In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are revi...In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.展开更多
A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study em...A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.展开更多
2022年第4号台风“暹芭”在7月2日夜间进入广西后出现突然北折路径,导致风雨预报出现显著偏差,对台风防御工作造成重大影响。本文利用高空、地面、卫星等多源气象观测资料以及欧洲中期天气预报中心(European Centre for Medium-range We...2022年第4号台风“暹芭”在7月2日夜间进入广西后出现突然北折路径,导致风雨预报出现显著偏差,对台风防御工作造成重大影响。本文利用高空、地面、卫星等多源气象观测资料以及欧洲中期天气预报中心(European Centre for Medium-range Weather Forecasts,ECMWF)提供的第5代再分析资料(ECMWF re-analysis 5,ERA5),采用天气学诊断方法对台风路径北折的成因进行深入分析,并运用位涡倾向方程进行定量诊断。结果表明:(1)“暹芭”台风路径北折是大尺度环流形势变化导致的深层引导气流改变与台风内部非对称结构变化共同作用的结果;(2)深层引导气流在路径转折中起主导作用,西太平洋副热带高压的西伸加强、高空西风槽前和南亚高压单体西北侧的西南气流与台风北向出流的相互作用是引导气流改变的关键驱动因素;同时正涡度平流的变化对“暹芭”台风路径北折具有指示性意义;(3)“暹芭”台风呈现非对称结构特征,其内部垂直运动所引发的积云对流对台风北折有重要影响,台风云系形态变化也为台风移向的转折提供指示;(4)位涡倾向方程定量诊断进一步表明,台风在南海移动期间主要受外部大尺度环流形成的引导气流影响,而台风进入内陆后突然北折则是引导气流和台风非对称结构引发垂直运动共同作用的结果;此外“暹芭”台风具有趋向于位势倾向正值中心移动特征。展开更多
运用澳大利亚大气海洋耦合预报模式(Predictive Ocean Atmosphere Model for Australia,POAMA)的输出结果,采用泰勒图与分类统计分析方法,评估了该模式对2003和2004年南海夏季风的爆发和演变进行实时预报的能力。通过对泰勒图的分析发现...运用澳大利亚大气海洋耦合预报模式(Predictive Ocean Atmosphere Model for Australia,POAMA)的输出结果,采用泰勒图与分类统计分析方法,评估了该模式对2003和2004年南海夏季风的爆发和演变进行实时预报的能力。通过对泰勒图的分析发现,随着预报初始时间越来越接近实际的季风爆发时间,模式预报南海夏季风爆发和演变的能力越来越强。当提前1—30d预报南海夏季风时,模式能够很好地预报风场、射出长波辐射OLR(Outgoing Longwave Radiation)和降水场的空间分布,其中对风场的预报最好。通过对季风爆发指数和分类统计的分析,定量分析了模式预报南海夏季风爆发的能力,结果表明该模式对南海夏季风爆发时间有一定的预报能力,其最大预报时限可以提前10—15d左右,这与目前中期预报的上限(2周)是一致的。展开更多
基金sponsored by the National Natural Science Foun-dation of China(Grant No.42330111).
文摘In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.
基金supported by the National Natural Science Foundation of China [grant number 42030605]the National Key R&D Program of China [grant number 2020YFA0608004]。
文摘A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.
文摘2022年第4号台风“暹芭”在7月2日夜间进入广西后出现突然北折路径,导致风雨预报出现显著偏差,对台风防御工作造成重大影响。本文利用高空、地面、卫星等多源气象观测资料以及欧洲中期天气预报中心(European Centre for Medium-range Weather Forecasts,ECMWF)提供的第5代再分析资料(ECMWF re-analysis 5,ERA5),采用天气学诊断方法对台风路径北折的成因进行深入分析,并运用位涡倾向方程进行定量诊断。结果表明:(1)“暹芭”台风路径北折是大尺度环流形势变化导致的深层引导气流改变与台风内部非对称结构变化共同作用的结果;(2)深层引导气流在路径转折中起主导作用,西太平洋副热带高压的西伸加强、高空西风槽前和南亚高压单体西北侧的西南气流与台风北向出流的相互作用是引导气流改变的关键驱动因素;同时正涡度平流的变化对“暹芭”台风路径北折具有指示性意义;(3)“暹芭”台风呈现非对称结构特征,其内部垂直运动所引发的积云对流对台风北折有重要影响,台风云系形态变化也为台风移向的转折提供指示;(4)位涡倾向方程定量诊断进一步表明,台风在南海移动期间主要受外部大尺度环流形成的引导气流影响,而台风进入内陆后突然北折则是引导气流和台风非对称结构引发垂直运动共同作用的结果;此外“暹芭”台风具有趋向于位势倾向正值中心移动特征。
文摘运用澳大利亚大气海洋耦合预报模式(Predictive Ocean Atmosphere Model for Australia,POAMA)的输出结果,采用泰勒图与分类统计分析方法,评估了该模式对2003和2004年南海夏季风的爆发和演变进行实时预报的能力。通过对泰勒图的分析发现,随着预报初始时间越来越接近实际的季风爆发时间,模式预报南海夏季风爆发和演变的能力越来越强。当提前1—30d预报南海夏季风时,模式能够很好地预报风场、射出长波辐射OLR(Outgoing Longwave Radiation)和降水场的空间分布,其中对风场的预报最好。通过对季风爆发指数和分类统计的分析,定量分析了模式预报南海夏季风爆发的能力,结果表明该模式对南海夏季风爆发时间有一定的预报能力,其最大预报时限可以提前10—15d左右,这与目前中期预报的上限(2周)是一致的。
