A sensitivity analysis of a regression model of ocean temperature
A sensitivity analysis of a regression model of ocean temperature
复制标题
海洋温度回归模型的敏感性分析
DOI：
10.1017/eds.2022.10
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Furner R
中科院分区：
文献类型：
--
作者：
Furner R
最近人们对开发用于天气和气候预测的数据驱动模型很感兴趣。然而，关于它们的普遍性和鲁棒性，还有一些悬而未决的问题，这突出表明需要更好地了解它们是如何做出预测的。特别是，重要的是要了解数据驱动模型是否学习了它们所训练的系统的底层物理，或者只是识别统计模式，而没有任何与底层物理的明确联系。在本文中，我们描述了一个基于回归的海洋温度模型的敏感性分析，该模型是在一个非常简单的配置中从3D海洋模型设置中进行模拟训练的。我们表明，回归严重的基础上，它的预测，并依赖于已知的关键的物理变量，如电流和密度。相比之下，回归因子没有大量使用位置等输入，这些输入的直接物理影响有限。该模型需要输入之间的非线性相互作用，以显示任何有意义的技能，符合海洋的高度非线性动力学。进一步的分析解释了回归模型使用某些变量的方式。我们看到，关于水柱的垂直剖面的信息减少了对流活动区域的误差，而关于水流的信息减少了由平流过程主导的区域的误差。我们的研究结果表明，即使是一个简单的回归模型也能够学习被建模系统的大部分物理特性。我们希望类似的敏感性分析可以有效地应用于更复杂的海洋配置。
There has been much recent interest in developing data-driven models for weather and climate predictions. However, there are open questions regarding their generalizability and robustness, highlighting a need to better understand how they make their predictions. In particular, it is important to understand whether data-driven models learn the underlying physics of the system against which they are trained, or simply identify statistical patterns without any clear link to the underlying physics. In this paper, we describe a sensitivity analysis of a regression-based model of ocean temperature, trained against simulations from a 3D ocean model setup in a very simple configuration. We show that the regressor heavily bases its forecasts on, and is dependent on, variables known to be key to the physics such as currents and density. By contrast, the regressor does not make heavy use of inputs such as location, which have limited direct physical impacts. The model requires nonlinear interactions between inputs in order to show any meaningful skill—in line with the highly nonlinear dynamics of the ocean. Further analysis interprets the ways certain variables are used by the regression model. We see that information about the vertical profile of the water column reduces errors in regions of convective activity, and information about the currents reduces errors in regions dominated by advective processes. Our results demonstrate that even a simple regression model is capable of learning much of the physics of the system being modeled. We expect that a similar sensitivity analysis could be usefully applied to more complex ocean configurations.
利用流体同构的大气-海洋模型
DOI：
--
发表时间：
2004
期刊：
影响因子：
--
作者：
J. Marshall;A. Adcroft;J. Campin;C. Hill;A. White
通讯作者：
A. White
在洛伦兹系统上训练的前馈神经网络的泛化特性
影响因子：
2.2
作者：
S. Scher;G. Messori
通讯作者：
G. Messori
用于识别回归问题的熟练预测的受控弃权神经网络
影响因子：
6.8
作者：
Barnes, Elizabeth A.;Barnes, Randal J.
通讯作者：
Barnes, Randal J.