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Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above
Tellus A: Dynamic Meteorology and Oceanography ( IF 2.247 ) Pub Date : 2021-06-12 , DOI: 10.1080/16000870.2021.1924952
Spencer Lunderman 1 , Matthias Morzfeld 2 , Derek J. Posselt 3
Affiliation  

Abstract

Global Bayesian optimization (GBO) is a derivative-free optimization method that is used widely in the tech-industry to optimize objective functions that are expensive to evaluate, numerically or otherwise. We discuss the use of GBO in ensemble data assimilation (DA), where the goal is to update the state of a numerical model in view of noisy observations. Specifically, we consider three tasks: (i) the estimation of model parameters; (ii) the tuning of localization and inflation in ensemble DA; (iii) doing both, i.e. estimating model parameters while simultaneously tuning the localization and inflation of the ensemble DA. For all three tasks, the GBO works ‘offline’, i.e. a set of ‘training’ observations are used within GBO to determine appropriate model or localization/inflation parameters, which are subsequently deployed within an ensemble DA system. Because of the offline nature of the technique, GBO can easily be combined with existing DA systems and it can effectively decouple (nearly) linear/Gaussian aspects of a problem from highly nonlinear/non-Gaussian ones. We illustrate the use of GBO in simple numerical experiments with the classical Lorenz problems. Our main goals are to introduce GBO in the context of ensemble DA and to spark an interest in GBO and its uses for streamlining important tasks in ensemble DA.



中文翻译:

在集成数据同化中使用全局贝叶斯优化:参数估计、调整定位和膨胀,或以上所有

摘要

全局贝叶斯优化 (GBO) 是一种无导数优化方法,在科技行业中广泛用于优化以数值或其他方式评估成本高昂的目标函数。我们讨论了 GBO 在集合数据同化 (DA) 中的使用,其目标是根据噪声观测更新数值模型的状态。具体来说,我们考虑三个任务:(i)模型参数的估计;(ii) 集成 DA 中本地化和膨胀的调整;(iii) 两者都做,即估计模型参数,同时调整集合 DA 的定位和膨胀。对于所有三个任务,GBO 都在“离线”工作,即在 GBO 中使用一组“训练”观察来确定适当的模型或定位/通货膨胀参数,随后部署在集成 DA 系统中。由于该技术的离线特性,GBO 可以很容易地与现有的 DA 系统结合,并且它可以有效地将问题的(几乎)线性/高斯方面与高度非线性/非高斯方面解耦。我们说明了 GBO 在经典洛伦兹问题的简单数值实验中的使用。我们的主要目标是在集成 DA 的背景下引入 GBO,并激发人们对 GBO 及其在集成 DA 中简化重要任务的用途的兴趣。

更新日期:2021-06-13
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