AI for Good | 人工智能如何改进天气和气候预测?
How can AI improve Weather and Climate Prediction?
人工智能如何改进天气和气候预测?
人工智能方法正在我们生活的几乎任何方面迅速扎根。在某些特定的应用领域，计算机在图像或音频分析、语音识别和控制动作方面的性能优于人类。然而，天气和气候预测仍然使用庞大的计算机代码，在最快的超级计算机上解决数千个微分方程。沿着这种预测系统的工作流程，研究人员正在尝试人工智能如何改变甚至革命性地改变天气和气候预测。虽然有一些很有前途的研究途径，但在我们看到气象中心和研究机构广泛采用这些方法之前，还必须克服一些科学和技术上的挑战。我们讨论了最近的成就和正在进行的活动，我可能很想推测人工智能概念在这个应用领域的基本的、固有的局限性。估计地球未来气候的不确定性来自于我们模型中的偏差，以及社会在这期间将要做出的大量可能的选择。气候模拟中最紧迫的不确定性之一是人为气溶胶的影响，特别是通过它们与云的相互作用。在这里，我将介绍一个通用的地球系统仿真框架，它利用了机器学习的进步，并描述了它在整个气候模型仿真中的应用，以减少这种不确定性。我还将演示如何利用这种模拟来更好地估计不同人为强迫因子对气候的反应，以帮助发现和归因这些因子，并探索未来不同的排放途径。 (彩云小译)
演讲者:
Duncan Watson-Parris
Martin Schultz
主持人:
Philip Stier，牛津大学大气、海洋和行星物理学负责人
时长：1小时33分
AI methods are rapidly taking hold in almost any aspect of our lives. In some specialized application areas, computers are outperforming humans with respect to image or audio analysis, speech recognition, and controlled movements. Nevertheless, weather and climate prediction still uses humongous computer codes, which solve thousands of differential equations on the fastest supercomputers. Everywhere along the workflow of such prediction systems researchers are trying out how AI could transform or even revolutionize weather and climate forecasting. While there are some promising research pathways, several scientific and technical challenges have to be overcome before we might see a widespread adaptation of such methods in weather centres and research organisations. We discuss recent achievements and ongoing activities and I may be tempted to speculate about fundamental, inherent limitations of AI concepts in this application area.
Uncertainties in estimating Earth’s future climate stem from both inaccuracies in our models and the vast array of possible choices that society will make in the intervening years. One of the most pressing uncertainties in climate modelling is that of the effect of anthropogenic aerosol, particularly through their interactions with clouds. Here I will introduce a general earth system emulation framework which leverages advances in machine learning and describe its application to the emulation of entire climate models for the reduction of this uncertainty. I will also demonstrate how such emulation can be used to better approximate the climate response to different anthropogenic forcing agents in order to aid in their detection and attribution, and in the exploration of different future emissions pathways.In partnership with @University of Oxford 🎙 Speakers: Duncan Watson-Parris, Postdoctoral Research Associate, University of Oxford Martin Schultz, Group leader, Earth System Data Exploration, Jülich Supercomputing Centre (JSC)🎙 Moderators: Philip Stier, Head of Atmospheric, Oceanic and Planetary Physics, University of Oxford
Website: https://aiforgood.itu.int/
What is AI for Good? The AI for Good series is the leading action-oriented, global & inclusive United Nations platform on AI. The Summit is organized all year, always online, in Geneva by the ITU with XPRIZE Foundation in partnership with over 35 sister United Nations agencies, Switzerland and ACM. The goal is to identify practical applications of AI and scale those solutions for global impact.Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.
来源：AI for Good
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