Fourier Series Model Research Articles

Probabilistic Multiple Pedestrian Walking Force Model including Pedestrian Inter‐ and Intrasubject Variabilities

A probabilistic walking load model that accounts for inter‐ and intrasubject variabilities has been developed to generate synthetic vertical load waveforms induced by pedestrians. The mathematical model is based on a comprehensive database of continuously recorded pedestrian walking forces on an instrumented treadmill, having a wide range of walking frequencies. The proposed model is able to replicate temporal and spectral features of real walking forces, which is a significant advantage over conventional Fourier series models. The load model results in more realistic force time histories than previous models, since it incorporates significant components of the spectra that are omitted in Fourier series approaches. The proposed mathematical model can be implemented in vibration serviceability assessment of civil engineering structures, such as building floors and footbridges, to estimate more realistically dynamic structural responses due to people walking.

Serviceability analysis of a cable-supported footbridge subjected to human-induced loads

Purpose The purpose of this paper is to obtain the response time history curves of vertical and lateral acceleration in the span of the main beam under different loads through the finite element time-history analysis method, so as to realize the Serviceability Analysis of a Cable-Supported Footbridge Subjected to Human-Induced Loads, taking the long-span cable-supported footbridge over Dongtan River as an example. Design/methodology/approach The finite element method is used for analysis of the footbridge. Findings It is found that under the condition of low-density pedestrians walking freely, the response of human vertical vibration acceleration and the load conditions of pedestrian overpasses cannot meet the requirements of normal use. Therefore, the vertical acceleration of the footbridge should be designed to reduce vibration. Under these two loading conditions, the lateral acceleration response meets the requirements of normal use. Originality/value On the basis of summarizing the research at home and abroad, the analysis of human-induced vibration is mainly considered from two aspects: frequency regulation and dynamic response control. The walking load models mainly include Fourier series model, self-excitation model, impulse model, stochastic model and more; the crowd load models are divided into groups: low-density crowd walking freely, high-density crowd flowing and more. Therefore, it is very important to calculate the structural vibration response in the design of long-span cable-supported footbridges under pedestrian excitation to meet comfort requirements.

Predicting the antimicrobial effect of probiotic lactic acid bacteria against Staphylococcus aureus in white cheeses, using Fourier series modeling method

AbstractIn this study, it was aimed to design the time‐predicted inactivation model for various growth curves and the growth parameters representing the changes in the inactivation of Staphylococcus aureus in probiotic white cheese. For this purpose, S. aureus were inoculated in the pasteurized milk and the production of probiotic white cheese was performed in five different groups, and then samples were matured for 60 days at 4°C. S. aureus, Lactobacillus rhamnosus, Lactobacillus casei Shirota, and mesophilic starter culture levels of the samples were analyzed on the 1st, 4th, 7th, 10th, 15th, 30th, and 60th days of maturation. The inactivation model evaluation was performed by using the number of microorganisms counted with Fourier modeling method. In determining the model to be applied, the coefficient of determination (R2) and the sum of squared errors (SSE) for the mean number of microorganisms detected via modeling method were used. As conclusions, the number of S. aureus has decreased by approximately 5 logarithmic units at the end of the storage period by using probiotics together with the starter culture. It was determined that the Fourier modeling method is a suitable inactivation model with R2 values of 0.9847–0.9986 and SSE values of 0.094–0.510.

Indonesia’s Inflation Analysis Using Hybrid Fourier - Wavelet Multiscale Autoregressive Method

Regression analysis is a statistical method to determine the relationship between predictor variables and response variables. Regression approach can be done with parametric and nonparametric approaches. The parametric approach is rigorous with assumptions and must fulfillthe assumptions to get a good model.Meanwhile, the nonparametric approach is not rigorous with assumptions because the method is based on an unknown curve shape approach. Nonparametric regression can be done with several approaches including Fourier and Wavelet methods. The Fourier method is a method based on cosine and sine series. It is very suitable for data that has repetitive or periodic patterns. The Fourier series modeling is less efficient because it requires many coefficients to obtain a good model and it is less able to handle data with sharp jumps. Recently, there has been a combination of two methods called hybrid methods that give better results. In this paper, Indonesia’s inflation data modelling is performed by using hybrid-wavelet. First, the data is modeled using Fourier with small K; then the error Fourier model is modeled by using multiscale waveletautoregressive. In this study, the value of Inflation in Indonesia was modeled from January 2007 to August 2017. The response variable was the inflation value, while the predictor variable was time. The Fourier method with K = 100 generated MSE of 0.846216 and R2 of 80.12%. The Fourier-Wavelet hybrid model with K = 1 generated MSE of 0.31 and R2 of 95%. So that in inflation modeling in Indonesia, the Fourier-Wavelet hybrid regression model generated a better model than the wavelet model with fewer parameters than the Fourier method.

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

To support alternative forms of energy resources, the prediction of global incident solar radiation (Irad) is critical to establish the efficacy of solar energy resources as a free and clean energy, and to identify and screen solar powered sites. Solar radiation data for construction of energy feasibility studies are not available in many locations due to the absence of meteorological stations, especially in remote or regional sites. To surmount the challenge in solar energy site identification, the universally gridded data integrated into predictive models used to generate reliable Irad forecasts can be considered as a viable medium for future energy utilization. The objective of this paper is to review, develop and evaluate a suite of machine learning (ML) models based on the artificial neural network (ANN) versus several other kinds of data-driven models such as support vector regression (SVR), Gaussian process machine learning (GPML) and genetic programming (GP) models for the prediction of daily Irad generated through the European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis fields. The performance of the ML models are benchmarked against several statistical tools: auto regressive moving integrated average (ARIMA), Temperature Model (TM), Time series and Fourier Series (TSFS) models. To train these models, 87 different predictor variables from the ERA-Interim reanalysis dataset (01-January-1979 to 31-December-2015) were extracted for 5 solar-rich metropolitan sites (i.e., Brisbane, Gold Coast, Sunshine Coast, Ipswich and Toowoomba, Australia) targeted against surface level Irad available from the measured Scientific Information for Land Owners dataset. For daily forecast models, a total of the 20 most important predictors related to the Irad dataset were screened with nearest component analysis: “fsrnca” feature selection, and partitioned into training (70%), validation (15%) and testing (15%) sets for model design. To benchmark the ANN, TSFS and TM models were developed with Fourier series and regression analysis, respectively and the statistical performance was benchmarked with root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), Mean Bias error (MBE), Legates and McCabe Index (E1), and relative MAE, RMSE and diagnostic plots. The performance of ANN was significantly better than the other models (SVR, GPML, GP, TM), resulting in lower RMSE (1.715–2.27 MJm−2/day relative to 2.14–5.90 MJm−2/day), relative RMSE (9.07–12.47 vs 10.98–29.15), relative RMAE (7.97–11.74 vs 9.27–33.96) and larger WI, ENS and E1 (0.938–0.967 vs. 0.462–0.955, 0.935–0.872 vs. 0.355–0.915, 0.672–0.783 vs. 0.252–0.740). Additionally, models assessed with predictors grouped into El Niño, La Niña and the positive, negative and neutral periods of Indian Ocean Dipole, affirmed the merits of ANN model (RRMSE ≤ 11%). Seasonal analysis showed that ANN was an elite tool over SVR, GPML and GP for Irad prediction. The study concludes that an ANN approach integrated with ECMWF fields, incorporating physical interactions of Irad with atmospheric data, is an efficacious alternative to forecast solar energy and assist with energy modelling for solar-rich sites that have diverse climatic conditions to further support clean energy utilization.

Outliers Identification From Instantaneous Angular Speed Signal Generated by Zebra Tapes

Instantaneous angular speed (IAS) measurement using zebra tape has been presented as an advantageous tool for rotating machinery surveillance. Unfortunately, both butt joint and mounting imperfection of the zebra tape result in stripes with different widths, which cause the outliers appear in the acquired signal. This paper proposed a solution to accurately identify the outliers and define the angular displacement values represented by outliers in the condition of periodic fluctuation of speed. The procedure is based on K-means clustering method and Fourier series model. Specially, a series of algorithms take K-means clustering partitioning method as core, and is used for outliers identification; the Fourier series fitting model is used to analyze the angular displacement values represented by outliers. Experimental study was performed on a test rig to validate the method under different measurement conditions. The result of error analysis shows the effectiveness of the algorithm.

Bearing Capacity Model of Corroded RC Eccentric Compression Columns Based on Hermite Interpolation and Fourier Fitting

With an extension in service years, bridges inevitably suffer from performance deterioration. Columns are the main components of bridge structures, which support the superstructure. The damage of pier columns is often more harmful to bridges than that of other components. To accurately evaluate the time-varying characteristics of corroded columns, this paper proposes a new model for the bearing capacity evaluation of deteriorated reinforced concrete (RC) eccentric compression columns based on the Hermite interpolation and Fourier function. Firstly, the axial compression point, the pure bending point and the balanced failure point were selected as the basic points, and the deteriorated strength of these basic points was calculated by considering factors such as concrete cracking, reduction of reinforcement area, buckling of the steel bar, bond slip and strength reduction of confined concrete. After that, the interpolation points were generated by a piecewise cubic Hermite interpolating polynomial, and the explicit expression of the interpolation points fitting function was realized by the trigonometric Fourier series model. Finally, comparison studies based on measured data from forty-five corroded RC eccentric compression columns were conducted to investigate the accuracy and efficiency of the proposed method. The results show that: (1) the prediction results for bearing capacity of corroded RC columns are in good agreement with the measured data, with the average ratio of predicted results to test results at 1.06 and the standard deviation at 0.14; (2) the proposed model unifies the three stress states of axial compression, eccentric compression and pure bending, and is consistent with the continuum mechanics characteristics; (3) the decrements of axial load carrying capacity for 10% and 50% of the corrosion rate are 31.4% and 45.2%, while in flexure they are 25.4% and 77.4%, respectively; and (4) the test data of small-scale specimens may overestimate the negative effect of corrosion on the bearing capacity of actual structures. The findings in this paper could lay a solid starting point for structural life prediction technologies based on nondestructive testing.

Hourly ambient air humidity fluctuation evaluation and forecasting based on the least-squares Fourier-model

Air humidity is one important measurement parameter involved in a wide range of fields of science and engineering. This study addresses Fourier-series-based evaluation and prediction of hourly ambient air humidity fluctuation. It is shown that hourly ambient humidity fluctuation displays a significant 24-hour cyclical variation. Then on the basis of daily periodic extension for hourly ambient air humidity fluctuation, a finite Fourier-series-based evaluation model is introduced for describing its hourly fluctuation. The Fourier-based analysis in its conventional form, however, cannot be straightly applicable to prediction. Then in combination with the least-squares method for coefficient-seeking based on finite observations of hourly air humidity, the extended version of the Fourier-series model in the least-squares is proposed for forecasting. The proposed Fourier-series-based method is applied to experiments of evaluating and forecasting hourly air humidity fluctuations at different monitoring stations with satisfying results. The experimental results, further mathematical analysis of the conjugate symmetry property and spectrum characteristic analysis for the Fourier-series involved in the Fourier-based model indicate that daily 24-hour air humidity fluctuation is well described by about 12-term harmonics and the extended Fourier-series forecasting model predicts the hourly humidity fluctuation best fitting with less than 6-term harmonics.

The least-square Fourier-series model-based evaluation and forecasting of monthly average water-levels

Water-level fluctuation evaluation and forecasting for a river is increasingly important because of its close ties to human living and production. In this study, with annual periodic extension for monthly average water-level fluctuation, Fourier analysis employing finite Fourier series is presented for evaluating its fluctuation. Fourier analysis in the conventional form which is the most common analysis method in the frequency domain, however, cannot be straightly applied for forecasting. Then, the extended version of the Fourier-series model in the least-square sense is proposed for forecasting. The extended forecasting model obtains its optimum Fourier coefficients in the least-squares based on previous monthly water-level observations. Experiments at the different monitoring stations of the Yangtze River in China indicate potentiality of the proposed method. It is shown that the fluctuation of monthly average water-level is well described by about six-term harmonics. And the extended Fourier forecasting model predicts the fluctuation of monthly water level most fitting about three-term harmonics.

Beating-heart registration for organ-mounted robots.

Organ-mounted robots address the problem of beating-heart surgery by adhering to the heart, passively providing a platform that approaches zero relative motion. Because of the quasi-periodic deformation of the heart due to heartbeat and respiration, registration must address not only spatial registration but also temporal registration. Motion data were collected in the porcine model in vivo (N = 6). Fourier series models of heart motion were developed. By comparing registrations generated using an iterative closest-point approach at different phases of respiration, the phase corresponding to minimum registration distance is identified. The spatiotemporal registration technique presented here reduces registration error by an average of 4.2mm over the 6 trials, in comparison with a more simplistic static registration that merely averages out the physiological motion. An empirical metric for spatiotemporal registration of organ-mounted robots is defined and demonstrated using data from animal models in vivo.

Power Spectral Density Function for Individual Jumping Load

Modern slender structures such as long-span floors and cantilever stands are sensitive to jumping-induced vibrations. A conventional deterministic Fourier series model for the human jumping load may overestimate a structure's responses in resonance condition. This paper suggests a power spectral density (PSD) function for the individual jumping load, which was treated as a narrowband stationary stochastic process. Experiments were conducted on individual jumping loads resulting in 334 records from 73 subjects. Statistical analysis of the records led to experimental PSD curves on which a symmetrical bilinear function was suggested. The proposed PSD function is centered on the given jumping frequency and its integer multiples. The function's parameters were determined by equating the total energy of the proposed PSD with that of the experimental PSD. Application of the proposed PSD for predicting a floor's peak response via the stochastic vibration theory was then presented. The predictions for an experimental floor model subjected to individual jumping were compared with the measured peak responses. The comparisons demonstrated that the proposed PSD function was applicable for predicting the floor's response to individual jumping. Finally, the framework for calculating crowd-induced structure vibrations using the suggested PSD was also discussed.

Fourier Transformation on Model Fitting for Pakistan Inflation Rate

Inflation is one of the serious economic indicators in Pakistan. Inflation can be crawling, walking, running, hyper and stagflation according to nature. To model monthly inflation rate in Pakistan periodogram analysis and frequency domain analysis which is also known as Fourier analysis or spectral analysis is used. After analyzing the data, inflation cycle length is observed and appropriate Fourier series models are fitted to the data. Monthly inflation rate is also analyzed by Auto Regressive Integrated Moving average (ARIMA). Further, models are compared and it is found that Fourier series models are more suitable to forecast inflation rate of Pakistan.

Solution box: SOLBOX-09, SOLBOX-10

Periodicity is a major property of many structures in electromagnetic applications, such as antenna arrays [1], frequency-selective surfaces, metamaterials [2], and photonic crystals [3]. Array elements (unit cells, antennas, etc.) are periodically arranged in order to enhance the response of a single element or to gain new capabilities due to interactions between them. Naturally, electromagnetic simulations of periodic structures have a long history in the literature. This is still an active area as the structures become more complicated and require more sophisticated (accurate, efficient, stable) tools for precise analysis. When the structure is infinitely large, and particularly when the periodicity is sub-wavelength, there are excellent tools based on Fourier series and finite-diff erence models, e.g., see [4] and [5]. Similarly, when the elements can be represented pointwise with or without interactions, there are analytical (e.g., using array factor) and semi-analytical (e.g., using lumped elements and/or transmission-line models) techniques. On the other hand, challenges arise when each element is a complex electromagnetic problem itself while it needs to interact with other complex elements. In addition, if the finiteness is an important geometric parameter of a periodic structure, its full-wave models may lead to large-scale problems that can be difficult to solve numerically. In such cases, it is tempting to substitute high-order mathematical models for the elements [6], specifically by isolating their inner dynamics to derive the required equivalence [7] within the decomposed models. A well-known method in this path is called the Equivalence-Principle Algorithm (EPA), which has been used and improved (e.g., hybridized with or used in other methods) by numerous researchers [7–11].

A methodology to quantify the long-term changes in social networks of competing species

Current methods of social network analysis need detailed data, which are often unavailable for long periods of time (e.g. decades). The main objective of the present work is to develop and test a methodology that requires less detailed data and relies on user-defined mathematical models. This methodology is a three-step procedure described as follows. First, long-term population data is divided into n subsets based on the desired time unit (e.g. months, or years). Second, a class of mathematical models are defined and fitted to each subset of data, where growth, migration, and interaction parameters are estimated. Third, time series of estimated parameter values are analyzed to detect trends, periodic cycles and patterns of ecological dominance. To examine the robustness of this method, the annual competitive Lotka–Volterra (CLV) models were parametrized using the long-term (1973–2003) population densities of five rodent species residing in Kansas: cotton rat (CR), prairie vole (PV), white-footed mouse (WFM), deer mouse (DM), and western harvest mouse (WHM). Using the Fourier series models fitted to the estimated parameter values, we found that the periodic cycles of growth and competition rates were about 5.5 and 11 years for CR and about 2.6–3.8 years in all rodent species. The linear regression models fitted to the estimated values indicate slight decreases in the growth rates of all species except PV. The competing effects of PV on CR and WFM have decreased more than 11.4%. Whereas the competing effect of the DM on PV, and WFM on DM and itself have increased by 15.4%, 25.8% and 14.9%, respectively. The estimated migration rates of DM and WHM were mainly negative, which indicates that these species were mainly migrating out of the study site. All other three species had mainly positive migration rates. The parametrized annual CLV models were employed to analyze the formation of stable equilibria and to numerically investigate spatio-temporal patterns of population densities. We found that CR and PV were often dominant. The present work is the first step towards employing user-defined models compensating for the lack of long-term detailed social network data.

A practical approach to the dynamic modelling of an underwater vehicle propeller in all four quadrants of operation

This article presents the open water propeller characteristics and the four-quadrant propeller models as applied to a torpedo-shaped underwater vehicle. A series of experiments with a Gavia autonomous underwater vehicle propeller were conducted in the towing tank using a rotor testing apparatus. The purpose of these tests was to measure the propeller thrust and torque under varying flow conditions and then to be used as the basis of the developed propeller models. These mathematical models were constructed using two regression models, a polynomial and a Fourier series. Model coefficients were derived using the method of least squares and a comparison analysis was also conducted to test the robustness of the methodology. Results show that the Fourier series models were able to produce a reasonable and accurate approximation of thrust and torque coefficients with a small number of parameters in the examined condition of this study. The obtained four-quadrant open water characteristics of the autonomous underwater vehicle propeller model would be utilised to improve the system mathematical model for more accurate simulation and controller design, to compare the autonomous underwater vehicle performance equipped with different propulsion units, and to validate computational fluid dynamic results.

Modeling of diurnal pattern of air temperature in a tropical environment: Ile-Ife and Ibadan, Nigeria

Modeling of diurnal pattern of air temperature (Ta) is essential in the parameterization of turbulent heat fluxes in climate models. However, climate models still simulate the nocturnal stable boundary layer surface Ta with biases across the globe. This paper therefore validated five different diurnal Ta models (Ephrath, Hirotaka, de Wit, Parton and Fourier series models) used for estimating hourly Ta from daily maximum, daily minimum, and daily mean air temperature. It also developed an improved fourier series parameterization approach for Ta using surface layer observations from Nigerian micrometeorological experimental (NIMEX) site. The overall performance of the Ephrath, Hirotaka, de Wit and Parton models revealed a large deviation from the measured data during the early hours of the morning and late hours of the nighttime. The original fourier series model showed better performance for unstable air temperature parameterizations while the stable Ta was strongly overestimated. The performance of the model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion which occurs during the nocturnal boundary layer condition. The mean bias error and root mean square error in estimated Ta by the modified fourier series model reduced by 4.13 and 3.01 °C, respectively during the transition period from dry to wet stable atmospheric conditions. The existing models simulated night time Ta with high biases and could not accurately capture the strong cooling inversion associated with a humid tropical region. The modified Fourier series model that incorporated the night time inversion variables gave the best estimation of the diurnal weather patterns of Ta when compared with other existing models for a tropical environment.

A curve fitting methodology to determine impact location, timing, and instantaneous post-impact ball velocity in cricket batting

This study aimed to develop a methodology for accurate determination of the impact location of a cricket ball on the bat face, as well as the identification of bat–ball contact timing and post-impact instantaneous ball velocity in a whole-body kinematic data collection environment. Three-dimensional kinematic data of bat and ball were recorded during 14 batting strokes: 8 hitting a static ball and 6 against a bowling machine. Curves were fitted separately to the pre- and post-impact phases of the ball position data against time in three axes according to logarithmic equations determined from mechanical principles. Separate Fourier series models were similarly fitted to the four corners of the bat face against time during the downswing prior to ball impact. Time of impact for the dynamic ball trials was determined based on the intersection of pre- and post-impact curves, with impact location calculated from ball and bat face curves at this time. R2 values for the goodness of fit of the ball and bat curves averaged 0.99 ± 0.04 and 1.00 ± 0.00 with root mean square errors of 7.5 ± 2.6 and 0.8 ± 0.2 mm, respectively. Calculated impact locations were assessed against measured impact locations derived from the impression imparted to a fine powder coating on the bat face, finding absolute differences of 6.4 ± 4.2 and 7.1 ± 4.4 mm in the transverse and longitudinal axes of the bat, respectively. Thus, an automated curve fitting methodology enables the accurate determination of cricket bat–ball impact characteristics for use in experimental investigations.

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Extension of Satellite Altimetry Jason-2 Sea Level Anomalies Towards the Red Sea Coast Using Polynomial Harmonic Techniques

ABSTRACTSatellite altimetry data are facing big challenges near the coasts. These challenges arise due to the fundamental difficulties of correction and land contamination in the foot print, which result in rejection of these data near the coast. Several studies have been carried out to extend these data towards the coast. Over the Red Sea, altimetry data consist of gaps, which extend to about 30–50 km from the coast. Two methods are used for processing and extending Jason-2 satellite altimetry sea level anomalies (SLAs) towards the Red Sea coast; Fourier Series Model (FSM), and the polynomial sum of sine model (SSM). FSM model technique uses Fourier series and statistical analysis reflects strong relationship with both the observation and AVISO data, with strong and positive correlation. The second prediction technique, SSM model, depends on the polynomial sum of sine, and does not reflect any relationship with the observations and AVISO data close to the coast and the correlation coefficient (CC) is weak and negative. The FSM model output results in SLA data significantly better and more accurate than the SSM model output.

Global 7 km mesh nonhydrostatic Model Intercomparison Project for improving TYphoon forecast (TYMIP-G7): experimental design and preliminary results

Abstract. Recent advances in high-performance computers facilitate operational numerical weather prediction by global hydrostatic atmospheric models with horizontal resolutions of ∼ 10 km. Given further advances in such computers and the fact that the hydrostatic balance approximation becomes invalid for spatial scales < 10 km, the development of global nonhydrostatic models with high accuracy is urgently required. The Global 7 km mesh nonhydrostatic Model Intercomparison Project for improving TYphoon forecast (TYMIP-G7) is designed to understand and statistically quantify the advantages of high-resolution nonhydrostatic global atmospheric models to improve tropical cyclone (TC) prediction. A total of 137 sets of 5-day simulations using three next-generation nonhydrostatic global models with horizontal resolutions of 7 km and a conventional hydrostatic global model with a horizontal resolution of 20 km were run on the Earth Simulator. The three 7 km mesh nonhydrostatic models are the nonhydrostatic global spectral atmospheric Double Fourier Series Model (DFSM), the Multi-Scale Simulator for the Geoenvironment (MSSG) and the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The 20 km mesh hydrostatic model is the operational Global Spectral Model (GSM) of the Japan Meteorological Agency. Compared with the 20 km mesh GSM, the 7 km mesh models reduce systematic errors in the TC track, intensity and wind radii predictions. The benefits of the multi-model ensemble method were confirmed for the 7 km mesh nonhydrostatic global models. While the three 7 km mesh models reproduce the typical axisymmetric mean inner-core structure, including the primary and secondary circulations, the simulated TC structures and their intensities in each case are very different for each model. In addition, the simulated track is not consistently better than that of the 20 km mesh GSM. These results suggest that the development of more sophisticated initialization techniques and model physics is needed to further improve the TC prediction.