论文标题
分析长期海面温度预测的物理学机器学习方法
Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting
论文作者
论文摘要
准确地预测未来数周到几个月的海面温度是朝着长期天气预报迈出的重要一步。标准的大气 - 海洋耦合数值模型在几天到几周的规模上提供了准确的海面预测，但是许多重要的天气系统需要更大的远见。在本文中，我们建议机器学习接近海面温度预测，这些温度预测在数十周的规模上是准确的。我们的方法基于Koopman操作员理论，这是动态系统建模的有用工具。通过这种方法，我们根据当前的热条件和三年历史训练数据的形象，预测未来180天的海面温度长达180天。我们在各种排列中评估了基本的Koopman方法与卷积自动编码器的组合以及新提出的“一致的Koopman”方法的组合。我们表明，Koopman方法始终胜过基线，我们讨论了我们在这个海面温度域中其他假设和方法的实用性。
Accurately predicting sea-surface temperature weeks to months into the future is an important step toward long term weather forecasting. Standard atmosphere-ocean coupled numerical models provide accurate sea-surface forecasts on the scale of a few days to a few weeks, but many important weather systems require greater foresight. In this paper we propose machine-learning approaches sea-surface temperature forecasting that are accurate on the scale of dozens of weeks. Our approach is based in Koopman operator theory, a useful tool for dynamical systems modelling. With this approach, we predict sea surface temperature in the Gulf of Mexico up to 180 days into the future based on a present image of thermal conditions and three years of historical training data. We evaluate the combination of a basic Koopman method with a convolutional autoencoder, and a newly proposed "consistent Koopman" method, in various permutations. We show that the Koopman approach consistently outperforms baselines, and we discuss the utility of our additional assumptions and methods in this sea-surface temperature domain.
