Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

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.

Impact of Initial Soil Conditions on Soil Hydrothermal and Surface Energy Fluxes in the Permafrost Region of the Tibetan Plateau

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%.

Initial Labor Market Conditions and Individual Employability

Initial labor market conditions affect how individuals build their human capital and look for jobs and thus can have long-term effects on their income levels,work performance,and career development.Based on data from the Urban Household Survey(UHS)of urban households in China from 1986 to 2009,we perform an empirical test of how initial labor market conditions affect the employability of individuals.Our research shows that people’s future incomes suffer if they start out in an adverse job market.Each percentage point of increase in the unemployment rate at an individual’s entry into the labor market is associated with a two-percentage-point drop in his or her average annual income.Even after looking at different parts of the job market and sample groups,this conclusion still holds.In the context of global economic instability,our findings may assist government policymakers in addressing adverse labor market conditions.

Initial and Stopping Condition in Possibility Principal Factor Rotation

Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian inference, the flow of subsequent research was to make the mapping formula multidimensional, to introduce the concept of time, and to derive a Markov (decision) process. Furthermore, we formulated stochastic differential equations to derive them [2]. This paper refers to type 2 vague events based on a second-order mapping equation. This quadratic mapping formula gives a certain rotation named as possibility principal factor rotation by transforming a non-mapping function by a relation between two mapping functions. In addition, the derivation of the Type 2 Complex Markov process and the initial and stopping conditions in this rotation are mentioned. .

Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System

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.

Impacts of Initial Conditions on Cloud-Resolving Model Simulations

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.

CONTINUOUS DEPENDENCE OF THE SOLUTIONS OF IMPULSIVE DIFFERENTIAL EQUATIONS ON THE INITIAL CONDITIONS AND BARRIER CURVES

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.

A STUDY ON THE PREDICT ABILITY OF GRAPES MODEL OVER SOUTH CHINA: COMPARISONS BY TWO INITIALIZATION CONDITIONS BETWEEN ECMWF AND NCEP

This paper aims to assess the performances of different model initialization conditions(ICs)and lateral boundary conditions between two global models(GMs),i.e.,the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP),on the accuracy of the Global/Regional Assimilation and Prediction System(GRAPES)forecasts for south China.A total of 3-month simulations during the rainy season were examined and a specific case of torrential rain over Guangdong Province was verified.Both ICs exhibited cold biases over south China,as well as a strong dry bias over the Pearl River Delta(PRD).In particular,the ICs from the ECMWF had a stronger cold bias over the PRD region and a more detailed structure than NCEP.In general,the NCEP provided a realistic surface temperature compared to the ECMWF over south China.Moreover,GRAPES initialized by the NCEP had better simulations of both location and intensity of precipitation than by the ECWMF.The results presented in this paper could be used as a general guideline to the operational numerical weather prediction that uses regional models driven by the GMs.

Numerical simulation of an extreme haze pollution event over the North China Plain based on initial and boundary condition ensembles

The North China Plain often su ers heavy haze pollution events in the cold season due to the rapid industrial development and urbanization in recent decades.In the winter of 2015,the megacity cluster of Beijing Tianjin Hebei experienced a seven-day extreme haze pollution episode with peak PM2.5(particulate matter(PM)with an aerodynamic diameter≤2.5μm)concentration of 727μg m 3.Considering the in uence of meteorological conditions on pollu-tant evolution,the e ects of varying initial conditions and lateral boundary conditions(LBCs)of the WRF-Chem model on PM2.5 concentration variation were investigated through ensemble methods.A control run(CTRL)and three groups of ensemble experiments(INDE,BDDE,INBDDE)were carried out based on difierent initial conditions and LBCs derived from ERA5 reanalysis data and its 10 ensemble members.The CTRL run reproduced the meteorological conditions and the overall life cycle of the haze event reasonably well,but failed to capture the intense oscillation of the instantaneous PM2.5 concentration.However,the ensemble forecasting showed a considerable advantage to some extent.Compared with the CTRL run,the root-mean-square error(RMSE)of PM2.5 concentration decreased by 4.33%,6.91%,and 8.44%in INDE,BDDE and INBDDE,respectively,and the RMSE decreases of wind direction(5.19%,8.89%and 9.61%)were the dominant reason for the improvement of PM2.5 concentration in the three ensemble experiments.Based on this case,the ensemble scheme seems an e ective method to improve the prediction skill of wind direction and PM2.5 concentration by using the WRF-Chem model.

Uncertainty in Tropical Cyclone Intensity Predictions due to Uncertainty in Initial Conditions

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.

Application of ERA5 Reanalysis to the Construction of Initial Conditions for WACCM Simulations

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.

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas： A Target Observation Perspective

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.

EXPERIMENTAL STUDY OF THE ROLE OF INITIAL AND BOUNDARY CONDITIONS IN MESOSCALE NUMERICAL WEATHER PREDICTION

Based on the real case of a frontal precipitation process affecting South China, 27 controlled numerical experiments was made for the effects of hydrostatic and non-hydrostatic effects, different driving models, combinations of initial/boundary conditions, updates of lateral values and initial time levels of forecast, on model predictions. Features about the impact of initial/boundary conditions on mesoscale numerical weather prediction (NWP) model are analyzed and discussed in detail. Some theoretically and practically valuable conclusions are drawn. It is found that the overall tendency of mesoscale NWP models is governed by its driving model, with the initial conditions showing remarkable impacts on mesoscale models for the first I0 hours of the predictions while leaving lateral boundary conditions to take care the period beyond; the latter affect the inner area of mesoscale predictions mainly through the propagation and movement of weather signals (waves) of different time scales; initial values of external model parameters such as soil moisture content may affect predictions of more longer time validity, while fast signals may be filtered away and only information with time scale 4 times as large as or more than the updated period of boundary values may be introduced, through lateral boundary, to mesoseale models, etc. Some results may be taken as important guidance on mesoseale model and its data a.ssimilation developments of the future.

Effect of Initial Stress Conditions on the Threshold Shear Strain of Nanjing's Saturated Fine Sand

By using GDS dynamic hollow cylinder torsional apparatus, a series of cyclic torsional triaxial tests under complex initial consolidation condition are performed on Nanjing saturated fine sand. The effects of the initial principal stress direction αo, the initial ratio of deviatoric stress η0, the initial average effective principal stress Po and the initial intermediate principal stress parameter b0 on the threshold shear strain γt of Nanjing saturated fine sand are then systematically investigated. The results show that γt increases as η0,p0 and b0 increase respectively, while the other three parameters remain constant. ao has a great influence on γt, which is reduced when ao increases from 0° to 45°and increased when α0 increases from 45° to 90°. The effect of α0 on γt, plays a leading role and the effect of η0 will weaken when ao is approximately 45°.

Application of Backward Nonlinear Local Lyapunov Exponent Method to Assessing the Relative Impacts of Initial Condition and Model Errors on Local Backward Predictability

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.

Analysis of convergence for initial condition estimation of coupled map lattices based on symbolic dynamics

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.

New initial condition of the new agegraphic dark energy model

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.

Time domain system identification of unknown initial conditions

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.

A method of estimating initial conditions of coupled map lattices based on time-varying symbolic dynamics

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）.

Existence and Continuous Dependence of Mild Solutions for Some Fractional Neutral Differential Equations with Nonlocal Initial Conditions

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.