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Verifying and assessing the performance of the perturbation strategy in polynomial chaos ensemble forecasts of the circulation in the Gulf of Mexico

机译:验证和评估墨西哥湾循环的多项式混沌集合预测中扰动策略的绩效

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摘要

We present an analysis of two recent efforts aimed at quantifying the uncertainties in a 30-day Hybrid Coordinate Ocean Model forecast of the circulation in the Gulf of Mexico, with particular emphasis on the separation of Loop Current Eddy Franklin, using Polynomial Chaos methods. The analysis herein explores whether the model perturbations lead to realistic representation of the uncertainty in the Gulf Circulation. Comparisons of model output with Sea Surface Height and current mooring data show that the observational data generally falls within the envelope of the ensemble and that the modal decomposition delivers "realistic" perturbations in the Loop Current region. We use information theory metrics to quantify the information gain and the computational trade-offs between different wind and initial conditions perturbation modes. The relative entropy measures indicate that two modes for initial condition perturbations are enough, in our model configuration, to represent the uncertainty in the Loop Current region; while two modes for wind forcing perturbations are necessary in order to estimate the uncertainty in the coastal zone. The ensemble statistics are then explored using the Polynomial Chaos surrogate and the newly developed contour boxplot methods.
机译:我们对最近的两个努力进行了分析,旨在量化墨西哥湾的循环的30天混合坐标海洋模型预测中的不确定性,特别强调了使用多项式混沌方法的环流涡流的分离。本文的分析探讨了模型扰动是否导致海湾循环中不确定性的现实表示。海面高度和电流系泊数据模型输出的比较表明,观测数据通常落入集合的信封内,并且模态分解在环电流区域中提供“现实”扰动。我们使用信息理论指标来量化不同风和初始条件扰动模式之间的信息增益和计算权衡。相对熵措施表明初始条件扰动的两种模式足够,在我们的模型配置中,表示环路电流区域中的不确定性;虽然需要两种风力强制扰动的模式,以估计沿海地区的不确定性。然后使用多项式Chaos代理和新开发的轮廓盒式盒方法探索集合统计数据。

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