Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI

被引：92

作者：

Chantry, Matthew ^{[1
]}

Christensen, Hannah ^{[1
]}

Dueben, Peter ^{[2
]}

Palmer, Tim ^{[1
]}

机构：

[1] Univ Oxford, Atmospher Ocean & Planetary Phys, Oxford, England

[2] European Ctr Medium RangeWeather Forecasts, Reading, Berks, England

来源：

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2021年 / 379卷 / 2194期

基金：

欧盟地平线“2020”; 欧洲研究理事会;

关键词：

weather prediction; machine learning; climate modelling; NEURAL-NETWORKS;

D O I：

10.1098/rsta.2020.0083

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In September 2019, a workshop was held to highlight the growing area of applying machine learning techniques to improve weather and climate prediction. In this introductory piece, we outline the motivations, opportunities and challenges ahead in this exciting avenue of research. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

引用

页数：8

共 41 条

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