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Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability
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作者 Ming ZHANG Ruiqiang DING +2 位作者 Quanjia ZHONG Jianping LI Deyu LU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第9期1769-1786,共18页
In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The eff... In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere. 展开更多
关键词 conditional nonlinear local lyapunov exponent second-kind predictability coupled Lorenz model ENSO
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Determining the Spectrum of the Nonlinear Local Lyapunov Exponents in a Multidimensional Chaotic System 被引量:6
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作者 Ruiqiang DING Jianping LI Baosheng LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2017年第9期1027-1034,共8页
For an n-dimensional chaotic system, we extend the definition of the nonlinear local Lyapunov exponent (NLLE) from one- to n-dimensional spectra, and present a method for computing the NLLE spectrum. The method is t... For an n-dimensional chaotic system, we extend the definition of the nonlinear local Lyapunov exponent (NLLE) from one- to n-dimensional spectra, and present a method for computing the NLLE spectrum. The method is tested on three chaotic systems with different complexity. The results indicate that the NLLE spectrum realistically characterizes the growth rates of initial error vectors along different directions from the linear to nonlinear phases of error growth. This represents an improvement over the traditional Lyapunov exponent spectrum, which only characterizes the error growth rates during the linear phase of error growth. In addition, because the NLLE spectrum can effectively separate the slowly and rapidly growing perturbations, it is shown to be more suitable for estimating the predictability of chaotic systems, as compared to the traditional Lyapunov exponent spectrum. 展开更多
关键词 lyapunov exponent nonlinear local lyapunov exponent PREDICTABILITY
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Quantitative Comparison of Predictabilities of Warm and Cold Events Using the Backward Nonlinear Local Lyapunov Exponent Method 被引量:1
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作者 Xuan LI Ruiqiang DING Jianping LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2020年第9期951-958,共8页
The backward nonlinear local Lyapunov exponent method(BNLLE)is applied to quantify the predictability of warm and cold events in the Lorenz model.Results show that the maximum prediction lead times of warm and cold ev... The backward nonlinear local Lyapunov exponent method(BNLLE)is applied to quantify the predictability of warm and cold events in the Lorenz model.Results show that the maximum prediction lead times of warm and cold events present obvious layered structures in phase space.The maximum prediction lead times of each warm(cold)event on individual circles concentric with the distribution of warm(cold)regime events are roughly the same,whereas the maximum prediction lead time of events on other circles are different.Statistical results show that warm events are more predictable than cold events. 展开更多
关键词 backward nonlinear local lyapunov exponent maximum prediction lead time layered structure statistical result
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Quantifying local predictability of the Lorenz system using the nonlinear local Lyapunov exponent
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作者 HUAI Xiao-Wei LI Jian-Ping +2 位作者 DING Rui-Qiang FENG Jie LIU De-Qiang 《Atmospheric and Oceanic Science Letters》 CSCD 2017年第5期372-378,共7页
The nonlinear local Lyapunov exponent(NLLE) can be used as a quantification of the local predictability limit of chaotic systems. In this study, the phase-spatial structure of the local predictability limit over the... The nonlinear local Lyapunov exponent(NLLE) can be used as a quantification of the local predictability limit of chaotic systems. In this study, the phase-spatial structure of the local predictability limit over the Lorenz-63 system is investigated. It is found that the inner and outer rims of each regime of the attractor have a high probability of a longer than average local predictability limit, while the center part is the opposite. However, the distribution of the local predictability limit is nonuniformly organized, with adjacent points sometimes showing quite distinct error growth.The source of local predictability is linked to the local dynamics, which is related to the region in the phase space and the duration on the current regime. 展开更多
关键词 nonlinear local lyapunov exponent local predictability limit chaotic system Lorenz system
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Estimation of the Monthly Precipitation Predictability Limit in China Using the Nonlinear Local Lyapunov Exponent 被引量:5
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作者 刘景鹏 李维京 +2 位作者 陈丽娟 左金清 张培群 《Journal of Meteorological Research》 SCIE CSCD 2016年第1期93-102,共10页
By using the nonlinear local Lyapunov exponent and nonlinear error growth dynamics, the predictability limit of monthly precipitation is quantitatively estimated based on daily observations collected from approx- imat... By using the nonlinear local Lyapunov exponent and nonlinear error growth dynamics, the predictability limit of monthly precipitation is quantitatively estimated based on daily observations collected from approx- imately 500 stations in China for the period 1960-2012. As daily precipitation data are not continuous in space and time, a transformation is first applied and a monthly standardized precipitation index (SPI) with Gaussian distribution is constructed. The monthly SPI predictability limit (MSPL) is quantitatively calcu- lated for SPI dry, wet, and neutral phases. The results show that the annual mean MSPL varies regionally for both wet and dry phases: the MSPL in the wet (dry) phase is relatively higher (lower) in southern China than in other regions. Further, the pattern of the MSPL for the wet phase is almost opposite to that for the dry phase in both autumn and winter. The MSPL in the dry phase is higher in winter and lower in spring and autumn in southern China, while the MSPL values in the wet phase are higher in summer and winter than those in spring and autumn in southern China. The spatial distribution of the MSPL resembles that of the prediction skill of monthly precipitation from a dynamic extended-range forecast system. 展开更多
关键词 monthly precipitation nonlinear local lyapunov exponent (NLLE) PREDICTABILITY spatial distribution
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The Predictability Limit of Oceanic Mesoscale Eddy Tracks in the South China Sea
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作者 Hailong LIU Pingxiang CHU +5 位作者 Yao MENG Mengrong DING Pengfei LIN Ruiqiang DING Pengfei WANG Weipeng ZHENG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第9期1661-1679,共19页
Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seas... Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5-10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS. 展开更多
关键词 PREDICTABILITY mesoscale eddy nonlinear local lyapunov exponent South China Sea seasonal variability
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Influence of Sea Surface Temperature on the Predictability of Idealized Tropical Cyclone Intensity 被引量:1
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作者 ZHONG Quan-jia LI Jian-ping +3 位作者 LI Shu-wen WANG Yuan DING Rui-qiang ZHANG Li-feng 《Journal of Tropical Meteorology》 SCIE 2021年第4期355-367,共13页
The role of sea surface temperature(SST)forcing in the development and predictability of tropical cyclone(TC)intensity is examined using a large set of idealized numerical experiments in the Weather Research and Forec... The role of sea surface temperature(SST)forcing in the development and predictability of tropical cyclone(TC)intensity is examined using a large set of idealized numerical experiments in the Weather Research and Forecasting(WRF)model.The results indicate that the onset time of rapid intensification of TC gradually decreases,and the peak intensity of TC gradually increases,with the increased magnitude of SST.The predictability limits of the maximum 10 m wind speed(MWS)and minimum sea level pressure(MSLP)are~72 and~84 hours,respectively.Comparisons of the analyses of variance for different simulation time confirm that the MWS and MSLP have strong signal-to-noise ratios(SNR)from 0-72 hours and a marked decrease beyond 72 hours.For the horizontal and vertical structures of wind speed,noticeable decreases in the magnitude of SNR can be seen as the simulation time increases,similar to that of the SLP or perturbation pressure.These results indicate that the SST as an external forcing signal plays an important role in TC intensity for up to 72 hours,and it is significantly weakened if the simulation time exceeds the predictability limits of TC intensity. 展开更多
关键词 PREDICTABILITY nonlinear local lyapunov exponent signal-to-noise ratios tropical cyclone intensity sea surface temperature
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