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 an...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.展开更多
大模型开启了人工智能发展的新阶段,人工智能赋能科学研究(AIfor Science)正成为全新的科学研究范式,体现出跨学科融合、人机协同共创等新特点。当前,大模型如何赋能哲学社会科学研究(AI for Social Science),其范式与路径尚不明确。本...大模型开启了人工智能发展的新阶段,人工智能赋能科学研究(AIfor Science)正成为全新的科学研究范式,体现出跨学科融合、人机协同共创等新特点。当前,大模型如何赋能哲学社会科学研究(AI for Social Science),其范式与路径尚不明确。本文首先回顾了研究范式的演进,并提炼出一种以大模型与研究者协同共创为特征的大模型赋能哲学社会科学研究的新范式。在此基础上,构建了一种以人为中心的“生成—批判—共创”实施路径。最后,本文以大模型助力艺术传承为例,为大模型赋能哲学社会科学研究提供案例参考。展开更多
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘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.
文摘大模型开启了人工智能发展的新阶段,人工智能赋能科学研究(AIfor Science)正成为全新的科学研究范式,体现出跨学科融合、人机协同共创等新特点。当前,大模型如何赋能哲学社会科学研究(AI for Social Science),其范式与路径尚不明确。本文首先回顾了研究范式的演进,并提炼出一种以大模型与研究者协同共创为特征的大模型赋能哲学社会科学研究的新范式。在此基础上,构建了一种以人为中心的“生成—批判—共创”实施路径。最后,本文以大模型助力艺术传承为例,为大模型赋能哲学社会科学研究提供案例参考。
