Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Bridging element-free Galerkin and pluri-Gaussian simulation for geological uncertainty estimation in an ensemble smoother data assimilation framework

The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.

On the Influences of Urbanization on the Extreme Rainfall over Zhengzhou on 20 July 2021: A Convection-Permitting Ensemble Modeling Study

This study investigates the influences of urban land cover on the extreme rainfall event over the Zhengzhou city in central China on 20 July 2021 using the Weather Research and Forecasting model at a convection-permitting scale[1-km resolution in the innermost domain(d3)].Two ensembles of simulation(CTRL,NURB),each consisting of 11 members with a multi-layer urban canopy model and various combinations of physics schemes,were conducted using different land cover scenarios:(i)the real urban land cover,(ii)all cities in d3 being replaced with natural land cover.The results suggest that CTRL reasonably reproduces the spatiotemporal evolution of rainstorms and the 24-h rainfall accumulation over the key region,although the maximum hourly rainfall is underestimated and displaced to the west or southwest by most members.The ensemble mean 24-h rainfall accumulation over the key region of heavy rainfall is reduced by 13%,and the maximum hourly rainfall simulated by each member is reduced by 15–70 mm in CTRL relative to NURB.The reduction in the simulated rainfall by urbanization is closely associated with numerous cities/towns to the south,southeast,and east of Zhengzhou.Their heating effects jointly lead to formation of anomalous upward motions in and above the planetary boundary layer(PBL),which exaggerates the PBL drying effect due to reduced evapotranspiration and also enhances the wind stilling effect due to increased surface friction in urban areas.As a result,the lateral inflows of moisture and high-θe(equivalent potential temperature)air from south and east to Zhengzhou are reduced.

Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach

In order to reduce the uncertainty of offline land surface model （LSM） simulations of land evapotranspiration （ET）, we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS （Institute of Tibetan Plateau Research, Chinese Academy of Sciences）, Qian] and four LSMs （BATS, VIC, CLM3.0 and CLM3.5）, to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over China's Mainland during 1982-2007. The results showed that various simulations of each member and their arithmetic mean （EnsAVlean） could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates （Obs_MTE）. For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs_MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens_Mean was closer to Obs_MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs_MTE and Ens_Mean exhibited a significant increasing trend during 1982-98; whereas after 1998, when the last big EI Nifio event occurred, the Ens_Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs_MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.

Analysis of an Ensemble of High-Resolution WRF Simulations for the Rapid Intensification of Super Typhoon Rammasun(2014)

Diagnostics are presented from an ensemble of high-resolution forecasts that differed markedly in their predictions of the rapid intensification(RI)of Typhoon Rammasun.We show that the basic difference stems from subtle differences in initializations of(a)500-850-h Pa environmental winds,and(b)midlevel moisture and ventilation.We then describe how these differences impact on the evolving convective organization,storm structure,and the timing of RI.As expected,ascent,diabatic heating and the secondary circulation near the inner-core are much stronger in the member that best forecasts the RI.The evolution of vortex cloudiness from this member is similar to the actual imagery,with the development of an inner cloud band wrapping inwards to form the eyewall.We present evidence that this structure,and hence the enhanced diabatic heating,is related to the tilt and associated dynamics of the developing inner-core in shear.For the most accurate ensemble member:(a)inhibition of ascent and a reduction in convection over the up-shear sector allow moistening of the boundary-layer air,which is transported to the down-shear sector to feed a developing convective asymmetry;(b)with minimal ventilation,undiluted clouds and moisture from the down-shear left quadrant are then wrapped inwards to the up-shear left quadrant to form the eyewall cloud;and(c)this process seems related to a critical down-shear tilt of the vortex from midlevels,and the vertical phase-locking of the circulation over up-shear quadrants.For the member that forecasts a much-delayed RI,these processes are inhibited by stronger vertical wind shear,initially resulting in poor vertical coherence of the circulation,lesser moisture and larger ventilation.Our analysis suggests that ensemble prediction is needed to account for the sensitivity of forecasts to a relatively narrow range of environmental wind shear,moisture and vortex inner-structure.

An efficient framework for ensemble of natural disaster simulations as a service

Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors.Such large ensembles cannot typically be run on a single computer due to the limited computational resources available.Cloud Computing offers an attractive alternative,with an almost unlimited capacity for computation,storage,and network bandwidth.However,there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources.As such,this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud.The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models.We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements.The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.

Observation system simulation experiments using an ensemble-based method in the northeastern South China Sea

An ensemble-based method for the observation system simulation experiment(OSSE)is employed to design optimal observation stations and assess the present observation stations in the northeastern South China Sea(SCS).We employed the 20-year(1992-2012)sea surface height(SSH)data to design an array to monitor the intraseasonal to interannual variability.The results show that the most key region was found located at the northwest of Luzon Island(LI)where the energetic Luzon cyclonic gyre(LCG)occurs;other key regions include the edge of the LCG,the northwest of the Luzon Strait(LS),and the southwest of Taiwan,China.By contrast,we found that the present observation stations might oversample at the northwest of the LS and undersample at the northwest of LI.In addition,the optimal stations perform better in a larger area than the present stations.In vertical direction,the key layer is located within the upper 200-m depth,of which the surface and subsurface layers are most valuable to the observing system.

Improving the simulation of terrestrial water storage anomalies over China using a Bayesian model averaging ensemble approach

The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.

GIBBS ENSEMBLE MONTE CARLO SIMULATION OF VAPORLIQUID EQUILIBRIA FOR SIMPLE FLUIDS

The Gibbs ensemble Monte Carlo simulation method has been investigated and used to calculate the vaporliquid equilibria of a real binary mixture, argon-methane. In this simulation, the micro-structural and macroscopic properties, including the distribution function, internal energy, densities and compositions of coexisting vapor and liquid phases as well as the enthalpy of vaporization are obtained. Compared with experimental data and calculated results from equations of state, our simulated phase equilibrium properties are of good accuracy, which demonstrates that this simulation methods is a powerful tool in research on phase equilibria of fluids.

The construction of general basis functions in reweighting ensemble dynamics simulations: Reproduce equilibrium distribution in complex systems from multiple short simulation trajectories

Ensemble simulations, which use multiple short independent trajectories from dispersive initial conformations, rather than a single long trajectory as used in traditional simulations, are expected to sample complex systems such as biomolecules much more efficiently. The re-weighted ensemble dynamics（RED） is designed to combine these short trajectories to reconstruct the global equilibrium distribution. In the RED, a number of conformational functions, named as basis functions,are applied to relate these trajectories to each other, then a detailed-balance-based linear equation is built, whose solution provides the weights of these trajectories in equilibrium distribution. Thus, the sufficient and efficient selection of basis functions is critical to the practical application of RED. Here, we review and present a few possible ways to generally construct basis functions for applying the RED in complex molecular systems. Especially, for systems with less priori knowledge, we could generally use the root mean squared deviation（RMSD） among conformations to split the whole conformational space into a set of cells, then use the RMSD-based-cell functions as basis functions. We demonstrate the application of the RED in typical systems, including a two-dimensional toy model, the lattice Potts model, and a short peptide system. The results indicate that the RED with the constructions of basis functions not only more efficiently sample the complex systems, but also provide a general way to understand the metastable structure of conformational space.

Reweighted ensemble dynamics simulations:Theory,improvement,and application

Based on multiple parallel short molecular dynamics simulation trajectories, we designed the reweighted ensem- ble dynamics （RED） method to more efficiently sample complex （biopolymer） systems, and to explore their hierarchical metastable states. Here we further present an improvement to depress statistical errors of the RED and we discuss a few keys in practical application of the RED, provide schemes on selection of basis functions, and determination of the free parameter in the RED. We illustrate the application of the improvements in two toy models and in the solvated alanine dipeptide. The results show the RED enables us to capture the topology of multiple-state transition networks, to detect the diffusion-like dynamical behavior in an entropy-dominated system, and to identify solvent effects in the solvated peptides. The illustrations serve as general applications of the RED in more complex biopolymer systems.

CWRF-based ensemble simulation of tropical cyclone activity near China and its sensitivity to the model physical parameterization schemes

To evaluate the downscaling ability with respect to tropical cyclones(TCs)near China and its sensitivity to the model physics representation,the authors performed a multi-physics ensemble simulation with the regional Climate-Weather Research and Forecasting(CWRF)model at a 30 km resolution driven by ERA-Interim reanalysis data.The ensemble consisted of 28 integrations during 1979-2016 with varying CWRF physics configurations.Both CWRF and ERA-Interim can generally capture the seasonal cycle and interannual variation of the TC number near China,but evidently underestimate them.The CWRF downscaling and its multi-physics ensemble can notably reduce the underestimation and significantly improve the simulation of the TC occurrences.The skill enhancement is especially large in terms of the interannual variation,which is most sensitive to the cumulus scheme,followed by the boundary layer,surface and radiation schemes,but weakly sensitive to the cloud and microphysics schemes.Generally,the Noah surface scheme,CAML(CAM radiation scheme as implemented by Liang together with the diagnostic cloud cover scheme of Xu and Randall(1996))radiation scheme,prognostic cloud scheme,and Thompson microphysics scheme stand out for their better performance in simulating the interannual variation of TC number.However,the Emanuel cumulus and MYNN boundary layer schemes produce severe interannual biases.Our study provides a valuable reference for CWRF application to improve the understanding and prediction of TC activity.

Ensemble Cloud Model Application in Simulating the Catastrophic Heavy Rainfall Event

An attempt has been made in the present research to simulate a deadly flash-flood event over the City of Skopje,Macedonia on 6 August 2016.A cloud model ensemble forecast method is developed to simulate a super-cell storm’s initiation and evolutionary features.Sounding data are generated using an ensemble approach,that utilizes a triple-nested WRF model.A three-dimensional(3-D)convective cloud model(CCM)with a very fine horizontal grid resolution of 250-m is initialized,using the initial representative sounding data,derived from the WRF 1-km forecast outputs.CCM is configured and run with an open lateral boundary conditions LBC,allowing explicit simulation of convective scale processes.This preliminary study showed that the ensemble approach has some advantages in the generation of the initial data and the model initialization.The applied method minimizes the uncertainties and provides a more qualitative-quantitative assessment of super-cell storm initiation,cell structure,evolutionary properties,and intensity.A high-resolution 3-D run is capable to resolve detailed aspects of convection,including high-intensity convective precipitation.The results are significant not only from the aspect of the cloud model’s ability to provide a qualitative-quantitative assessment of intense precipitation but also for a deeper understanding of the essence of storm development,its vortex dynamics,and the meaning of micro-physical processes for the production and release of large amounts of precipitation that were the cause of the catastrophic flood in an urban area.After a series of experiments and verification,such a system could be a reliable tool in weather services for very short-range forecasting(now-casting)and early warning of weather disasters.

Hybrid stacking ensemble algorithm and simulated annealing optimization for stability evaluation of underground entry-type excavations

The stability of underground entry-type excavations(UETEs)is of paramount importance for ensuring the safety of mining operations.As more engineering cases are accumulated,machine learning(ML)has demonstrated great potential for the stability evaluation of UETEs.In this study,a hybrid stacking ensemble method aggregating support vector machine(SVM),k-nearest neighbor(KNN),decision tree(DT),random forest(RF),multilayer perceptron neural network(MLPNN)and extreme gradient boosting(XGBoost)algorithms was proposed to assess the stability of UETEs.Firstly,a total of 399 historical cases with two indicators were collected from seven mines.Subsequently,to pursue better evaluation performance,the hyperparameters of base learners(SVM,KNN,DT,RF,MLPNN and XGBoost)and meta learner(MLPNN)were tuned by combining a five-fold cross validation(CV)and simulated annealing(SA)approach.Based on the optimal hyperparameters configuration,the stacking ensemble models were constructed using the training set(75%of the data).Finally,the performance of the proposed approach was evaluated by two global metrics(accuracy and Cohen’s Kappa)and three within-class metrics(macro average of the precision,recall and F1-score)on the test set(25%of the data).In addition,the evaluation results were compared with six base learners optimized by SA.The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy,Kappa coefficient,macro average of the precision,recall and F1-score were 0.92,0.851,0.885,0.88 and 0.883,respectively.The rock mass rating(RMR)had the most important influence on evaluation results.Moreover,the critical span graph(CSG)was updated based on the proposed model,representing a significant improvement compared with the previous studies.This study can provide valuable guidance for stability analysis and risk management of UETEs.However,it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.

Random dynamic analysis of vertical train–bridge systems under small probability by surrogate model and subset simulation with splitting

The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.

Ensemble Forecast for Tropical Cyclone Based on CNOP-P Method:A Case Study of WRF Model and Two Typhoons

In this paper,we set out to study the ensemble forecast for tropical cyclones.The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)method and the WRF model to improve the prediction accuracy for track and intensity,and two different typhoons are selected as cases for analysis.We first select perturbed parameters in the YSU and WSM6 schemes,and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters.Finally,perturbations are imposed on default parameter values to generate the ensemble members.The whole proposed procedures are referred to as the PerturbedParameter Ensemble(PPE).We also conduct two experiments,which are control forecast and ensemble forecast,termed Ctrl and perturbed-physics ensemble(PPhyE)respectively,to demonstrate the performance for contrast.In the article,we compare the effects of three experiments on tropical cyclones in aspects of track and intensity,respectively.For track,the prediction errors of PPE are smaller.The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members.As for intensity,ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time.The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall.The PPE also shows uncertainty in the forecast.Moreover,we also analyze the track and intensity from physical variable fields of PPE.Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.

An ensemble-based SST nudging method proposed for correcting the subsurface temperature field in climate model

An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature(SST) observations into the Max Planck Institute(MPI) climate model,ECHAM5/MPI-OM. This method can project SST directly to subsurface according to model ensemble-based correlations between SST and subsurface temperature. Results from a 50 year(1960–2009) assimilation experiment show the method can improve the subsurface temperature field up to 300 m compared to the qualitycontrolled subsurface ocean temperature objective analyses(EN4), through reducing the biases of the thermal states, improving the thermocline structure, and reducing the root mean square(RMS) errors. Moreover, as most of the improvements concentrate over the upper 100 m, the ocean heat content in the upper 100 m(OHT100 m)is further adopted as a property to validate the performance of the ensemble-based correction method. The results show that RMS errors of the global OHT100 m convergent to one value after several times iteration,indicating this method can represent the relationship between SST and subsurface temperature fields well, and then improve the accuracy of the simulation in the subsurface temperature of the climate model.

Vapor-Liquid Equilibrium Simulation of Binary and Ternary Mixtures of CH_4,C_2H_4 and iso-C_4H_(10)

The vapor-liquid equilibrium(VLE) properties for the binary and ternary mixtures of CH4,C2H4 and isoC4H10 are of great importance in the recovery of ethylene from mixture containing CH4 and C2H4 with iso-C4H10 as solvent.Hence,Gibbs ensemble Monte Carlo(GEMC) simulations were used to estimate vapor-liquid equilibrium for the binary and ternary mixtures of CH4,C2H4 and iso-C4H10 with the united atom potential NERD model.The selected simulation conditions are based on the experiment in the literature.The results of this work were shown to be in satisfactory agreement with available experimental data and predictions of Peng-Robinson equation of state.The structure of simulated liquid phase is also characterized by radial distribution function(RDF),which contributes to further understanding of the VLE curve of these systems.RDF is not sensitive to the pressure and temperature range.With the increase of pressure or the decrease of temperature,the molecules tend to gather together.

Hierarchical Visual Analysis and Steering Framework for Astrophysical Simulations

A framework for accelerating modern long-running astrophysical simulations is presented, which is based on a hierarchical architecture where computational steering in the high-resolution run is performed under the guide of knowledge obtained in the gradually refined ensemble analyses. Several visualization schemes for facilitating ensemble management, error analysis, parameter grouping and tuning are also integrated owing to the pluggable modular design. The proposed approach is prototyped based on the Flash code, and it can be extended by introducing userdefined visualization for specific requirements. Two real-world simulations, i.e., stellar wind and supernova remnant, are carried out to verify the proposed approach.

基于Stacking集成学习模型的苹果树逐日蒸散量模拟研究

为准确模拟苹果树逐日蒸散量,以支持向量机(SVM)、多层感知机(MLP)、随机森林(RF)和梯度提升决策树(GBDT)为初级学习器,以多元线性回归(MLR)为次级学习器,基于Stacking策略建立集成学习模型(LSM),将LSM模型的模拟精度与MLR、SVM、MLP、RF、GBDT模型的模拟精度进行对比。结果表明,影响苹果树蒸散量的主要因子为日平均太阳辐射、相对湿度、风速、温度和日序数,最大互信息值分别为0.97、0.72、0.63、0.62、0.60,表层土壤温度及土壤含水率对蒸散量的影响较小。相比于MLR、SVM、MLP、RF、GBDT模型,LSM模型的模拟精度最高,MLR模型的模拟精度最低;使用日平均太阳辐射、相对湿度、风速、温度及日序数5个特征参数在准确模拟苹果树蒸散量的同时,还能降低特征的获取成本。研究结果可为苹果树逐日蒸散量的精准模拟提供有效方法。