Step-by-Step Numerical Prediction of Aerodynamic Noise Generated by High Speed Trains

In this paper,the unsteady flow around a high-speed train is numerically simulated by detached eddy simulation method(DES),and the far-field noise is predicted using the Ffowcs Williams-Hawkings(FW-H)acoustic model.The reliability of the numerical calculation is verified by wind tunnel experiments.The superposition relationship between the far-field radiated noise of the local aerodynamic noise sources of the high-speed train and the whole noise source is analyzed.Since the aerodynamic noise of high-speed trains is derived from its different components,a stepwise calculation method is proposed to predict the aerodynamic noise of high-speed trains.The results show that the local noise sources of high-speed trains and the whole noise source conform to the principle of sound source energy superposition.Using the head,middle and tail cars of the high-speed train as noise sources,different numerical models are established to obtain the far-field radiated noise of each aerodynamic noise source.The far-field total noise of high-speed trains is predicted using sound source superposition.A step-by-step calculation of each local aerodynamic noise source is used to obtain the superimposed value of the far-field noise.This is consistent with the far-field noise of the whole train model’s aerodynamic noise.The averaged sound pressure level of the far-field longitudinal noise measurement points differs by 1.92 dBA.The step-by-step numerical prediction method of aerodynamic noise of high-speed trains can provide a reference for the numerical prediction of aerodynamic noise generated by long marshalling high-speed trains.

Numerical prediction of spatio-temporal frosting patterns of curved surface considering varying working parameters

Accurate numerical prediction of frosting patterns is essential for the efficient layout and timing defrosting of heat exchangers under frosting conditions.In this study,a numerical model is developed to predict the spatio-temporal frosting habits on curved surfaces in combination with the correlations of frost density and thermal conductivity.In the model,frost melting is considered.After verification,the frosting and heat transfer characteristics along the flow path are investigated under various structural and operating conditions.Frost thickness along the path is mainly affected by the cooling surface temperature,while the heat and mass transfer rates are strongly correlated with the humidity ratio.The proportions of latent heat and sensible heat are distributed more unevenly in parallel flow case than in counter flow case.Frost deposition is facilitated by a smaller radius of curvature of the cooling surface.More uniform frosting characteristics along the path and smaller heat transfer obstruction are presented with a smaller length-to-height ratio of the flow path.

Experimental and numerical prediction of railway induced vibration

In this paper,both measurements and numerical simulations of railway induced vibration are discussed.A measurement campaign has been carried out along the high-speed railway track in Lincent,Belgium.The experimental determination of transfer functions and vibration velocity during train passages are discussed.A numerical model is introduced to predict the transfer functions and the vibration velocity during train passages.The comparison of experimental and numerical results demonstrates the importance of accurate numerical models and input data.The results are obtained in the framework of the development of a hybrid prediction method,where numerical and experimental data can be combined to improve the prediction accuracy for railway induced vibration.

System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction(SNAP):System Formulation and Preliminary Evaluation

A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.

Statistical downscaling of numerical weather prediction based on convolutional neural networks

Numerical Weather Prediction(NWP)is a necessary input for short-term wind power forecasting.Existing NWP models are all based on purely physical models.This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high.There is a need to further improve the timeliness and accuracy of the assimilation process.In order to solve the above problems,NWP method based on artificial intelligence is proposed in this paper.It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position.We considered the actual data of a wind farm in north China as an example to analyze the calculation example.The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model.The prediction accuracy in some months is better than that of the purely physical model,and the calculation efficiency is considerably improved.The validity and advantages of the proposed method are verified from the results,and the traditional NWP method is replaced to a certain extent.

Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions

Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.

Chen-Chao Koo and the Early Numerical Weather Prediction Experiments in China

Although the first successful numerical weather prediction(NWP)project led by Charney and von Neumann is widely known,little is known by the international community about the development of NWP during the 1950s in China.Here,a detailed historical perspective on the early NWP experiments in China is provided.The leadership in NWP of the late Professor Chen-Chao Koo,a protége of C.G.Rossby at the University of Stockholm during the late 1940s and a key leader of modern meteorology(particularly of atmospheric dynamics and physics)in China during the 1950s−70s,is highlighted.The unique contributions to NWP by Koo and his students,such as the ideas of formulating NWP as an“evolution”problem,in which the past data over multiple time steps are utilized,rather than an initial-value problem,and on the cybernetic aspects of atmospheric processes,i.e.,regarding the motion of the atmosphere at various time scales as an optimal control system,are also emphasized.

Simulation of Gas Oil Hydrotreater Heat Exchange Tube and Crystallization Prediction of NH_(4)Cl by Thermodynamic Equilibrium

The hydrotreater system heat exchanger is one of the main pieces of heat exchange equipment in petrochemical enterprises.In recent years,oil resources have shown a deterioration trend of high sulfur and high acid content,with corrosion risk being prominent in oil processing.Taking the multi-medium flow corrosion risk of the hydrotreater heat exchanger pipeline in a petrochemical enterprise as the research object,based on the parameter characteristics of corrosive NH_(3) and HCl media under a high-temperature and high-pressure environment,the ammonium salt crystallization and deposition mechanism under multi-phase flow is revealed.The thermodynamic equilibrium curve is modified based on the thermodynamic principle and fugacity coefficient variation,and the prediction model of ammonium chloride crystallization in hydrotreater heat exchanger under high temperature and high pressure is constructed according to the modification.This study uses the mixture model,the flow-thermal coupling method,and the discrete phase model method to carry out the numerical simulation of multiphase flow and the numerical prediction of particle distribution characteristics in the heat exchanger pipeline of the hydrotreater heat exchange equipment,so as to realize the quantitative prediction of the particle crystallization deposition distribution in the pipeline.The results show that with the decrease of temperature,the crystallization occurs first on both sides of the center of the tube bundle,and more crystallization occurs in the lower half of the U-shaped tube,which may seriously lead to problems such as pipe blockage and under-deposit corrosion.

The Potential Predictability of the South China Sea Summer Monsoon in a Dynamical Seasonal Prediction System

The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.

Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons

This paper presents an attempt at assimilating clear-sky FY-4A Advanced Geosynchronous Radiation Imager(AGRI)radiances from two water vapor channels for the prediction of three landfalling typhoon events over the West Pacific Ocean using the 3DVar data assimilation(DA)method along with the WRF model.A channel-sensitive cloud detection scheme based on the particle filter(PF)algorithm is developed and examined against a cloud detection scheme using the multivariate and minimum residual(MMR)algorithm and another traditional cloud mask–dependent cloud detection scheme.Results show that both channel-sensitive cloud detection schemes are effective,while the PF scheme is able to reserve more pixels than the MMR scheme for the same channel.In general,the added value of AGRI radiances is confirmed when comparing with the control experiment without AGRI radiances.Moreover,it is found that the analysis fields of the PF experiment are mostly improved in terms of better depicting the typhoon,including the temperature,moisture,and dynamical conditions.The typhoon track forecast skill is improved with AGRI radiance DA,which could be explained by better simulating the upper trough.The impact of assimilating AGRI radiances on typhoon intensity forecasts is small.On the other hand,improved rainfall forecasts from AGRI DA experiments are found along with reduced errors for both the thermodynamic and moisture fields,albeit the improvements are limited.

An Evaluation of Tropical Cyclone Genesis Forecast over the Western North Pacific and the South China Sea from the CMA-TRAMS

Tropical cyclone(TC) genesis forecasting is essential for daily operational practices during the typhoon season.The updated version of the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS) offers forecasters reliable numerical weather prediction(NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul(2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis,fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters.

Experimental and Numerical Investigation on the Mechanical Behaviors of the Velcro^(■) and Dual-Lock Fasteners

In order to characterize the mechanical behaviors of the Velcro~? and Dual-lock fasteners, a series of tests including the butt-joint(BJ) monotonic tensile and shear, mixed tensile-shear with various loading angles, the loading rates effects, the double cantilever beam(DCB) fracture and 180° peel experiments were performed. The tensile and shear tests results showed that the mechanical behaviors of Velcro~? fastener separation are analogous to ductile materials, and those of Dual-lock fasteners are more like brittle ones. The mixed tensile-shear with various loading angles tests results demonstrated that magnitudes of the peak stresses in 30°, 45°, and 60° have no significant differences, which are lower than those in the monotonic tensile or shear tests for the two fasteners. The effects of the loading rate tests show that the peak stresses of the Velcro~? fastener manifested good performance at the loading rate of 10 to 20 mm/min in the tensile and shear conditions, and the Dual-lock did it well around the loading rates of 10 to 20 mm/min in the tensile condition. The cohesive zone model(CZM) is employed to numerical predict the DCB fracture and the 180° peel tests. The CZM predictions results are proven to commendably capture the two tests separation processes, of the tow fasteners, and the numerical results agreed well with the peeling tests data of the Dual lock fasteners. The results and discussions in this study are expected to bring more understanding to engineers and designers about the performance of Velcro~? and Dual lock fasteners.

Numerical simulation and experimental verification of bubble size distribution in an air dense medium fluidized bed

Bubble size distribution is the basic apparent performance and obvious characteristics in the air dense medium fluidized bed (ADMFB). The approaches of numerical simulation and experimental verification were combined to conduct the further research on the bubble generation and movement behavior. The results show that ADMFB could display favorable expanded characteristics after steady fluidization. With different particle size distributions of magnetite powder as medium solids, we selected an appropriate prediction model for the mean bubble diameter in ADMFB. The comparison results indicate that the mean bubble diameters along the bed heights are 35 mm < D b < 66 mm and 40 mm < D b < 69 mm with the magnetite powder of 0.3 mm+0.15mm and 0.15mm+0.074mm, respectively. The prediction model provides good agreements with the experimental and simulation data. Based on the optimal operating gas velocity distribution, the mixture of magnetite powder and <1mm fine coal as medium solids were utilized to carry out the separation experiment on 6-50mm raw coal. The results show that an optimal separation density d P of 1.73g/cm 3 with a probable error E of 0.07g/cm 3 and a recovery efficiency of 99.97% is achieved, which indicates good separation performance by applying ADMFB.

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

The Regularized WSM6 Microphysical Scheme and Its Validation in WRF 4D-Var

A cold cloud assimilation scheme was developed that fully considers the water substances,i.e.,water vapor,cloud water,rain,ice,snow,and graupel,based on the single-moment WSM6 microphysical scheme and four-dimensional variational(4D-Var)data assimilation in the Weather Research and Forecasting data assimilation(WRFDA)system.The verification of the regularized WSM6 and its tangent linearity model(TLM)and adjoint mode model(ADM)was proven successful.Two groups of single observation and real sounding data assimilation experiments were set up to further verify the correctness of the assimilation scheme.The results showed that the consideration of ice,snow,and graupel in the assimilation system of the 4D-Var,as opposed to their omission in the warm rain Kessler scheme,allowed the water substances to be reasonably updated,further improving the forecast.Before it can be further applied in the assimilation of observational data,radar reflectivities,and satellite radiances,the cold cloud assimilation scheme needs additional verification,including using conventional ground and sounding observations in the 4D-Var assimilation system.

A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings.The refined grid forecast requires direct correction on gridded forecast products,as opposed to correcting forecast data only at individual weather stations.In this study,a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model(ECMWF-IFS): 2-m temperature,2-m relative humidity,10-m wind speed,and 10-m wind direction,with a forecast lead time of 24 h to 240 h in North China.First,the forecast correction problem is transformed into an image-toimage translation problem in deep learning under the CU-net architecture,which is based on convolutional neural networks.Second,the ECMWF-IFS forecasts and ECMWF reanalysis data(ERA5) from 2005 to 2018 are used as training,validation,and testing datasets.The predictors and labels(ground truth) of the model are created using the ECMWF-IFS and ERA5,respectively.Finally,the correction performance of CU-net is compared with a conventional method,anomaly numerical correction with observations(ANO).Results show that forecasts from CU-net have lower root mean square error,bias,mean absolute error,and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h.CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics,whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity.For the correction of the 10-m wind direction forecast,which is often difficult to achieve,CU-net also improves the correction performance.

A comprehensive review for wind,solar,and electrical load forecasting methods

Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.

THE IMPACT OF DIFFERENT PHYSICAL PROCESSES AND THEIR PARAMETERIZATIONS ON FORECAST OF A HEAVY RAINFALL IN SOUTH CHINA IN ANNUALLY FIRST RAINING SEASON

An ensemble prediction system based on the GRAPES model, using multi-physics, is used to discuss the influence of different physical processes in numerical models on forecast of heavy rainfall in South China in the annually first raining season(AFRS). Pattern, magnitude and area of precipitation, evolution of synoptic situation, as well as apparent heat source and apparent moisture sink between different ensemble members are comparatively analyzed. The choice of parameterization scheme for land-surface processes gives rise to the largest influence on the precipitation prediction. The influences of cumulus-convection and cloud-microphysics processes are mainly focused on heavy rainfall;the use of cumulus-convection parameterization tends to produce large-area and light rainfall. Change in parameterization schemes for land-surface and cumulus-convection processes both will cause prominent change in forecast of both dynamic and thermodynamic variables, while change in cloud-microphysics processes show primary impact on dynamic variables. Comparing simplified Arakawa-Schubert and Kain-Fritsch with Betts-Miller-Janjic schemes, SLAB with NOAH schemes, as well as both WRF single moment 6-class and NCEP 3-class with simplified explicit schemes of phase-mixed cloud and precipitation shows that the former predicts stronger low-level jets and high humidity concentration, more convective rainfall and local heavy rainfall, and have better performance in precipitation forecast. Appropriate parameterization schemes can reasonably describe the physical process related to heavy rainfall in South China in the AFRS, such as low-level convergence, latent heat release, vertical transport of heat and water vapor, thereby depicting the multi-scale interactions of low-level jet and meso-scale convective systems in heavy rainfall suitably, and improving the prediction of heavy rainfall in South China in the AFRS as a result.

Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning

The quantitative precipitation forecast(QPF)performance by numerical weather prediction(NWP)methods depends fundamentally on the adopted physical parameterization schemes(PS).However,due to the complexity of the physical mechanisms of precipitation processes,the uncertainties of PSs result in a lower QPF performance than their prediction of the basic meteorological variables such as air temperature,wind,geopotential height,and humidity.This study proposes a deep learning model named QPFNet,which uses basic meteorological variables in the ERA5 dataset by fitting a non-linear mapping relationship between the basic variables and precipitation.Basic variables forecasted by the highest-resolution model(HRES)of the European Centre for Medium-Range Weather Forecasts(ECMWF)were fed into QPFNet to forecast precipitation.Evaluation results show that QPFNet achieved better QPF performance than ECMWF HRES itself.The threat score for 3-h accumulated precipitation with depths of 0.1,3,10,and 20 mm increased by 19.7%,15.2%,43.2%,and 87.1%,respectively,indicating the proposed performance QPFNet improved with increasing levels of precipitation.The sensitivities of these meteorological variables for QPF in different pressure layers were analyzed based on the output of the QPFNet,and its performance limitations are also discussed.Using DL to extract features from basic meteorological variables can provide an important reference for QPF,and avoid some uncertainties of PSs.