Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consistent with real-world observations. When calibrating models with high-dimensional output (e.g. a spatial field), it is common to represent the output as a linear combination of a small set of basis vectors. Often, when trying to calibrate to such output, what is important to the credibility of the model is that key emergent physical phenomena are represented, even if not faithfully or in the right place. In these cases, comparison of model output and data in a linear subspace is inappropriate and will usually lead to poor model calibration. To overcome this, we present kernel-based history matching (KHM), generalising the meaning of the technique sufficiently to be able to project model outputs and observations into a higher-dimensional feature space, where patterns can be compared without their location necessarily being fixed. We develop the technical methodology, present an expert-driven kernel selection algorithm, and then apply the techniques to the calibration of boundary layer clouds for the French climate model IPSL-CM.

翻译：计算机模型校准涉及利用真实世界的部分且不完美观测，学习模型输入参数的哪些取值能产生与真实世界观测一致的输出。当校准具有高维输出（例如空间场）的模型时，通常将该输出表示为少量基向量的线性组合。然而，在尝试校准此类输出时，模型可信度的关键在于能否呈现关键的涌现物理现象，即使这些现象在位置或保真度上不尽完美。在此类情况下，在线性子空间中比较模型输出与数据是不恰当的，通常会导致模型校准效果不佳。为解决此问题，我们提出了基于核的历史匹配方法（KHM），该方法充分泛化了技术内涵，能够将模型输出与观测投影到更高维特征空间，使得模式比较不再受限于其位置固定性。我们发展了技术方法论，提出了一种专家驱动的核选择算法，并将该技术应用于法国气候模型IPSL-CM中边界层云的校准。