The conditional nonlinear optimal perturbations（CNOPs） obtained by a fast algorithm are applied to determining the sensitive area for the targeting observation of Typhoon Matsa in 2005 using an operational regional ...The conditional nonlinear optimal perturbations（CNOPs） obtained by a fast algorithm are applied to determining the sensitive area for the targeting observation of Typhoon Matsa in 2005 using an operational regional prediction model-the Global/Regional Assimilation and PrEdiction System（GRAPES）.Through a series of sensitivity experiments,several issues on targeting strategy design are discussed,including the effectivity of different guidances to determine the sensitive area（or targeting area） and the impact of sensitive area size on improving the 24-h forecast.In this study,three guidances are used along with the CNOP to find sensitive area for improving the 24-h prediction of sea level pressure and accumulated rainfall in the verification region.The three guidances are based on winds only;on winds,geopotential height,and specific humidity;and on winds,geopotential height,specific humidity,and observation error,respectively.The distribution and effectivity of the sensitive areas are compared with each other,and the results show that the sensitive areas identified by the three guidances are different in terms of convergence and effectivity.All the sensitive areas determined by these guidances can lead to improvement of the 24-h forecast of interest. The second and third guidances are more effective and can identify more similar sensitive areas than the first one.Further,the size of sensitive areas is changed the same way for three guidances and the 24-h accumulated rainfall prediction is examined.The results suggest that a larger sensitive area can result in better prediction skill,provided that the guidance is sensitive to the size of sensitive areas.展开更多
The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear o...The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.展开更多
Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) metho...Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) methods.A series of sensitivity experiments were designed to test the sensitivity of sensitive areas within the numerical model.The following results were obtained:(1) the effect of initial CNOP and FSV patterns in their sensitive areas is greater than that of the same patterns in randomly selected areas,with the effect of the initial CNOP patterns in CNOP sensitive areas being the greatest;(2) both CNOP- and FSV-type initial errors grow more quickly than random errors;(3) the effect of random errors superimposed on the sensitive areas is greater than that of random errors introduced into randomly selected areas,and initial errors in the CNOP sensitive areas have greater effects on final forecasts.These results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected areas.In addition,ideal hindcasting experiments were conducted to examine the validity of the sensitive areas.The results indicate that reduction(or elimination) of CNOP-type errors in CNOP sensitive areas at the initial time has a greater forecast benefit than the reduction(or elimination) of FSVtype errors in FSV sensitive areas.These results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.展开更多
With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational dat...With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.展开更多
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observation...In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.展开更多
Based on the viewpoint that the North Atlantic Oscillation(NAO)has an intrinsic timescale of approximate two weeks and can be treated as an initial value problem,targeted observations for improving the prediction of t...Based on the viewpoint that the North Atlantic Oscillation(NAO)has an intrinsic timescale of approximate two weeks and can be treated as an initial value problem,targeted observations for improving the prediction of the onset of NAO events are investigated by using the conditional nonlinear optimal perturbation(CNOP)method with a quasigeostrophic model.The results show that flow-dependent sensitive areas for the prediction of NAO onset are mainly located over North Atlantic and its upstream regions.Targeted observations over the main sensitive areas could improve NAO onset prediction in most cases(approximately 75%)due to reduced errors in anomalous eddy vorticity forcing(EVF)projection in the typical NAO mode.Moreover,a flow-independent sensitive area is determined based on the winter climatological flow,which is located over North America and its adjacent ocean.The NAO onset prediction can also be improved by targeted observations over the flow-independent sensitive area,but the skill improvement is somewhat lower than that derived from observations over the flow-dependent sensitive area.The above results indicate that targeted observations over sensitive areas identified by the CNOP method can help to improve the onset prediction of NAO events.展开更多
Within the frame of the Zebiak-Cane model,the impact of the uncertainties of the Madden-Julian Oscillation（MJO） on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal for...Within the frame of the Zebiak-Cane model,the impact of the uncertainties of the Madden-Julian Oscillation（MJO） on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing.The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation（CNOP）;compared to CNOP-type initial error,the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO,and its influence over the ENSO predictability was not significant.This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction,which could provide a theoretical foundation for the data assimilation of the ENSO forecast.展开更多
In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we si...In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram, then choose two equilibrium states (called jet-up state and jet-down state) as reference states respectively, propose Principal Component Analysis-based Simulated Annealing (PCASA) algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and jet-down state. PCASA algorithm is an adjoint-free method which searches optimal solution randomly in the whole solution space. In addition, we investigate CNOP-type initial perturbations how to evolve with time. The results show:(1) the CNOP-type perturbations present a two-cell structure, and gradually evolves into a three-cell structure at predictive time;(2) by superimposing CNOP-type perturbations on the jet-up state and integrating ROMS, double-gyre circulation transfers from jet-up state to jet-down state, and vice versa, and random initial perturbations don't cause the transitions, which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions;(3) by analyzing the transition process of double-gyre regime transitions, we find that CNOP-type initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities, and barotropic instability contributes more significantly to the fast-growth of the perturbations. The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.展开更多
Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonl...Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.展开更多
基金Supported by the State Key 11th Five-Year Project on Sci.& Tech.under Grant No.2006BAC02B03the China Meteorological Administration R & D Special Fund for Public Welfare(meteorology) under Grant No.GYHY(QX)2007-6-12the National Natural Science Foundation of China under Grant No.40605018
文摘The conditional nonlinear optimal perturbations（CNOPs） obtained by a fast algorithm are applied to determining the sensitive area for the targeting observation of Typhoon Matsa in 2005 using an operational regional prediction model-the Global/Regional Assimilation and PrEdiction System（GRAPES）.Through a series of sensitivity experiments,several issues on targeting strategy design are discussed,including the effectivity of different guidances to determine the sensitive area（or targeting area） and the impact of sensitive area size on improving the 24-h forecast.In this study,three guidances are used along with the CNOP to find sensitive area for improving the 24-h prediction of sea level pressure and accumulated rainfall in the verification region.The three guidances are based on winds only;on winds,geopotential height,and specific humidity;and on winds,geopotential height,specific humidity,and observation error,respectively.The distribution and effectivity of the sensitive areas are compared with each other,and the results show that the sensitive areas identified by the three guidances are different in terms of convergence and effectivity.All the sensitive areas determined by these guidances can lead to improvement of the 24-h forecast of interest. The second and third guidances are more effective and can identify more similar sensitive areas than the first one.Further,the size of sensitive areas is changed the same way for three guidances and the 24-h accumulated rainfall prediction is examined.The results suggest that a larger sensitive area can result in better prediction skill,provided that the guidance is sensitive to the size of sensitive areas.
基金supported bythe National Natural Science Foundation of China(Grant Nos40975063 and 40830955)
文摘The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.
基金Supported by the National Natural Science Foundation of China(Nos.41230420,41306023)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA11010303)the NSFC-Shandong Joint Fund for Marine Science Research Centers(No.U1406401)
文摘Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) methods.A series of sensitivity experiments were designed to test the sensitivity of sensitive areas within the numerical model.The following results were obtained:(1) the effect of initial CNOP and FSV patterns in their sensitive areas is greater than that of the same patterns in randomly selected areas,with the effect of the initial CNOP patterns in CNOP sensitive areas being the greatest;(2) both CNOP- and FSV-type initial errors grow more quickly than random errors;(3) the effect of random errors superimposed on the sensitive areas is greater than that of random errors introduced into randomly selected areas,and initial errors in the CNOP sensitive areas have greater effects on final forecasts.These results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected areas.In addition,ideal hindcasting experiments were conducted to examine the validity of the sensitive areas.The results indicate that reduction(or elimination) of CNOP-type errors in CNOP sensitive areas at the initial time has a greater forecast benefit than the reduction(or elimination) of FSVtype errors in FSV sensitive areas.These results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.
基金The National Natural Science Foundation of China under contract No.41405062
文摘With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.
基金supported by National Natural Science Foundation of China (Grant Nos. 40775050,40975049,and 40810059003)National Basic Research Program of China (Grant No.2011CB952002)
文摘In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
基金Supported by the National Natural Science Foundation of China(41775001)Technology Development Foundation of Chinese Academy of Meteorological Sciences(2018KJ036).
文摘Based on the viewpoint that the North Atlantic Oscillation(NAO)has an intrinsic timescale of approximate two weeks and can be treated as an initial value problem,targeted observations for improving the prediction of the onset of NAO events are investigated by using the conditional nonlinear optimal perturbation(CNOP)method with a quasigeostrophic model.The results show that flow-dependent sensitive areas for the prediction of NAO onset are mainly located over North Atlantic and its upstream regions.Targeted observations over the main sensitive areas could improve NAO onset prediction in most cases(approximately 75%)due to reduced errors in anomalous eddy vorticity forcing(EVF)projection in the typical NAO mode.Moreover,a flow-independent sensitive area is determined based on the winter climatological flow,which is located over North America and its adjacent ocean.The NAO onset prediction can also be improved by targeted observations over the flow-independent sensitive area,but the skill improvement is somewhat lower than that derived from observations over the flow-dependent sensitive area.The above results indicate that targeted observations over sensitive areas identified by the CNOP method can help to improve the onset prediction of NAO events.
基金sponsored by the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No. KZCX2-YW-QN203)the National Basic Research Program of China (Grant Nos. 2012CB955202 and 2010CB950402)the National Natural Science Foundation of China (Grant No. 40821092)
文摘Within the frame of the Zebiak-Cane model,the impact of the uncertainties of the Madden-Julian Oscillation（MJO） on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing.The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation（CNOP）;compared to CNOP-type initial error,the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO,and its influence over the ENSO predictability was not significant.This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction,which could provide a theoretical foundation for the data assimilation of the ENSO forecast.
基金Supported by the National Natural Science Foundation of China(No.41405097)the Fundamental Research Funds for the Central Universities of China in 2017
文摘In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram, then choose two equilibrium states (called jet-up state and jet-down state) as reference states respectively, propose Principal Component Analysis-based Simulated Annealing (PCASA) algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and jet-down state. PCASA algorithm is an adjoint-free method which searches optimal solution randomly in the whole solution space. In addition, we investigate CNOP-type initial perturbations how to evolve with time. The results show:(1) the CNOP-type perturbations present a two-cell structure, and gradually evolves into a three-cell structure at predictive time;(2) by superimposing CNOP-type perturbations on the jet-up state and integrating ROMS, double-gyre circulation transfers from jet-up state to jet-down state, and vice versa, and random initial perturbations don't cause the transitions, which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions;(3) by analyzing the transition process of double-gyre regime transitions, we find that CNOP-type initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities, and barotropic instability contributes more significantly to the fast-growth of the perturbations. The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.
基金Supported by the National Natural Science Foundation of China(No.41405097)the Fundamental Research Funds for the Central Universities of China in 2017
文摘Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.
