闪电类型识别在雷电探测和气象防灾减灾等领域具有重要的作用。目前闪电类型识别面临数据多样性、波形特征复杂等难题,限制了识别算法的准确性和泛化能力。提出了一种基于多尺度残差网络和注意力机制的闪电波形多分类算法MSRES-SA(multi...闪电类型识别在雷电探测和气象防灾减灾等领域具有重要的作用。目前闪电类型识别面临数据多样性、波形特征复杂等难题,限制了识别算法的准确性和泛化能力。提出了一种基于多尺度残差网络和注意力机制的闪电波形多分类算法MSRES-SA(multi-scale residuals and self-attention),旨在提高闪电波形识别的准确性。首先构建了一个多尺度残差特征提取模块,用于提取闪电波形在时间维度上不同尺度的信息,并使用残差连接来增强模型的表征能力。然后使用注意力机制来动态加权重要特征,捕捉波形序列中的长距离关联。实验结果表明,MSRES-SA算法的平均识别精度为99.35%,在多个闪电波形类别识别中优于基线模型,并通过消融实验证明了多尺度残差模块和注意力模块在闪电波形识别任务中的有效性。展开更多
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
文摘闪电类型识别在雷电探测和气象防灾减灾等领域具有重要的作用。目前闪电类型识别面临数据多样性、波形特征复杂等难题,限制了识别算法的准确性和泛化能力。提出了一种基于多尺度残差网络和注意力机制的闪电波形多分类算法MSRES-SA(multi-scale residuals and self-attention),旨在提高闪电波形识别的准确性。首先构建了一个多尺度残差特征提取模块,用于提取闪电波形在时间维度上不同尺度的信息,并使用残差连接来增强模型的表征能力。然后使用注意力机制来动态加权重要特征,捕捉波形序列中的长距离关联。实验结果表明,MSRES-SA算法的平均识别精度为99.35%,在多个闪电波形类别识别中优于基线模型,并通过消融实验证明了多尺度残差模块和注意力模块在闪电波形识别任务中的有效性。
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.
