Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-ra...Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of...An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.展开更多
Impacts of initial conditions on cloud-resolving model simulations are investigated using a series of sensitivity experiments. Five experiments with perturbed initial temperature, moisture, and cloud conditions are co...Impacts of initial conditions on cloud-resolving model simulations are investigated using a series of sensitivity experiments. Five experiments with perturbed initial temperature, moisture, and cloud conditions are conducted and compared to the control experiment. The model is forced by the large-scale vertical velocity and zonal wind observed and derived from NCEP/Global Data Assimilation System （GDAS）. The results indicate that model predictions of rainfall are much more sensitive to the initial conditions than those of temperature and moisture. Further analyses of the surface rainfall equation and the moisture and cloud hydrometeor budgets reveal that the calculations of vapor condensation and deposition rates in the model account for the large sensitivities in rainfall simulations.展开更多
The basic objects of investigation in this article are nonlinear impulsive dif- ferential equations. The impulsive moments coincide with the moments of meeting of the integral curve and some of the so-called barrier c...The basic objects of investigation in this article are nonlinear impulsive dif- ferential equations. The impulsive moments coincide with the moments of meeting of the integral curve and some of the so-called barrier curves. For such type of equations, suf- ficient conditions are found under which the solutions are continuously dependent on the perturbations with respect to the initial conditions and barrier curves. The results are applied to a mathematical model of population dynamics.展开更多
Focusing on the role of initial condition uncertainty,we use WRF initial perturbation ensemble forecasts to investigate the uncertainty in intensity forecasts of Tropical Cyclone(TC)Rammasun(1409),which is the stronge...Focusing on the role of initial condition uncertainty,we use WRF initial perturbation ensemble forecasts to investigate the uncertainty in intensity forecasts of Tropical Cyclone(TC)Rammasun(1409),which is the strongest TC to have made landfall in China during the past 50 years.Forecast results indicate that initial condition uncertainty leads to TC forecast uncertainty,particularly for TC intensity.This uncertainty increases with forecast time,with a more rapid and significant increase after 24 h.The predicted TC develops slowly before 24 h,and at this stage the TC in the member forecasting the strongest final TC is not the strongest among all members.However,after 24 h,the TC in this member strengthens much more than that the TC in other members.The variations in convective instability,precipitation,surface upward heat flux,and surface upward water vapor flux show similar characteristics to the variation in TC intensity,and there is a strong correlation between TC intensity and both the surface upward heat flux and the surface upward water vapor flux.The initial condition differences that result in the maximum intensity difference are smaller than the errors in the analysis system.Differences in initial humidity,and to a lesser extent initial temperature differences,at the surface and at lower heights are the key factors leading to differences in the forecasted TC intensity.These differences in initial humidity and temperature relate to both the overall values and distribution of these parameters.展开更多
This study uses ECMWF fifth-generation reanalysis, ERA5, which extends to the mesopause, to construct the Initial Conditions(IC) for WACCM(Whole Atmosphere Community Climate Model) simulations. Because the biases betw...This study uses ECMWF fifth-generation reanalysis, ERA5, which extends to the mesopause, to construct the Initial Conditions(IC) for WACCM(Whole Atmosphere Community Climate Model) simulations. Because the biases between ERA5 and Sounding of the Atmosphere using Broadband Emission Radiometry(SABER) temperature data are within ±5 K below the lower mesosphere,ERA5 reanalysis is used to construct IC in the lower atmosphere. Four experiments are performed to simulate a Stratospheric Sudden Warming(SSW) event from 5 to 15 February 2016. The simulation using the WACCM default climatic IC cannot represent the sharp meteorological variation during SSW. In contrast, the 0～4 d forecast results driven by ERA5-constructed IC is consistent with ERA5 reanalysis below the middle mesosphere. Comparing with WACCM climatology ICs scheme, the ICs constructing method based on ERA5 reanalysis can obtain 67%, 40%, 22%, 4% and 6% reduction of temperature forecast RMSE at 10 hPa, 1 hPa, 0.1 hPa, 0.01 hPa and 0.001 hPa respectively. However,such improvement is not shown in the lower thermosphere.展开更多
Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction s...Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.展开更多
Initial condition and model errors both contribute to the loss of atmospheric predictability.However,it remains debatable which type of error has the larger impact on the prediction lead time of specific states.In thi...Initial condition and model errors both contribute to the loss of atmospheric predictability.However,it remains debatable which type of error has the larger impact on the prediction lead time of specific states.In this study,we perform a theoretical study to investigate the relative effects of initial condition and model errors on local prediction lead time of given states in the Lorenz model.Using the backward nonlinear local Lyapunov exponent method,the prediction lead time,also called local backward predictability limit(LBPL),of given states induced by the two types of errors can be quantitatively estimated.Results show that the structure of the Lorenz attractor leads to a layered distribution of LBPLs of states.On an individual circular orbit,the LBPLs are roughly the same,whereas they are different on different orbits.The spatial distributions of LBPLs show that the relative effects of initial condition and model errors on local backward predictability depend on the locations of given states on the dynamical trajectory and the error magnitudes.When the error magnitude is fixed,the differences between the LBPLs vary with the locations of given states.The larger differences are mainly located on the inner trajectories of regimes.When the error magnitudes are different,the dissimilarities in LBPLs are diverse for the same given state.展开更多
A novel approach to the inverse problem of diffusively coupled map lattices is systematically investigated by utilizing the symbolic vector dynamics. The relationship between the performance of initial condition estim...A novel approach to the inverse problem of diffusively coupled map lattices is systematically investigated by utilizing the symbolic vector dynamics. The relationship between the performance of initial condition estimation and the structural feature of dynamical system is proved theoretically. It is found that any point in a spatiotemporal coupled system is not necessary to converge to its initial value with respect to sufficient backward iteration, which is directly relevant to the coupling strength and local mapping function. When the convergence is met, the error bound in estimating the initial condition is proposed in a noiseless environment, which is determined by the dimension of attractors and metric entropy of the system. Simulation results further confirm the theoretic analysis, and prove that the presented method provides the important theory and experimental results for better analysing and characterizing the spatiotemporal complex behaviours in an actual system.展开更多
The initial condition Ωde（zini）=n^2（1＋zini）^-2/4 at zini = 2000,widely used to solve the differential equation of the density of the new agegraphic dark energy（NADE） Ωde,makes the NADE model a single-paramete...The initial condition Ωde（zini）=n^2（1＋zini）^-2/4 at zini = 2000,widely used to solve the differential equation of the density of the new agegraphic dark energy（NADE） Ωde,makes the NADE model a single-parameter dark-energy cosmological model.However,we find that this initial condition is only applicable in a flat universe with only dark energy and pressureless matter.In fact,in order to obtain more information from current observational data,such as the cosmic microwave background（CMB） and the baryon acoustic oscillations（BAO）,we need to consider the contribution of radiation.For this situation,the initial condition mentioned above becomes invalid.To overcome this shortcoming,we investigate the evolutions of dark energy in matter-dominated and radiation-dominated epochs,and obtain a new initial condition de（zini）=n2（1＋zini）-2（1＋F（zini））2/4 at z ini = 2000,where F（z）≡Ωr0（1＋z）/[Ωm0＋Ωr0（1＋z）] with Ωr0 and Ωm0 being the current density parameters of radiation and pressureless matter,respectively.This revised initial condition is applicable for the differential equation of Ωde obtained in the standard Friedmann-Robertson-Walker（FRW） universe with dark energy,pressureless matter,radiation,and even spatial curvature,and can still keep the NADE model as a single-parameter model.With the revised initial condition and the observational data of type Ia supernova（SNIa）,CMB,and BAO,we finally constrain the NADE model.The results show that the single free parameter n of the NADE model can be constrained tightly.展开更多
In this paper,we study the high-order nonlinear Schrodinger equation with periodic initial conditions via the unified transform method extended by Fokas and Lenells.For the high-order nonlinear Schrodinger equation,th...In this paper,we study the high-order nonlinear Schrodinger equation with periodic initial conditions via the unified transform method extended by Fokas and Lenells.For the high-order nonlinear Schrodinger equation,the initial value problem on the circle can be expressed in terms of the solution of a Riemann–Hilbert problem.The related jump matrix can be explicitly expressed based on the initial data alone.Furthermore,we present the explicit solution,which corresponds to a one-gap solution.展开更多
System identification is a method for using measured data to create or improve a mathematical model of the object being tested. From the measured data however, noise is noticed at the beginning of the response. One so...System identification is a method for using measured data to create or improve a mathematical model of the object being tested. From the measured data however, noise is noticed at the beginning of the response. One solution to avoid this noise problem is to skip the noisy data and then use the initial conditions as active parameters, to be found by using the system identification process. This paper describes the development of the equations for setting up the initial conditions as active parameters. The simulated data and response data from actual shear buildings were used to prove the accuracy of both the algorithm and the computer program, which include the initial conditions as active parameters. The numerical and experimental model analysis showed that the value of mass, stiffness and frequency were very reasonable and that the computed acceleration and measured acceleration matched very well.展开更多
Symmetrical passive running of a simple spring model is commonly used to predict the locomotion of real animals and develop the design strategies for legged systems. If the initial conditions for running can be found ...Symmetrical passive running of a simple spring model is commonly used to predict the locomotion of real animals and develop the design strategies for legged systems. If the initial conditions for running can be found automatically, we can expand the ranges of the model parameters freely. To achieve this objective, we derive an efcient searching procedure based on analysis of the symmetries. Using this process, the desired initial states of the passive running are obtained. Existence of the results reveals that the leg symmetries can be used to generate the symmetrical running. Moreover, the leg forces are associated with the running pattern, implying that we should choose suitable motion for the given model characteristics.展开更多
A novel computationally efficient algorithm in terms of the time-varying symbolic dynamic method is proposed to estimate the unknown initial conditions of coupled map lattices （CMLs）. The presented method combines s...A novel computationally efficient algorithm in terms of the time-varying symbolic dynamic method is proposed to estimate the unknown initial conditions of coupled map lattices （CMLs）. The presented method combines symbolic dynamics with time-varying control parameters to develop a time-varying scheme for estimating the initial condition of multi-dimensional spatiotemporal chaotic signals. The performances of the presented time-varying estimator in both noiseless and noisy environments are analysed and compared with the common time-invariant estimator. Simulations are carried out and the obtained results show that the proposed method provides an efficient estimation of the initial condition of each lattice in the coupled system. The algorithm cannot yield an asymptotically unbiased estimation due to the effect of the coupling term, but the estimation with the time-varying algorithm is closer to the Cramer-Rao lower bound （CRLB） than that with the time-invariant estimation method, especially at high signal-to-noise ratios （SNRs）.展开更多
In this paper, a class of one-dimension p-Laplacian equation with nonlocal initial value is studied. The existence of solutions to such a problem is obtained by using the topological degree method.
The existence,uniqueness,and continuous dependence to the mild solutions of the nonlocal Cauchy problem were proved for a class of semilinear fractional neutral differential equations.The results are obtained by using...The existence,uniqueness,and continuous dependence to the mild solutions of the nonlocal Cauchy problem were proved for a class of semilinear fractional neutral differential equations.The results are obtained by using the Krasnoselskii's fixed point theorem and the theory of resolvent operators for integral equations.展开更多
A chimera state consisting of both coherent and incoherent groups is a fascinating spatial pattern in non-locally coupled identical oscillators. It is thought that random initial conditions hardly evolve to chimera st...A chimera state consisting of both coherent and incoherent groups is a fascinating spatial pattern in non-locally coupled identical oscillators. It is thought that random initial conditions hardly evolve to chimera states. In this work, we study the dependence of chimera states on initial conditions. We show that random initial conditions may lead to chimera states and the chance of realizing chimera states becomes increasing when the model parameters axe moving away from the boundary of their stable regime.展开更多
This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula...This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.展开更多
Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling.This study emphasized the influence of the initial soil temperature(ST)an...Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling.This study emphasized the influence of the initial soil temperature(ST)and soil moisture(SM)conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau(TP)using the Community Land Model version 5.0(CLM5.0).The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic,and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site.Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes.The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost,which coexists with soil liquid water(SLW),and soil ice(SI)when the ST is below freezing temperature,effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes.Consequently,the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method.Three modified initial soil schemes experiments resulted in a 64%,88%,and 77%reduction in the average mean bias error(MBE)of ST,and a 13%,21%,and 19%reduction in the average root-mean-square error(RMSE)of SLW compared to the default simulation results.Also,the average MBE of net radiation was reduced by 7%,22%,and 21%.展开更多
基金supported by the joint funds of the Chinese National Natural Science Foundation(NSFC)(Grant No.U2242213)the funds of the NSFC(Grant No.42341209)+2 种基金the National Key Research and Development(R&D)Program of the Ministry of Science and Technology of China(Grant No.2021YFC3000902)the National Science Foundation for Young Scholars(Grant No.42205166)the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSQ008)。
文摘Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
基金supported by the National Natural Science Foundation of China (Grant No. 91437113)the Special Fund for Meteorological Scientific Research in the Public Interest (Grant Nos. GYHY201506007 and GYHY201006015)+1 种基金the National 973 Program of China (Grant Nos. 2012CB417204 and 2012CB955200)the Scientific Research & Innovation Projects for Academic Degree Students of Ordinary Universities of Jiangsu (Grant No. KYLX 0827)
文摘An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.
基金the National Key BasicResearch and Development Project of China under GrantNo. 2004CB418301the National Natural Sciences Foun-dation of China under Grant No. 40775031"Outstand-ing Oversea Scholars" Project No.2005-2-16.
文摘Impacts of initial conditions on cloud-resolving model simulations are investigated using a series of sensitivity experiments. Five experiments with perturbed initial temperature, moisture, and cloud conditions are conducted and compared to the control experiment. The model is forced by the large-scale vertical velocity and zonal wind observed and derived from NCEP/Global Data Assimilation System （GDAS）. The results indicate that model predictions of rainfall are much more sensitive to the initial conditions than those of temperature and moisture. Further analyses of the surface rainfall equation and the moisture and cloud hydrometeor budgets reveal that the calculations of vapor condensation and deposition rates in the model account for the large sensitivities in rainfall simulations.
文摘The basic objects of investigation in this article are nonlinear impulsive dif- ferential equations. The impulsive moments coincide with the moments of meeting of the integral curve and some of the so-called barrier curves. For such type of equations, suf- ficient conditions are found under which the solutions are continuously dependent on the perturbations with respect to the initial conditions and barrier curves. The results are applied to a mathematical model of population dynamics.
基金financial support from the National Natural Science Foundation of China (Grant Nos. 41575108 and 41475082)
文摘Focusing on the role of initial condition uncertainty,we use WRF initial perturbation ensemble forecasts to investigate the uncertainty in intensity forecasts of Tropical Cyclone(TC)Rammasun(1409),which is the strongest TC to have made landfall in China during the past 50 years.Forecast results indicate that initial condition uncertainty leads to TC forecast uncertainty,particularly for TC intensity.This uncertainty increases with forecast time,with a more rapid and significant increase after 24 h.The predicted TC develops slowly before 24 h,and at this stage the TC in the member forecasting the strongest final TC is not the strongest among all members.However,after 24 h,the TC in this member strengthens much more than that the TC in other members.The variations in convective instability,precipitation,surface upward heat flux,and surface upward water vapor flux show similar characteristics to the variation in TC intensity,and there is a strong correlation between TC intensity and both the surface upward heat flux and the surface upward water vapor flux.The initial condition differences that result in the maximum intensity difference are smaller than the errors in the analysis system.Differences in initial humidity,and to a lesser extent initial temperature differences,at the surface and at lower heights are the key factors leading to differences in the forecasted TC intensity.These differences in initial humidity and temperature relate to both the overall values and distribution of these parameters.
基金Supported by the National Natural Science Foundation of China(41375105)
文摘This study uses ECMWF fifth-generation reanalysis, ERA5, which extends to the mesopause, to construct the Initial Conditions(IC) for WACCM(Whole Atmosphere Community Climate Model) simulations. Because the biases between ERA5 and Sounding of the Atmosphere using Broadband Emission Radiometry(SABER) temperature data are within ±5 K below the lower mesosphere,ERA5 reanalysis is used to construct IC in the lower atmosphere. Four experiments are performed to simulate a Stratospheric Sudden Warming(SSW) event from 5 to 15 February 2016. The simulation using the WACCM default climatic IC cannot represent the sharp meteorological variation during SSW. In contrast, the 0～4 d forecast results driven by ERA5-constructed IC is consistent with ERA5 reanalysis below the middle mesosphere. Comparing with WACCM climatology ICs scheme, the ICs constructing method based on ERA5 reanalysis can obtain 67%, 40%, 22%, 4% and 6% reduction of temperature forecast RMSE at 10 hPa, 1 hPa, 0.1 hPa, 0.01 hPa and 0.001 hPa respectively. However,such improvement is not shown in the lower thermosphere.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060102)the National Natural Science Foundation of China (Grant Nos. 41475101, 41690122, 41690120 and 41421005)the National Programme on Global Change and Air–Sea Interaction Interaction (Grant Nos. GASI-IPOVAI-06 and GASI-IPOVAI-01-01)
文摘Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.
基金supported by the National Natural Science Foundation of China (Grant Nos.42005054,41975070)China Postdoctoral Science Foundation (Grant No.2020M681154)。
文摘Initial condition and model errors both contribute to the loss of atmospheric predictability.However,it remains debatable which type of error has the larger impact on the prediction lead time of specific states.In this study,we perform a theoretical study to investigate the relative effects of initial condition and model errors on local prediction lead time of given states in the Lorenz model.Using the backward nonlinear local Lyapunov exponent method,the prediction lead time,also called local backward predictability limit(LBPL),of given states induced by the two types of errors can be quantitatively estimated.Results show that the structure of the Lorenz attractor leads to a layered distribution of LBPLs of states.On an individual circular orbit,the LBPLs are roughly the same,whereas they are different on different orbits.The spatial distributions of LBPLs show that the relative effects of initial condition and model errors on local backward predictability depend on the locations of given states on the dynamical trajectory and the error magnitudes.When the error magnitude is fixed,the differences between the LBPLs vary with the locations of given states.The larger differences are mainly located on the inner trajectories of regimes.When the error magnitudes are different,the dissimilarities in LBPLs are diverse for the same given state.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 60571066,60271023 and 61072037)the Natural Science Foundation of Guangdong Province,China (Grant No. 07008126)
文摘A novel approach to the inverse problem of diffusively coupled map lattices is systematically investigated by utilizing the symbolic vector dynamics. The relationship between the performance of initial condition estimation and the structural feature of dynamical system is proved theoretically. It is found that any point in a spatiotemporal coupled system is not necessary to converge to its initial value with respect to sufficient backward iteration, which is directly relevant to the coupling strength and local mapping function. When the convergence is met, the error bound in estimating the initial condition is proposed in a noiseless environment, which is determined by the dimension of attractors and metric entropy of the system. Simulation results further confirm the theoretic analysis, and prove that the presented method provides the important theory and experimental results for better analysing and characterizing the spatiotemporal complex behaviours in an actual system.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10705041,10975032,11047112,and 11175042)the Program for New Century Excellent Talents at the University of Ministry of Education of China (Grant No. NCET-09-0276)the National Ministry of Education of China(Grant Nos. N100505001 and N110405011)
文摘The initial condition Ωde（zini）=n^2（1＋zini）^-2/4 at zini = 2000,widely used to solve the differential equation of the density of the new agegraphic dark energy（NADE） Ωde,makes the NADE model a single-parameter dark-energy cosmological model.However,we find that this initial condition is only applicable in a flat universe with only dark energy and pressureless matter.In fact,in order to obtain more information from current observational data,such as the cosmic microwave background（CMB） and the baryon acoustic oscillations（BAO）,we need to consider the contribution of radiation.For this situation,the initial condition mentioned above becomes invalid.To overcome this shortcoming,we investigate the evolutions of dark energy in matter-dominated and radiation-dominated epochs,and obtain a new initial condition de（zini）=n2（1＋zini）-2（1＋F（zini））2/4 at z ini = 2000,where F（z）≡Ωr0（1＋z）/[Ωm0＋Ωr0（1＋z）] with Ωr0 and Ωm0 being the current density parameters of radiation and pressureless matter,respectively.This revised initial condition is applicable for the differential equation of Ωde obtained in the standard Friedmann-Robertson-Walker（FRW） universe with dark energy,pressureless matter,radiation,and even spatial curvature,and can still keep the NADE model as a single-parameter model.With the revised initial condition and the observational data of type Ia supernova（SNIa）,CMB,and BAO,we finally constrain the NADE model.The results show that the single free parameter n of the NADE model can be constrained tightly.
基金funded by National Natural Science Foundation of China(No.11471215)。
文摘In this paper,we study the high-order nonlinear Schrodinger equation with periodic initial conditions via the unified transform method extended by Fokas and Lenells.For the high-order nonlinear Schrodinger equation,the initial value problem on the circle can be expressed in terms of the solution of a Riemann–Hilbert problem.The related jump matrix can be explicitly expressed based on the initial data alone.Furthermore,we present the explicit solution,which corresponds to a one-gap solution.
文摘System identification is a method for using measured data to create or improve a mathematical model of the object being tested. From the measured data however, noise is noticed at the beginning of the response. One solution to avoid this noise problem is to skip the noisy data and then use the initial conditions as active parameters, to be found by using the system identification process. This paper describes the development of the equations for setting up the initial conditions as active parameters. The simulated data and response data from actual shear buildings were used to prove the accuracy of both the algorithm and the computer program, which include the initial conditions as active parameters. The numerical and experimental model analysis showed that the value of mass, stiffness and frequency were very reasonable and that the computed acceleration and measured acceleration matched very well.
文摘Symmetrical passive running of a simple spring model is commonly used to predict the locomotion of real animals and develop the design strategies for legged systems. If the initial conditions for running can be found automatically, we can expand the ranges of the model parameters freely. To achieve this objective, we derive an efcient searching procedure based on analysis of the symmetries. Using this process, the desired initial states of the passive running are obtained. Existence of the results reveals that the leg symmetries can be used to generate the symmetrical running. Moreover, the leg forces are associated with the running pattern, implying that we should choose suitable motion for the given model characteristics.
基金supported by the National Natural Science Foundation of China(Grant Nos 60271023 and 60571066)the Natural Science Foundation of Guangdong Province,China(Grant Nos 5008317 and 7118382)
文摘A novel computationally efficient algorithm in terms of the time-varying symbolic dynamic method is proposed to estimate the unknown initial conditions of coupled map lattices （CMLs）. The presented method combines symbolic dynamics with time-varying control parameters to develop a time-varying scheme for estimating the initial condition of multi-dimensional spatiotemporal chaotic signals. The performances of the presented time-varying estimator in both noiseless and noisy environments are analysed and compared with the common time-invariant estimator. Simulations are carried out and the obtained results show that the proposed method provides an efficient estimation of the initial condition of each lattice in the coupled system. The algorithm cannot yield an asymptotically unbiased estimation due to the effect of the coupling term, but the estimation with the time-varying algorithm is closer to the Cramer-Rao lower bound （CRLB） than that with the time-invariant estimation method, especially at high signal-to-noise ratios （SNRs）.
基金The NSF(11271154 and 11326103)of ChinaResearch Project(2014164)of the Education of Jilin Provincethe Youth Studies Program(XJ2012006)of Jilin University of Finance and Economics
文摘In this paper, a class of one-dimension p-Laplacian equation with nonlocal initial value is studied. The existence of solutions to such a problem is obtained by using the topological degree method.
文摘The existence,uniqueness,and continuous dependence to the mild solutions of the nonlocal Cauchy problem were proved for a class of semilinear fractional neutral differential equations.The results are obtained by using the Krasnoselskii's fixed point theorem and the theory of resolvent operators for integral equations.
基金Supported by the National Natural Science Foundation of China under Grant No 71301012
文摘A chimera state consisting of both coherent and incoherent groups is a fascinating spatial pattern in non-locally coupled identical oscillators. It is thought that random initial conditions hardly evolve to chimera states. In this work, we study the dependence of chimera states on initial conditions. We show that random initial conditions may lead to chimera states and the chance of realizing chimera states becomes increasing when the model parameters axe moving away from the boundary of their stable regime.
基金supported by the Korea Meteorological Administration Research and Development Program “Developing Application Technology for Atmospheric Research Aircraft” (Grant No. KMA2018-00222)
文摘This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.
基金the National Natural Science Foundation of China(Grant No.U20A2081)West Light Foundation of the Chinese Academy of Sciences(Grant No.xbzg-zdsys-202102)the Second Tibetan Plateau Scientific Expedition and Research(STEP)Project(Grant No.2019QZKK0105).
文摘Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling.This study emphasized the influence of the initial soil temperature(ST)and soil moisture(SM)conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau(TP)using the Community Land Model version 5.0(CLM5.0).The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic,and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site.Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes.The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost,which coexists with soil liquid water(SLW),and soil ice(SI)when the ST is below freezing temperature,effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes.Consequently,the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method.Three modified initial soil schemes experiments resulted in a 64%,88%,and 77%reduction in the average mean bias error(MBE)of ST,and a 13%,21%,and 19%reduction in the average root-mean-square error(RMSE)of SLW compared to the default simulation results.Also,the average MBE of net radiation was reduced by 7%,22%,and 21%.
