出和观测的基本表示：我们匹配这些分解以有效地进行校准。在第二步中，我们将非平稳行为，在空间变化的方差和相关性，在校准。我们插入两个集成的嵌套拉普拉斯近似随机偏微分方程参数的校准。一个合成的例子和一个气候模型说明突出了我们的方法的好处。
Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently. In the second step, we incorporate the non-stationary behaviour, in terms of spatial variations of both variance and correlations, in the calibration. We insert two integrated nested Laplace approximation-stochastic partial differential equation parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach.