Machine learning for weather and climate are worlds apart
Machine learning for weather and climate are worlds apart
复制标题
天气和气候的机器学习有天壤之别
DOI：
10.1098/rsta.2020.0098
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Watson‐Parris
中科院分区：
文献类型：
--
作者：
D. Watson‐Parris
现代天气和气候模型有着共同的传统，甚至经常有着共同的组成部分;然而，它们以不同的方式被用来回答根本不同的问题。因此，尝试使用机器学习来模拟它们应该反映这一点。虽然使用机器学习来模拟天气预报模型是一项相对较新的工作，但气候模型模拟有着丰富的历史。这主要是因为，虽然天气建模是一个初始条件问题，它密切依赖于大气的当前状态，气候建模主要是一个边界条件问题。因此，为了模拟气候对不同驱动因素的反应，既没有必要，也不需要对大气的全部动态演变进行描述。气候科学家通常也对不同的问题感兴趣。事实上，模拟稳态气候响应已经有很多年了，并提供了显着的速度增加，允许解决逆问题，例如参数估计。然而，大型数据集，非线性关系和有限的训练数据使气候成为一个充满有趣的机器学习挑战的领域。在这里，我试图阐述气候模型仿真的现状，并展示尽管存在一些挑战，机器学习的最新进展如何为创建有用的气候统计模型提供新的机会。这篇文章是“天气和气候建模的机器学习”主题的一部分。
Modern weather and climate models share a common heritage and often even components; however, they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour, there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem, which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. To emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make climate a domain which is rich in interesting machine learning challenges. Here, I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
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影响因子：
5.1
作者：
D. Williamson;A. Blaker;B. Sinha
通讯作者：
D. Williamson;A. Blaker;B. Sinha
影响因子：
6.3
作者：
Lee, L. A.;Carslaw, K. S.;Spracklen, D. V.
通讯作者：
Spracklen, D. V.
限制气溶胶直接强迫的不确定性
影响因子：
5.2
作者：
Watson-Parris D
通讯作者：
Watson-Parris D
DOI：
10.1073/pnas.1810286115
发表时间：
2018-09-25
影响因子：
11.1
作者：
Rasp S;Pritchard MS;Gentine P
通讯作者：
Gentine P