首页|Predictability Study of Weather and Climate Events Related to Artificial Intelligence Models

Predictability Study of Weather and Climate Events Related to Artificial Intelligence Models

扫码查看
原文链接
  • 万方数据
  • 维普
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition-observation-model paradigm.Comprehensive predictability studies have the potential to transform"big data"to"big and better data"and shift the focus from"AI for forecasts"to"AI for science",ultimately advancing the development of the atmospheric and oceanic sciences.

predictabilityartificial intelligence modelssimulation and forecastingnonlinear optimizationcognition-observation-model paradigm

Mu MU、Bo QIN、Guokun DAI

展开 >

Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China

Shanghai Key Laboratory of Ocean-land-atmosphere Boundary Dynamics and Climate Change,Fudan University,Shanghai 200438,China

Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction,Fudan University,Shanghai 200438,China

CMA-FDU Joint Laboratory of Marine Meteorology,Shanghai 200438,China

Key Laboratory of Polar Atmosphere-ocean-ice System for Weather and Climate,Ministry of Education,Shanghai 200438,China

展开 >

2025

10.1007/s00376-024-4372-7

大气科学进展(英文版)
中国科学院大气物理研究所

大气科学进展(英文版)

影响因子：0.741
ISSN：0256-1530
年,卷(期)：2025.42(1)