论文标题
使用概率机器学习在参数化中更好地建模时间模式：使用Lorenz 96模型进行案例研究
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
论文作者
论文摘要
小规模过程的建模是气候模型中误差的主要来源，阻碍了低成本模型的准确性，必须通过参数化近似此类过程。红噪声对于许多操作参数化方案至关重要，有助于建模时间相关。我们通过将随机性的已知好处与机器学习相结合，展示了如何建立红噪声的成功。这是在概率框架内使用物理信息的复发性神经网络完成的。当应用于Lorenz 96大气模拟时，我们的模型具有竞争力，通常优于定制基线和现有的概率机器学习方法（GAN）。这是由于其与标准一阶自回旋方案相比，其具有较高的时间模式的能力。这也是看不见的场景。我们从文献中评估了许多指标，还讨论了使用持有可能性的概率度量的好处。
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a physically-informed recurrent neural network within a probabilistic framework. Our model is competitive and often superior to both a bespoke baseline and an existing probabilistic machine learning approach (GAN) when applied to the Lorenz 96 atmospheric simulation. This is due to its superior ability to model temporal patterns compared to standard first-order autoregressive schemes. It also generalises to unseen scenarios. We evaluate across a number of metrics from the literature, and also discuss the benefits of using the probabilistic metric of hold-out likelihood.
