Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest-generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning, across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in earth systems. A difficult test for deep learning-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A deep learning emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here we examine, with a case study in satellite-based remote sensing, the hypothesis that deep learning approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the deep learning emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of deep learning as well as the growing desire for higher-resolution simulations in the earth sciences.
翻译:以物理为基础的数字模型代表着地球系统模型中的最新先进技术,包括了我们产生洞察力和预测的最佳工具。尽管计算能力迅速增长,但人们认识到需要更高的模型分辨率来压倒最新一代的计算机,降低了模型家为理解参数敏感性和确定变异性和不确定性而进行模拟的能力。因此,往往会开发替代模型,以捕捉完整数字模型的基本属性。最近在许多学科中,机器学习方法的成功,特别是深层学习,提供了复杂的非线性联系学表现可能能够捕捉到地球系统中的复杂结构和非线性进程的潜在希望。深层次基于学习的模拟模型的难度测试,这是指数字模型的功能的近似近似性。从计算效率的角度来理解这些模型能否与传统形式的模拟模型相仿,同时以可信的方式复制模型的结果。经过这一测试的深层次学习模拟模型可能比简单模型的更能表现得更好,以捕捉到复杂的进程和偏差依赖性能模型。在这里,我们通过在深层次的模型中进行模拟研究,在深度的模拟中,我们从深层次的学习结果中,可以很好地分析,我们从深层次的模拟中学习结果。