An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dyna...An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).展开更多
In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions...In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions.Annual medians of these gradients for quiet days are computed as templates.The metric distances(L2N)and Symmetric Kullback-Leibler Distances(SKLD)are obtained between the templates and the daily gradient series.The grouped histograms are fitted to the prospective Probability Density Functions(PDF).The method is applied to the Slant Total Electron Content(STEC)estimates from the Turkish National Permanent GPS Network(TNPGN-Active)for 2015.The highest gradients are observed in the east-west axis with a maximum of 25 mm/km during a geomagnetic storm.The maximum differences from the gradient templates occur for neighboring stations within100-130 km distance away from each other,during night hours,and for regions bordering the Black Sea and the Mediterranean in the northeast and southeast of Turkey.The empirical PDFs of the stationpair gradients are predominantly Weibull-distributed.The mean values of Weibull PDFs in all station groups are between 1.2 and 1.8 mm/km,with an increase during noon and afternoon hours.The standard deviations of the gradient PDFs generally increase during night hours.The algorithm will form a basis for quantifying the stochastic variations of the spatial rate of change of TEC trends in midlatitude regions,thus supplementing reliable and accurate regional monitoring of ionospheric variability.展开更多
The recent discovery of type-Ⅶboron-carbon clathrates with calculated superconducting transition temperatures approaching~100 K has sparked interest in exploring new conventional superconductors that may be stabilize...The recent discovery of type-Ⅶboron-carbon clathrates with calculated superconducting transition temperatures approaching~100 K has sparked interest in exploring new conventional superconductors that may be stabilized at ambient pressure.The electronic structure of the clathrate is highly tunable based on the ability to substitute different metal atoms within the cages,which may also be large enough to host small molecules.Here we introduce molecular hydrogen(H_(2))within the clathrate cages and investigate its impact on electron-phonon coupling interactions and the superconducting transition temperature(T_(c)).Our approach involves combining molecular hydrogen with the new diamond-like covalent framework,resulting in a hydrogen-encapsulated clathrate,(H_(2))B_(3)C_(3).A notable characteristic of(H_(2))B_(3)C_(3)is the dynamic behavior of the H_(2)molecules,which exhibit nearly free rotations within the B-C cages,resulting in a dynamic structure that remains cubic on average.The static structure of(H_(2))B_(3)C_(3)(a snapshot in its dynamic trajectory)is calculated to be dynamically stable at ambient and low pressures.Topological analysis of the electron density reveals weak van der Waals interactions between molecular hydrogen and the B-C cages,marginally influencing the electronic structure of the material.The electron count and electronic structure calculations indicate that(H_(2))B_(3)C_(3)is a hole conductor,in which H_(2)molecules donate a portion of their valence electron density to the metallic cage framework.Electron-phonon coupling calculation using the Migdal-Eliashberg theory predicts that(H_(2))B_(3)C_(3)possesses a T_(c) of 46 K under ambient pressure.These results indicate potential for additional light-element substitutions within the type-Ⅶclathrate framework and suggest the possibility of molecular hydrogen as a new approach to optimizing the electronic structures of this new class of superconducting materials.展开更多
Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector ...Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.展开更多
The purpose of this study was to design and test a statistical-dynamical scheme for the extraseasonal（one season in advance） prediction of summer rainfall at 160 observation stations across China.The scheme combined...The purpose of this study was to design and test a statistical-dynamical scheme for the extraseasonal（one season in advance） prediction of summer rainfall at 160 observation stations across China.The scheme combined both valuable information from the preceding observations and dynamical information from synchronous numerical predictions of atmospheric circulation factors produced by an atmospheric general circulation model.First,the key preceding climatic signals and synchronous atmospheric circulation factors that were not only closely related to summer rainfall but also numerically predictable were identified as the potential predictors.Second,the extraseasonal prediction models of summer rainfall were constructed using a multivariate linear regression analysis for 15 subregions and then 160 stations across China.Cross-validation analyses performed for the period 1983-2008 revealed that the performance of the prediction models was not only high in terms of interannual variation,trend,and sign but also was stable during the whole period.Furthermore,the performance of the scheme was confirmed by the accuracy of the real-time prediction of summer rainfall during 2009 and 2010.展开更多
In the first paper in this series, a variational data assimilation of ideal tropical cyclone （TC） tracks was performed for the statistical-dynamical prediction model SD-90 by the adjoint method, and a prediction of ...In the first paper in this series, a variational data assimilation of ideal tropical cyclone （TC） tracks was performed for the statistical-dynamical prediction model SD-90 by the adjoint method, and a prediction of TC tracks was made with good accuracy for tracks containing no sharp turns. In the present paper, the cases of real TC tracks are studied. Due to the complexity of TC motion, attention is paid to the diagnostic research of TC motion. First, five TC tracks are studied. Using the data of each entire TC track, by the adjoint method, five TC tracks are fitted well, and the forces acting on the TCs are retrieved. For a given TC, the distribution of the resultant of the retrieved force and Coriolis force well matches the corresponding TC track, i.e., when a TC turns, the resultant of the retrieved force and Coriolis force acts as a centripetal force, which means that the TC indeed moves like a particle; in particular, for TC 9911, the clockwise looping motion is also fitted well. And the distribution of the resultant appears to be periodic in some cases. Then, the present method is carried out for a portion of the track data for TC 9804, which indicates that when the amount of data for a TC track is sufficient, the algorithm is stable. And finally, the same algorithm is implemented for TCs with a double-eyewall structure, namely Bilis （2000） and Winnie （1997）, and the results prove the applicability of the algorithm to TCs with complicated mesoscale structures if the TC track data are obtained every three hours.展开更多
The cold vortex is a major high impact weather system in northeast China during the warm season, its frequent activities also affect the short-term climate throughout eastern China. How to objectively and quantitative...The cold vortex is a major high impact weather system in northeast China during the warm season, its frequent activities also affect the short-term climate throughout eastern China. How to objectively and quantitatively predict the intensity trend of the cold vortex is an urgent and difficult problem for current short-term climate prediction. Based on the dynamical-statistical combining principle, the predicted results of the Beijing Climate Center＇s global atmosphereocean coupled model and rich historical data are used for dynamic-statistical extra-seasonal prediction testing and actual prediction of the summer 500-hPa geopotential height over the cold vortex activity area. The results show that this method can significantly reduce the model＇s prediction error over the cold vortex activity area, and improve the prediction skills. Furthermore, the results of the sensitivity test reveal that the predicted results are highly dependent on the quantity of similar factors and the number of similar years.展开更多
Following Tsai & Ma[1] and Tsai & Liu[2], a statistical and dynamical near-wall turbulent coherent structural model with separate consideration of two different portions:locally generated and upstream-transpo...Following Tsai & Ma[1] and Tsai & Liu[2], a statistical and dynamical near-wall turbulent coherent structural model with separate consideration of two different portions:locally generated and upstream-transported large eddies has been established.With this model, heat transfer in a fully developed open channel in the absence of pressure gradient is numerically simulated. Database of fluctuations of velocity and temperature has also been set. Numerical analysis shows the existence of high-low temperature streak caused by near-wall coherent structure and its swing in the lateral direction.Numerical results are in accordance with the computations and experimental results of other researchers.展开更多
The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is n...The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is no simple way to reconcile them. So, simple climate models, like statistical-dynamical models (SDMs), appear to be useful in this context. This kind of models is essentially mechanistic, being directed towards understanding the dependence of a particular mechanism on the other parameters of the problem. In this paper, the utility of SDMs for studies of climate change is discussed in some detail. We show that these models are an indispensable part of hierarchy of climate models.展开更多
The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown th...The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.展开更多
How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interio...How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis （SEA） model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics （CFD） and sensitivity analysis of acoustic contribution.展开更多
Purpose–The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system.This paper aims to define and substantiate the ass...Purpose–The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system.This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice.The key principles and approacheswill be proposed,and their applications to high-speed trains in Chinawill be presented.Design/methodology/approach–First,the structural integrity and dynamical integrity of high-speed trains are defined,and their relationship is introduced.Then,the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided.Finally,the principles and approaches for assessing the dynamical integrity of highspeed trains are presented and a novel operational assessment method is further presented.Findings–Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system.For assessing the structural integrity of structural components,an open-loop analysis considering both normal and abnormal vehicle conditions is needed.For assessing the structural integrity of dynamical components,a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed.The analysis of vehicle system dynamics should follow the principles of complete objects,conditions and indices.Numerical,experimental and operational approaches should be combined to achieve effective assessments.Originality/value–The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects,better lifespan management of train components and better maintenance decision-making for high-speed trains.展开更多
Earthquakes triggered by dynamic disturbances have been confirmed by numerous observations and experiments.In the past several decades,earthquake triggering has attracted increasing attention of scholars in relation t...Earthquakes triggered by dynamic disturbances have been confirmed by numerous observations and experiments.In the past several decades,earthquake triggering has attracted increasing attention of scholars in relation to exploring the mechanism of earthquake triggering,earthquake prediction,and the desire to use the mechanism of earthquake triggering to reduce,prevent,or trigger earthquakes.Natural earthquakes and large‐scale explosions are the most common sources of dynamic disturbances that trigger earthquakes.In the past several decades,some models have been developed,including static,dynamic,quasi‐static,and other models.Some reviews have been published,but explosiontriggered seismicity was not included.In recent years,some new results on earthquake triggering have emerged.Therefore,this paper presents a new review to reflect the new results and include the content of explosion‐triggered earthquakes for the reference of scholars in this area.Instead of a complete review of the relevant literature,this paper primarily focuses on the main aspects of dynamic earthquake triggering on a tectonic scale and makes some suggestions on issues that need to be resolved in this area in the future.展开更多
In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three...In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Models of marine ecosystem dynamics play an important role in revealing the evolution mechanisms of marine ecosystems and in forecasting their future changes. Most traditional ecological dynamics models are establishe...Models of marine ecosystem dynamics play an important role in revealing the evolution mechanisms of marine ecosystems and in forecasting their future changes. Most traditional ecological dynamics models are established based on basic physical and biological laws, and have obvious dynamic characteristics and ecological significance. However, they are not flexible enough for the variability of environment conditions and ecological processes found in offshore marine areas, where it is often difficult to obtain parameters for the model, and the precision of the model is often low. In this paper, a new modeling method is introduced, which aims to establish an evolution model of marine ecosystems by coupling statistics with differential dynamics. Firstly, we outline the basic concept and method of inverse modeling of marine ecosystems. Then we set up a statistical dynamics model of marine ecosystems evolution according to annual ecological observation data from Jiaozhou Bay. This was done under the forcing conditions of sea surface temperature and surface irradiance and considering the state variables of phytoplankton, zooplankton and nutrients. This model is dynamic, makes the best of field observation data, and the average predicted precision can reach 90% or higher. A simpler model can be easily obtained through eliminating the terms with smaller contributions according to the weight coefficients of model differential items. The method proposed in this paper avoids the difficulties of obtaining and optimizing parameters, which exist in traditional research, and it provides a new path for research of marine ecological dynamics.展开更多
Canopy interception of incident precipitation, as a critical component of a forest＇s water budget, can affect the amount of water available to the soil, and ultimately vegetation distribution and function. In this pa...Canopy interception of incident precipitation, as a critical component of a forest＇s water budget, can affect the amount of water available to the soil, and ultimately vegetation distribution and function. In this paper, a statistical-dynamic approach based on leaf area index and statistical canopy interception is used to parameterize the canopy interception process. The statistical-dynamic canopy interception scheme is implemented into the Community Land Model with dynamic global vegetation model （CLM-DGVM） to improve its dynamic vegetation simulation. The simulation for continental China by the land surface model with the new canopy interception scheme shows that the new one reasonably represents the precipitation intercepted by the canopy. Moreover, the new scheme enhances the water availability in the root zone for vegetation growth, especially in the densely vegetated and semi-arid areas, and improves the model＇s performance of potential vegetation simulation.展开更多
Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks,...Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.展开更多
This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc...This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and Friedman.It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns.The study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios.The findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements.This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.展开更多
Dead heart of sugarcane is an important symptom caused by borer attack. In the present study, the spatial distribution and dynamics of dead heart of sugarcane in the field were investigated based on geostatistical ana...Dead heart of sugarcane is an important symptom caused by borer attack. In the present study, the spatial distribution and dynamics of dead heart of sugarcane in the field were investigated based on geostatistical analysis, and semivariograms were computed in four separate directions(0°, 45°, 90° and 135°) and fitted with various theoretical models to determine the best fitted one. The Ordinary Kriging was used to interpolate spatial data. The results revealed that the density of dead hearts of sugarcane increased in a single-peak pattern, and the degree of spatial aggregation and random variation both decreased with the increase in the density of dead heart. In addition, dead heart of sugarcane caused by borer exhibited spatial aggregation.With the increase in the density of dead heart, the degree of spatial aggregation decreased, while the correlation increased. Kriging interpolation indicated that the correlation between the spatial patches was weak in early seedling stage, and became strong in middle and late seedling stage.展开更多
基金Botnia-Atlantica, an EU-programme financing cross border cooperation projects in Sweden, Finland and Norway, for their support of this work through the WindCoE project
文摘An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).
基金supported by TUBITAK 112E568,114E092,and 115E915 projectsTNPGN-Active RINEX data set is available to the IONOLAB group for the TUBITAK 109E055 project。
文摘In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions.Annual medians of these gradients for quiet days are computed as templates.The metric distances(L2N)and Symmetric Kullback-Leibler Distances(SKLD)are obtained between the templates and the daily gradient series.The grouped histograms are fitted to the prospective Probability Density Functions(PDF).The method is applied to the Slant Total Electron Content(STEC)estimates from the Turkish National Permanent GPS Network(TNPGN-Active)for 2015.The highest gradients are observed in the east-west axis with a maximum of 25 mm/km during a geomagnetic storm.The maximum differences from the gradient templates occur for neighboring stations within100-130 km distance away from each other,during night hours,and for regions bordering the Black Sea and the Mediterranean in the northeast and southeast of Turkey.The empirical PDFs of the stationpair gradients are predominantly Weibull-distributed.The mean values of Weibull PDFs in all station groups are between 1.2 and 1.8 mm/km,with an increase during noon and afternoon hours.The standard deviations of the gradient PDFs generally increase during night hours.The algorithm will form a basis for quantifying the stochastic variations of the spatial rate of change of TEC trends in midlatitude regions,thus supplementing reliable and accurate regional monitoring of ionospheric variability.
基金supported by Carnegie Canada and Natural Sciences and Engineering Research Council of Canada(NSERC)support from the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences,under Award No.DESC0020683。
文摘The recent discovery of type-Ⅶboron-carbon clathrates with calculated superconducting transition temperatures approaching~100 K has sparked interest in exploring new conventional superconductors that may be stabilized at ambient pressure.The electronic structure of the clathrate is highly tunable based on the ability to substitute different metal atoms within the cages,which may also be large enough to host small molecules.Here we introduce molecular hydrogen(H_(2))within the clathrate cages and investigate its impact on electron-phonon coupling interactions and the superconducting transition temperature(T_(c)).Our approach involves combining molecular hydrogen with the new diamond-like covalent framework,resulting in a hydrogen-encapsulated clathrate,(H_(2))B_(3)C_(3).A notable characteristic of(H_(2))B_(3)C_(3)is the dynamic behavior of the H_(2)molecules,which exhibit nearly free rotations within the B-C cages,resulting in a dynamic structure that remains cubic on average.The static structure of(H_(2))B_(3)C_(3)(a snapshot in its dynamic trajectory)is calculated to be dynamically stable at ambient and low pressures.Topological analysis of the electron density reveals weak van der Waals interactions between molecular hydrogen and the B-C cages,marginally influencing the electronic structure of the material.The electron count and electronic structure calculations indicate that(H_(2))B_(3)C_(3)is a hole conductor,in which H_(2)molecules donate a portion of their valence electron density to the metallic cage framework.Electron-phonon coupling calculation using the Migdal-Eliashberg theory predicts that(H_(2))B_(3)C_(3)possesses a T_(c) of 46 K under ambient pressure.These results indicate potential for additional light-element substitutions within the type-Ⅶclathrate framework and suggest the possibility of molecular hydrogen as a new approach to optimizing the electronic structures of this new class of superconducting materials.
文摘Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.
基金provided by the Special Scientific Research Fund of Meteorological Public Welfare Profession of China(Grant No. GYHY200906018)the National Basic Research Program of China (Grant Nos. 2009CB421406 and 2010CB950304)the Knowledge Innovation Project of the Chinese Academy of Sciences (Grant No. KZCX2-YW-Q03-3)
文摘The purpose of this study was to design and test a statistical-dynamical scheme for the extraseasonal（one season in advance） prediction of summer rainfall at 160 observation stations across China.The scheme combined both valuable information from the preceding observations and dynamical information from synchronous numerical predictions of atmospheric circulation factors produced by an atmospheric general circulation model.First,the key preceding climatic signals and synchronous atmospheric circulation factors that were not only closely related to summer rainfall but also numerically predictable were identified as the potential predictors.Second,the extraseasonal prediction models of summer rainfall were constructed using a multivariate linear regression analysis for 15 subregions and then 160 stations across China.Cross-validation analyses performed for the period 1983-2008 revealed that the performance of the prediction models was not only high in terms of interannual variation,trend,and sign but also was stable during the whole period.Furthermore,the performance of the scheme was confirmed by the accuracy of the real-time prediction of summer rainfall during 2009 and 2010.
基金This work was supported jointly by the Typhoon Foundation of Shanghaiby LASC of the Institute of Atmospheric Physics of the Chinese Academy of Sciencesby the National Natural Science Foundation of China under Grant No. 40633030.
文摘In the first paper in this series, a variational data assimilation of ideal tropical cyclone （TC） tracks was performed for the statistical-dynamical prediction model SD-90 by the adjoint method, and a prediction of TC tracks was made with good accuracy for tracks containing no sharp turns. In the present paper, the cases of real TC tracks are studied. Due to the complexity of TC motion, attention is paid to the diagnostic research of TC motion. First, five TC tracks are studied. Using the data of each entire TC track, by the adjoint method, five TC tracks are fitted well, and the forces acting on the TCs are retrieved. For a given TC, the distribution of the resultant of the retrieved force and Coriolis force well matches the corresponding TC track, i.e., when a TC turns, the resultant of the retrieved force and Coriolis force acts as a centripetal force, which means that the TC indeed moves like a particle; in particular, for TC 9911, the clockwise looping motion is also fitted well. And the distribution of the resultant appears to be periodic in some cases. Then, the present method is carried out for a portion of the track data for TC 9804, which indicates that when the amount of data for a TC track is sufficient, the algorithm is stable. And finally, the same algorithm is implemented for TCs with a double-eyewall structure, namely Bilis （2000） and Winnie （1997）, and the results prove the applicability of the algorithm to TCs with complicated mesoscale structures if the TC track data are obtained every three hours.
基金supported by the National Natural Science Foundation of China(Grant No.41375078)the National Basic Research Program of China(Grant Nos.2012CB955902 and 2013CB430204)the Special Scientific Research Fund of Public Welfare Profession of China(Grant No.GYHY201306021)
文摘The cold vortex is a major high impact weather system in northeast China during the warm season, its frequent activities also affect the short-term climate throughout eastern China. How to objectively and quantitatively predict the intensity trend of the cold vortex is an urgent and difficult problem for current short-term climate prediction. Based on the dynamical-statistical combining principle, the predicted results of the Beijing Climate Center＇s global atmosphereocean coupled model and rich historical data are used for dynamic-statistical extra-seasonal prediction testing and actual prediction of the summer 500-hPa geopotential height over the cold vortex activity area. The results show that this method can significantly reduce the model＇s prediction error over the cold vortex activity area, and improve the prediction skills. Furthermore, the results of the sensitivity test reveal that the predicted results are highly dependent on the quantity of similar factors and the number of similar years.
文摘Following Tsai & Ma[1] and Tsai & Liu[2], a statistical and dynamical near-wall turbulent coherent structural model with separate consideration of two different portions:locally generated and upstream-transported large eddies has been established.With this model, heat transfer in a fully developed open channel in the absence of pressure gradient is numerically simulated. Database of fluctuations of velocity and temperature has also been set. Numerical analysis shows the existence of high-low temperature streak caused by near-wall coherent structure and its swing in the lateral direction.Numerical results are in accordance with the computations and experimental results of other researchers.
文摘The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is no simple way to reconcile them. So, simple climate models, like statistical-dynamical models (SDMs), appear to be useful in this context. This kind of models is essentially mechanistic, being directed towards understanding the dependence of a particular mechanism on the other parameters of the problem. In this paper, the utility of SDMs for studies of climate change is discussed in some detail. We show that these models are an indispensable part of hierarchy of climate models.
基金supported by the National Key R&D Program of China(Grant No.2019YFA0606703)the National Natural Science Foundation of China(Grant No.41975116)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202025)。
文摘The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.
基金supported by National Natural Science Foundation of China (Grant No. 51175214)Scientific and Technological Planning Project of China (Grant No. 2011BAG03B01-1)Based Research Operation Expenses Project of Jilin University, China (Grant No. 421032572415)
文摘How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis （SEA） model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics （CFD） and sensitivity analysis of acoustic contribution.
基金This work was partly funded by the National Key R&D Project of China(2021YFB3400704)China State Railway Group(K2022J004 and N2023J011)China Railway Chengdu Group(CJ23018).
文摘Purpose–The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system.This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice.The key principles and approacheswill be proposed,and their applications to high-speed trains in Chinawill be presented.Design/methodology/approach–First,the structural integrity and dynamical integrity of high-speed trains are defined,and their relationship is introduced.Then,the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided.Finally,the principles and approaches for assessing the dynamical integrity of highspeed trains are presented and a novel operational assessment method is further presented.Findings–Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system.For assessing the structural integrity of structural components,an open-loop analysis considering both normal and abnormal vehicle conditions is needed.For assessing the structural integrity of dynamical components,a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed.The analysis of vehicle system dynamics should follow the principles of complete objects,conditions and indices.Numerical,experimental and operational approaches should be combined to achieve effective assessments.Originality/value–The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects,better lifespan management of train components and better maintenance decision-making for high-speed trains.
基金supported by the National Natural Science Foundation of China(NSFC grants No.12172036,51774018)the Program for Changjiang Scholars and Innovative Research Team in University(PCSIRT,IRT_17R06)+2 种基金the Russian Foundation for Basic Research,Grant Number 20‐55‐53032Russian State Task number 1021052706247‐7‐1.5.4the Government of Perm Krai,research project No.С‐26/628.
文摘Earthquakes triggered by dynamic disturbances have been confirmed by numerous observations and experiments.In the past several decades,earthquake triggering has attracted increasing attention of scholars in relation to exploring the mechanism of earthquake triggering,earthquake prediction,and the desire to use the mechanism of earthquake triggering to reduce,prevent,or trigger earthquakes.Natural earthquakes and large‐scale explosions are the most common sources of dynamic disturbances that trigger earthquakes.In the past several decades,some models have been developed,including static,dynamic,quasi‐static,and other models.Some reviews have been published,but explosiontriggered seismicity was not included.In recent years,some new results on earthquake triggering have emerged.Therefore,this paper presents a new review to reflect the new results and include the content of explosion‐triggered earthquakes for the reference of scholars in this area.Instead of a complete review of the relevant literature,this paper primarily focuses on the main aspects of dynamic earthquake triggering on a tectonic scale and makes some suggestions on issues that need to be resolved in this area in the future.
基金supported jointly by the National Natural Science Foundation of China (Grant No.42075170)the National Key Research and Development Program of China (2022YFF0802503)+2 种基金the Jiangsu Collaborative Innovation Center for Climate Changea Chinese University Direct Grant(Grant No. 4053331)supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulator Facility”(EarthLab)
文摘In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
基金Supported by the National Basic Research Program of China (973 Program) (No. 2010CB428703)Oceanic Science Fund for Young Scholar of SOA (Nos. 2010225, 2010118)+1 种基金Public Science and Technology Research Funds Projects of Ocean of China (Nos. 201005008, 201005009)Open Fund of MOIDAT (No. 201011)
文摘Models of marine ecosystem dynamics play an important role in revealing the evolution mechanisms of marine ecosystems and in forecasting their future changes. Most traditional ecological dynamics models are established based on basic physical and biological laws, and have obvious dynamic characteristics and ecological significance. However, they are not flexible enough for the variability of environment conditions and ecological processes found in offshore marine areas, where it is often difficult to obtain parameters for the model, and the precision of the model is often low. In this paper, a new modeling method is introduced, which aims to establish an evolution model of marine ecosystems by coupling statistics with differential dynamics. Firstly, we outline the basic concept and method of inverse modeling of marine ecosystems. Then we set up a statistical dynamics model of marine ecosystems evolution according to annual ecological observation data from Jiaozhou Bay. This was done under the forcing conditions of sea surface temperature and surface irradiance and considering the state variables of phytoplankton, zooplankton and nutrients. This model is dynamic, makes the best of field observation data, and the average predicted precision can reach 90% or higher. A simpler model can be easily obtained through eliminating the terms with smaller contributions according to the weight coefficients of model differential items. The method proposed in this paper avoids the difficulties of obtaining and optimizing parameters, which exist in traditional research, and it provides a new path for research of marine ecological dynamics.
文摘Canopy interception of incident precipitation, as a critical component of a forest＇s water budget, can affect the amount of water available to the soil, and ultimately vegetation distribution and function. In this paper, a statistical-dynamic approach based on leaf area index and statistical canopy interception is used to parameterize the canopy interception process. The statistical-dynamic canopy interception scheme is implemented into the Community Land Model with dynamic global vegetation model （CLM-DGVM） to improve its dynamic vegetation simulation. The simulation for continental China by the land surface model with the new canopy interception scheme shows that the new one reasonably represents the precipitation intercepted by the canopy. Moreover, the new scheme enhances the water availability in the root zone for vegetation growth, especially in the densely vegetated and semi-arid areas, and improves the model＇s performance of potential vegetation simulation.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.72071153 and 72231008)Laboratory of Science and Technology on Integrated Logistics Support Foundation (Grant No.6142003190102)the Natural Science Foundation of Shannxi Province (Grant No.2020JM486)。
文摘Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.
基金supported by Northern Border University,Arar,KSA,through the Project Number“NBU-FFR-2024-2248-02”.
文摘This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and Friedman.It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns.The study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios.The findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements.This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.
基金Supported by Earmarked Fund for Modern Agro-industry Technology Research System of China(CARS-20-2-2)Earmarked Fund for Modern Agro-industry Technology Research System of Yunnan Province
文摘Dead heart of sugarcane is an important symptom caused by borer attack. In the present study, the spatial distribution and dynamics of dead heart of sugarcane in the field were investigated based on geostatistical analysis, and semivariograms were computed in four separate directions(0°, 45°, 90° and 135°) and fitted with various theoretical models to determine the best fitted one. The Ordinary Kriging was used to interpolate spatial data. The results revealed that the density of dead hearts of sugarcane increased in a single-peak pattern, and the degree of spatial aggregation and random variation both decreased with the increase in the density of dead heart. In addition, dead heart of sugarcane caused by borer exhibited spatial aggregation.With the increase in the density of dead heart, the degree of spatial aggregation decreased, while the correlation increased. Kriging interpolation indicated that the correlation between the spatial patches was weak in early seedling stage, and became strong in middle and late seedling stage.
