The detailed surface rainfall processes associated with landfalling typhoon Kaemi（2006） are investigated based on hourly data from a two-dimensional cloud-resolving model simulation. The model is integrated for 6 da...The detailed surface rainfall processes associated with landfalling typhoon Kaemi（2006） are investigated based on hourly data from a two-dimensional cloud-resolving model simulation. The model is integrated for 6 days with imposed large-scale vertical velocity, zonal wind, horizontal temperature and vapor advection from National Center for Environmental Prediction （NCEP） / Global Data Assimilation System （GDAS） data. The simulation data are validated with observations in terms of surface rain rate. The Root-Mean-Squared （RMS） difference in surface rain rate between the simulation and the gauge observations is 0.660 mm h^-1, which is smaller than the standard deviations of both the simulated rain rate （0.753 mm h^-1） and the observed rain rate （0.833 mm h^-1）. The simulation data are then used to study the physical causes associated with the detailed surface rainfall processes during the landfall. The results show that time averaged and model domain-mean Ps mainly comes from large-scale convergence （QWVF） and local vapor loss （positive QWVT）. Large underestimation （about 15%） of Ps will occur if QWVT and QCM （cloud source/sink） are not considered as contributors to Ps ,QWVF accounts for the variation of P during most of the integration time, while it is not always a contributor to Ps,Sometimes surface rainfall could occur when divergence is dominant with local vapor loss to be a contributor to Ps - Surface rainfall is a result ofmulti-timescale interactions. QWVE possesses the longest time scale and the lowest frequeney the second and QCM of variation with time and may exert impact on P on longer time scales. QWVF possesses longest time scale and lowest frequency and can explain most of the variation of Ps. QWVT possess shorter time scales and higher frequencies, which can explain more detailed variations in Ps. Partitioning analysis shows that stratiform rainfall is dominant from the morning of 26 July till the late night of 27 July. After that, convective rainfall dominates till about 1000 LST 28 July. Before 28 July, the variations of QWVT in rainfall-free regions contribute less to that of the domain-mean QWVT while after that they contribute much, which is consistent to the corresponding variations in their fractional coverage. The variations of QWVF in rainfall regions are the main contributors to that of the domain-mean QWVF, then the main contributors to the surface rain rate before the afternoon of 28 July.展开更多
Convective processes affect large-scale environments through cloud-radiation interaction, cloud micro- physical processes, and surface rainfall processes. Over the last three decades, cloud-resolving models （CRMs） h...Convective processes affect large-scale environments through cloud-radiation interaction, cloud micro- physical processes, and surface rainfall processes. Over the last three decades, cloud-resolving models （CRMs） have demonstrated to be capable of simulating convective-radiative responses to an imposed large-scale forcing. The CRM-produced cloud and radiative properties have been utilized to study the convective- related processes and their ensemble effects on large-scale circulations. This review the recent progress on the understanding of convective processes with the use of CRM simulations, including precipitation processes; cloud microphysical and radiative processes; dynamical processes; precipitation efficiency; diurnal variations of tropical oceanic convection; local-scale atmosphere-ocean coupling processes; and tropical convective-radiative equilibrium states. Two different ongoing applications of CRMs to general circulation models （GCMs） are discussed： replacing convection and cloud schemes for studying the interaction between cloud systems and large-scale circulation, and improving the schemes for climate simulations.展开更多
Water vapor, cloud, and surface rainfall budgets associated with the landfall of Typhoon Krosa on 6-8 October 2007 are analyzed based on a two-dimensional cloud-resolving model simulation. The model is integrated with...Water vapor, cloud, and surface rainfall budgets associated with the landfall of Typhoon Krosa on 6-8 October 2007 are analyzed based on a two-dimensional cloud-resolving model simulation. The model is integrated with imposed zonally-uniform vertical velocity, zonal wind, horizontal temperature, and vapor advection from NCEP/Global Data Assimilation System （GDAS） data. The simulation data that are validated with observations are examined to study physical causes associated with surface rainfall processes during the landfall. The time- and domain-mean analysis shows that when Krosa approached the eastern coast of China on 6 October, the water vapor convergence over land caused a local atmospheric moistening and a net condensation that further produced surface rainfall and an increase of cloud hydrometeor concentration. Meanwhile, latent heating was balanced by advective cooling and a local atmospheric warming. One day later, the enhancement of net condensation led to an increase of surface rainfall and a local atmospheric drying, while the water vapor convergence weakened as a result of the landfall-induced deprivation of water vapor flux. At the same time, the latent heating is mainly compensated the advective cooling. Further weakening of vapor convergence on 8 October enhanced the local atmospheric drying while the net condensation and associated surface rainfall was maintained. The latent heating is balanced by advective cooling and a local atmospheric cooling.展开更多
Impacts of initial conditions on cloud-resolving model simulations are investigated using a series of sensitivity experiments. Five experiments with perturbed initial temperature, moisture, and cloud conditions are co...Impacts of initial conditions on cloud-resolving model simulations are investigated using a series of sensitivity experiments. Five experiments with perturbed initial temperature, moisture, and cloud conditions are conducted and compared to the control experiment. The model is forced by the large-scale vertical velocity and zonal wind observed and derived from NCEP/Global Data Assimilation System （GDAS）. The results indicate that model predictions of rainfall are much more sensitive to the initial conditions than those of temperature and moisture. Further analyses of the surface rainfall equation and the moisture and cloud hydrometeor budgets reveal that the calculations of vapor condensation and deposition rates in the model account for the large sensitivities in rainfall simulations.展开更多
ABSTRACT Rainfall responses to doubled atmospheric carbon dioxide concentration were investigated through the analysis of two pairs of two-dimensional cloud-resolving model sensitivity experiments. One pair of experi...ABSTRACT Rainfall responses to doubled atmospheric carbon dioxide concentration were investigated through the analysis of two pairs of two-dimensional cloud-resolving model sensitivity experiments. One pair of experiments simulated pre-summer heavy rainfall over southern China around the summer solstice, whereas the other pair of experiments simulated tropical rainfall around the winter solstice. The analysis of the time and model domain mean heat budget revealed that the enhanced local atmospheric warming was associated with doubled carbon dioxide through the weakened infrared radiative cooling during the summer solstice. The weakened mean pre-summer rainfall corresponded to the weakened mean infrared radiative cooling. Doubled carbon dioxide increased the mean tropical atmospheric warming via the enhanced mean latent heat in correspondence with the strengthened mean infrared radiative cooling during the winter solstice. The enhanced mean tropical rainfall was associated with the increased mean latent heat.展开更多
This study investigates the effects of vertical wind shear on the torrential rainfall response to the large-scale forcing using a rainfall separation analysis of a pair of two-dimensional cloud-resolving model sensiti...This study investigates the effects of vertical wind shear on the torrential rainfall response to the large-scale forcing using a rainfall separation analysis of a pair of two-dimensional cloud-resolving model sensitivity experiments for a pre-summer heavy rainfall event over southern China from 3-8 June 2008 coupled with National Centers for Environmental Prediction(NCEP)/Global Data Assimilation System(GDAS) data.The rainfall partitioning analysis based on the surface rainfall budget indicates that the exclusion of vertical wind shear decreases the contribution to total rainfall from the largest contributor,which is the rainfall associated with local atmospheric drying,water vapor divergence,and hydrometeor loss/convergence,through the reduction of the rainfall area and reduced rainfall during the rainfall event.The removal of vertical wind shear increases the contribution to total rainfall from the rainfall associated with local atmospheric drying,water vapor convergence,and hydrometeor loss/convergence through the expansion of the rainfall area and enhanced rainfall.The elimination of vertical wind shear enhances heavy rainfall and expands its area,whereas it reduces moderate rainfall and its area.展开更多
The decorrelation length（Lcf） has been widely used to describe the behavior of vertical overlap of clouds in general circulation models（GCMs）; however, it has been a challenge to associate Lcf with the large-scale...The decorrelation length（Lcf） has been widely used to describe the behavior of vertical overlap of clouds in general circulation models（GCMs）; however, it has been a challenge to associate Lcf with the large-scale meteorological conditions during cloud evolution. This study explored the relationship between Lcf and the strength of atmospheric convection in the tropics based on output from a global cloud-resolving model. Lcf tends to increase with vertical velocity in the mid-troposphere（w500） at locations of ascent, but shows little or no dependency on w500 at locations of descent. A representation of Lcf as a function of vertical velocity is obtained, with a linear regression in ascending regions and a constant value in descending regions. This simple and dynamic-related representation of Lcf leads to a significant improvement in simulation of both cloud cover and radiation fields compared with traditional overlap treatments. This work presents a physically justifiable approach to depicting cloud overlap in the tropics in GCMs.展开更多
Recent decades have witnessed the rapid development of cloud-system resolving models （CRM）, which are now capable of simulating cloud systems and accompanying interactions on scales up to global, albeit in the latte...Recent decades have witnessed the rapid development of cloud-system resolving models （CRM）, which are now capable of simulating cloud systems and accompanying interactions on scales up to global, albeit in the latter application small- scale convection （cumulus） remains unresolved. The implication of such a truncation is not understood. The CRM approach has its roots in non-hydrostatic cloud models developed a generation ago for simulating individual cumulonimbus in integrations lasting about an hour Advances in computer capacity enable CRMs to be run with progressively larger computational domains and be integrated for weeks or months,展开更多
Simulated regional precipitation, especially extreme precipitation events, and the regional hydrologic budgets over the western North Pacific region during the period from May to June 2008 were investigated with the h...Simulated regional precipitation, especially extreme precipitation events, and the regional hydrologic budgets over the western North Pacific region during the period from May to June 2008 were investigated with the high-resolution （4-km grid spacing） Weather Research and Forecast （WRF v3.2.1） model with explicit cloud microphysics. The model initial and boundary conditions were derived from the National Centers for Environmental Prediction/Department of Energy （NCEP/DOE） Reanalysis 2 data. The model precipitation results were evaluated against the Tropical Rainfall Measuring Mission （TRMM） Multisatellite Precipitation Analysis 3B42 product. The results show that the WRF simulations can reason- ably reproduce the spatial distributions of daily mean precipitation and rainy days. However, the simulated frequency distributions of rainy days showed an overestimation of light precipitation, an underestimation of moderate to heavy precipitation, but a good representation of extreme precipitation. The downscaling approach was able to add value to the very heavy precipitation over the ocean since the convective processes are resolved by the high-resolution cloud-resolving model. Moreover, the water vapor budget analysis indi- cates that heavy precipitation is contributed mostly by the stronger moisture convergence; whereas, in less convective periods, the precipitation is more influenced by the surface evaporation. The simulated water vapor budgets imply the importance in the tropical monsoon region of cloud microphysics that affects the precipitation, atmospheric latent heating and, subsequently, the large-scale circulation.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
基金National Basic Research Program of China (973 Program) (2009CB421505)National Natural Science Foundation of China (40775036)Knowledge Innovation Program of Chinese Academy of Sciences (IAP07214)
文摘The detailed surface rainfall processes associated with landfalling typhoon Kaemi（2006） are investigated based on hourly data from a two-dimensional cloud-resolving model simulation. The model is integrated for 6 days with imposed large-scale vertical velocity, zonal wind, horizontal temperature and vapor advection from National Center for Environmental Prediction （NCEP） / Global Data Assimilation System （GDAS） data. The simulation data are validated with observations in terms of surface rain rate. The Root-Mean-Squared （RMS） difference in surface rain rate between the simulation and the gauge observations is 0.660 mm h^-1, which is smaller than the standard deviations of both the simulated rain rate （0.753 mm h^-1） and the observed rain rate （0.833 mm h^-1）. The simulation data are then used to study the physical causes associated with the detailed surface rainfall processes during the landfall. The results show that time averaged and model domain-mean Ps mainly comes from large-scale convergence （QWVF） and local vapor loss （positive QWVT）. Large underestimation （about 15%） of Ps will occur if QWVT and QCM （cloud source/sink） are not considered as contributors to Ps ,QWVF accounts for the variation of P during most of the integration time, while it is not always a contributor to Ps,Sometimes surface rainfall could occur when divergence is dominant with local vapor loss to be a contributor to Ps - Surface rainfall is a result ofmulti-timescale interactions. QWVE possesses the longest time scale and the lowest frequeney the second and QCM of variation with time and may exert impact on P on longer time scales. QWVF possesses longest time scale and lowest frequency and can explain most of the variation of Ps. QWVT possess shorter time scales and higher frequencies, which can explain more detailed variations in Ps. Partitioning analysis shows that stratiform rainfall is dominant from the morning of 26 July till the late night of 27 July. After that, convective rainfall dominates till about 1000 LST 28 July. Before 28 July, the variations of QWVT in rainfall-free regions contribute less to that of the domain-mean QWVT while after that they contribute much, which is consistent to the corresponding variations in their fractional coverage. The variations of QWVF in rainfall regions are the main contributors to that of the domain-mean QWVF, then the main contributors to the surface rain rate before the afternoon of 28 July.
文摘Convective processes affect large-scale environments through cloud-radiation interaction, cloud micro- physical processes, and surface rainfall processes. Over the last three decades, cloud-resolving models （CRMs） have demonstrated to be capable of simulating convective-radiative responses to an imposed large-scale forcing. The CRM-produced cloud and radiative properties have been utilized to study the convective- related processes and their ensemble effects on large-scale circulations. This review the recent progress on the understanding of convective processes with the use of CRM simulations, including precipitation processes; cloud microphysical and radiative processes; dynamical processes; precipitation efficiency; diurnal variations of tropical oceanic convection; local-scale atmosphere-ocean coupling processes; and tropical convective-radiative equilibrium states. Two different ongoing applications of CRMs to general circulation models （GCMs） are discussed： replacing convection and cloud schemes for studying the interaction between cloud systems and large-scale circulation, and improving the schemes for climate simulations.
基金supported by the National Natural Science Foundation of China(Grants Nos.40875025,40875030,and 40775033)the Shanghai Natural Science Foundation of China(Grant No.08ZR1422900)
文摘Water vapor, cloud, and surface rainfall budgets associated with the landfall of Typhoon Krosa on 6-8 October 2007 are analyzed based on a two-dimensional cloud-resolving model simulation. The model is integrated with imposed zonally-uniform vertical velocity, zonal wind, horizontal temperature, and vapor advection from NCEP/Global Data Assimilation System （GDAS） data. The simulation data that are validated with observations are examined to study physical causes associated with surface rainfall processes during the landfall. The time- and domain-mean analysis shows that when Krosa approached the eastern coast of China on 6 October, the water vapor convergence over land caused a local atmospheric moistening and a net condensation that further produced surface rainfall and an increase of cloud hydrometeor concentration. Meanwhile, latent heating was balanced by advective cooling and a local atmospheric warming. One day later, the enhancement of net condensation led to an increase of surface rainfall and a local atmospheric drying, while the water vapor convergence weakened as a result of the landfall-induced deprivation of water vapor flux. At the same time, the latent heating is mainly compensated the advective cooling. Further weakening of vapor convergence on 8 October enhanced the local atmospheric drying while the net condensation and associated surface rainfall was maintained. The latent heating is balanced by advective cooling and a local atmospheric cooling.
基金the National Key BasicResearch and Development Project of China under GrantNo. 2004CB418301the National Natural Sciences Foun-dation of China under Grant No. 40775031"Outstand-ing Oversea Scholars" Project No.2005-2-16.
文摘Impacts of initial conditions on cloud-resolving model simulations are investigated using a series of sensitivity experiments. Five experiments with perturbed initial temperature, moisture, and cloud conditions are conducted and compared to the control experiment. The model is forced by the large-scale vertical velocity and zonal wind observed and derived from NCEP/Global Data Assimilation System （GDAS）. The results indicate that model predictions of rainfall are much more sensitive to the initial conditions than those of temperature and moisture. Further analyses of the surface rainfall equation and the moisture and cloud hydrometeor budgets reveal that the calculations of vapor condensation and deposition rates in the model account for the large sensitivities in rainfall simulations.
基金supported by 985 Program of Zhejiang University under Grant No.188020+193432602/215National Natural Science Foundation of China (Grant No.41175047)+3 种基金the R&D Special Fund for Public Welfare Industry by the Ministry of Finance and the Ministry of Science and Technology (Grant Nos.GYHY201006014 and 20100503310)the Basic Research Project of the State Key Laboratory of Severe Weather (12011LAS-B14)supported by the National Key Basic Research and Development Project of China under Grant Nos.2013CB430103 and 2011CB403405the National Natural Science Foundation of China under Grant Nos.41375058 and 41175065
文摘ABSTRACT Rainfall responses to doubled atmospheric carbon dioxide concentration were investigated through the analysis of two pairs of two-dimensional cloud-resolving model sensitivity experiments. One pair of experiments simulated pre-summer heavy rainfall over southern China around the summer solstice, whereas the other pair of experiments simulated tropical rainfall around the winter solstice. The analysis of the time and model domain mean heat budget revealed that the enhanced local atmospheric warming was associated with doubled carbon dioxide through the weakened infrared radiative cooling during the summer solstice. The weakened mean pre-summer rainfall corresponded to the weakened mean infrared radiative cooling. Doubled carbon dioxide increased the mean tropical atmospheric warming via the enhanced mean latent heat in correspondence with the strengthened mean infrared radiative cooling during the winter solstice. The enhanced mean tropical rainfall was associated with the increased mean latent heat.
基金supported by the National Key Basic Research and Development Project of China under Grant 2011CB403405the Chinese Special Scientific Research Project for Public Interest under Grant GYHY200806009+1 种基金the National Natural Science Foundation of China under Grants 41075039 and 41175065the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘This study investigates the effects of vertical wind shear on the torrential rainfall response to the large-scale forcing using a rainfall separation analysis of a pair of two-dimensional cloud-resolving model sensitivity experiments for a pre-summer heavy rainfall event over southern China from 3-8 June 2008 coupled with National Centers for Environmental Prediction(NCEP)/Global Data Assimilation System(GDAS) data.The rainfall partitioning analysis based on the surface rainfall budget indicates that the exclusion of vertical wind shear decreases the contribution to total rainfall from the largest contributor,which is the rainfall associated with local atmospheric drying,water vapor divergence,and hydrometeor loss/convergence,through the reduction of the rainfall area and reduced rainfall during the rainfall event.The removal of vertical wind shear increases the contribution to total rainfall from the rainfall associated with local atmospheric drying,water vapor convergence,and hydrometeor loss/convergence through the expansion of the rainfall area and enhanced rainfall.The elimination of vertical wind shear enhances heavy rainfall and expands its area,whereas it reduces moderate rainfall and its area.
基金Supported by the National Key Research and Development Program of China(2017YFA0603502)(Key)National Natural Science Foundation of China(91644211 and 41375080)China Meteorological Administration Special Public Welfare Research Fund(GYHY201406023)
文摘The decorrelation length（Lcf） has been widely used to describe the behavior of vertical overlap of clouds in general circulation models（GCMs）; however, it has been a challenge to associate Lcf with the large-scale meteorological conditions during cloud evolution. This study explored the relationship between Lcf and the strength of atmospheric convection in the tropics based on output from a global cloud-resolving model. Lcf tends to increase with vertical velocity in the mid-troposphere（w500） at locations of ascent, but shows little or no dependency on w500 at locations of descent. A representation of Lcf as a function of vertical velocity is obtained, with a linear regression in ascending regions and a constant value in descending regions. This simple and dynamic-related representation of Lcf leads to a significant improvement in simulation of both cloud cover and radiation fields compared with traditional overlap treatments. This work presents a physically justifiable approach to depicting cloud overlap in the tropics in GCMs.
文摘Recent decades have witnessed the rapid development of cloud-system resolving models （CRM）, which are now capable of simulating cloud systems and accompanying interactions on scales up to global, albeit in the latter application small- scale convection （cumulus） remains unresolved. The implication of such a truncation is not understood. The CRM approach has its roots in non-hydrostatic cloud models developed a generation ago for simulating individual cumulonimbus in integrations lasting about an hour Advances in computer capacity enable CRMs to be run with progressively larger computational domains and be integrated for weeks or months,
基金support by the National Taiwan University and the high performance computer center in the National Central University.W.H.GAO was supported by the National Basic Research Program of China(Grant No.2013CB955804,2011CB403401)2012 National abroad personnel science and technology project.C.-H.SUI was supported by the National Science Council(Grant No.100-2745-M-002-003-ASP)
文摘Simulated regional precipitation, especially extreme precipitation events, and the regional hydrologic budgets over the western North Pacific region during the period from May to June 2008 were investigated with the high-resolution （4-km grid spacing） Weather Research and Forecast （WRF v3.2.1） model with explicit cloud microphysics. The model initial and boundary conditions were derived from the National Centers for Environmental Prediction/Department of Energy （NCEP/DOE） Reanalysis 2 data. The model precipitation results were evaluated against the Tropical Rainfall Measuring Mission （TRMM） Multisatellite Precipitation Analysis 3B42 product. The results show that the WRF simulations can reason- ably reproduce the spatial distributions of daily mean precipitation and rainy days. However, the simulated frequency distributions of rainy days showed an overestimation of light precipitation, an underestimation of moderate to heavy precipitation, but a good representation of extreme precipitation. The downscaling approach was able to add value to the very heavy precipitation over the ocean since the convective processes are resolved by the high-resolution cloud-resolving model. Moreover, the water vapor budget analysis indi- cates that heavy precipitation is contributed mostly by the stronger moisture convergence; whereas, in less convective periods, the precipitation is more influenced by the surface evaporation. The simulated water vapor budgets imply the importance in the tropical monsoon region of cloud microphysics that affects the precipitation, atmospheric latent heating and, subsequently, the large-scale circulation.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
