Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific
Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific
复制标题
热带太平洋大气环流模型中基于多项式混沌的 K 剖面参数化贝叶斯推理
DOI：
10.1175/mwr-d-15-0394.1
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发表时间：
2015
影响因子：
3.2
通讯作者：
I. Hoteit
中科院分区：
文献类型：
--
作者：
I. Sraj;S. Zedler;O. Knio;C. Jackson;I. Hoteit
摘要作者提出了一种基于多项式混沌 (PC) 的贝叶斯推理方法，用于量化热带太平洋 MIT 大气环流模型 (MITgcm) 中 K 剖面参数化 (KPP) 的不确定性。不确定参数的推断基于马尔可夫链蒙特卡罗 (MCMC) 方案，该方案利用新制定的检验统计量，除了数据质量之外，还考虑了代表日常和季节时间尺度上湍流混合结构的不同成分，并过滤了参数扰动对风变化造成的影响。为了避免在每次 MCMC 迭代中集成 MITgcm 模型的高昂计算成本，使用 PC 方法构建了检验统计量的代理模型。由于模型预测中存在噪声，因此采用基础追踪去噪 (BPDN) 压缩感知方法来确定代表性替代模型的 PC 系数。 PC代理...
AbstractThe authors present a polynomial chaos (PC)–based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate...