Cloud type profoundly affects precipitation,but few studies have explored its impact on precipitation scale height.The authors calculated the ratio of the volume of each cloud type to the total cloud volume and partit...Cloud type profoundly affects precipitation,but few studies have explored its impact on precipitation scale height.The authors calculated the ratio of the volume of each cloud type to the total cloud volume and partitioned the tropical region based on the dominant cloud types.Based on this,tropical regions were categorized into altocumulus control regions,stratocumulus control regions,deep convective cloud control regions,and transition regions.These regions exhibit unique characteristics:high precipitation scale heights and low surface precipitation rates in altocumulus control regions;low precipitation scale heights and low surface precipitation rates in stratocumulus control regions;and moderate precipitation scale heights with high surface precipitation rates in deep convective cloud regions.These features arise from differences in cloud characteristics,precipitation probability,and intensity,influenced by varying water vapor structures.In terms of physical mechanisms,altocumulus,stratocumulus,and deep convective cloud regions are characterized by total dryness,upper-level dryness with lower-level wetness,and total wetness,respectively.Upper-layer dryness leads to low cloud and precipitation structures,reducing the precipitation scale height,while lower-layer dryness increases it.Different humidity conditions in the upper and lower layers lead to variations in cloud type and volume distribution,ultimately affecting precipitation scale heights.This finding aids the mechanistic study of cloud precipitation physics in the tropics,providing valuable insights for developing numerical models and parameterizations.展开更多
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ...Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.展开更多
Cloud-native data warehouses have revolutionized data analysis by enabling elasticity,high availability and lower costs.And the increasing popularity of artificial intelligence(AI)drives data warehouses to provide pre...Cloud-native data warehouses have revolutionized data analysis by enabling elasticity,high availability and lower costs.And the increasing popularity of artificial intelligence(AI)drives data warehouses to provide predictive analytics besides the existing descriptive analytics.Consequently,more vendors start to support training and inference of AI models in data warehouses,exploiting the benefits of near-data processing for fast model development and deployment.However,most of the existing solutions are limited by a complex syntax or slow data transportation across engines.In this paper,we present GaussDB-AISQL,a composable SQL system with AI capabilities.GaussDB-AISQL adopts a composable system design that decouples computing,storage,caching,DB engine and AI engine.Our system offers all the functionality needed by end-to-end model training and inference during the model lifecycle.It also enjoys the simplicity and efficiency by providing a SQL-like syntax and removes the burden of manual model management.When training an AI model,GaussDB-AISQL benefits from highly parallel data transportation by concurrent data pulling from the distributed shared memory.The feature selection algorithms in GaussDB-AISQL make the training more data-efficient.When running model inference,GaussDB-AISQL registers the trained model object in the local data warehouse as a user-defined-function,which avoids moving inference data out of the data warehouse to an external AI engine.Experiments show that GaussDB-AISQL is up to 19×faster than baseline approaches.展开更多
Predictive cruise control(PCC)is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance.With the continuous dev...Predictive cruise control(PCC)is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance.With the continuous development of cloud control platforms(CCPs)and telematics boxes(T-boxes),cloud-based predictive cruise control(CPCC)systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side.In this study,a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box.This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering,enabling rolling optimization of the system and commending parsing at the vehicle end.This approach significantly improves the anti-interference capability and resolution efficiency of the system.On the CCP side,a predictive fuel-saving speed-planning(PFSP)algorithm that considers the throttle input,speed variations,and time efficiency based on the waypoint structure is proposed.It features a forward optimization search without requiring weight adjustments,demonstrating robust applicability to various road conditions and vehicles equiped with constant cruise(CC)system.On the vehicle-side T-box,based on the reference control sequence with the global navigation satellite system position,the recommended speed is analyzed and controlled using the acute angle principle.Through analyzing the differences of the PFSP algorithm compared to dynamic programming(DP)and Model predictive control(MPC)algorithms under uphill and downhill conditions,the results show that the PFSP achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations,demonstrating strong adaptability to the CC system.Finally,by building an experimental platform and running field tests over a total of 2000 km,we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm.The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%-4.39%compared to CC.展开更多
Satellite remote sensing is very important to obtain a variety of cloud properties. However, the data quality from satellites varies with different satellite characteristics. From December 2015 to January 2016, ground...Satellite remote sensing is very important to obtain a variety of cloud properties. However, the data quality from satellites varies with different satellite characteristics. From December 2015 to January 2016, ground-based air quality index （AQI） data showed severe haze events occurred successively in eastern China, particularly in the Beijing-Tianjin-Hebei region. During those days, a red alert （the most serious level）, orange alert （the second-highest level）, and yellow alert （the third-highest level） for haze, were issued in Beijing. Cloud detection from four sensors onboard the ＇A-Train＇satellite constellation were compared for two severe haze episodes, on 21 and 30 December 2015 respectively. Results showed that the MODIS sensor onboard the Aqua satellite misclassified aerosol as cloud, while the other three sensors-AIRS onboard Aqua, the cloud profiling radar onboard CloudSat, and CALIOP onboard CALIPSO-did not observe cloud over the same location. Through the high-AQI haze region in the CALIPSO and CloudSat orbit track, MODIS marked cloud close to the surface, while the MODIS true-color image and CALIOP observed an aerosol layer over the same location, suggesting MODIS falsely observed cloud there. Over the haze region in eastern China, MODIS observed 36% on average greater cloud fraction than AIRS, suggesting haze pollution induces a greater MODIS cloud amount.展开更多
The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parame...The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parameter in the commonly used bulk double-moment schemes, the cloud droplet spectra cannot reasonably be described during the condensation process. Therefore, a newly-developed triple-parameter condensation scheme with the shape parameter diagnosed through the number concentration, cloud water content, and reflectivity factor of cloud droplets can be applied to improve the evolution of the cloud droplet spectrum. The simulation with the new parameterization scheme was compared to those with a high-resolution Lagrangian bin scheme, the double-moment schemes in a parcel model, and the observation in a 1.5D Eulerian model that consists of two cylinders. The new scheme with the shape parameter varying with time and space can accurately simulate the evolution of the cloud droplet spectrum. Furthermore, the volume-mean radius and cloud water content simulated with the new scheme match the Lagrangian analytical solutions well, and the errors are steady, within approximately 0.2%.展开更多
The development and evolution of precipitation microphysical parameters and the vertical structure characteristics associated with Typhoon Yagi(201814)are analyzed in the city of Jinan,Shandong Province based primaril...The development and evolution of precipitation microphysical parameters and the vertical structure characteristics associated with Typhoon Yagi(201814)are analyzed in the city of Jinan,Shandong Province based primarily on the observations of a micro rain radar(MRR),a cloud radar,and a disdrometer.The precipitation process is further subdivided into four types:convective,stratiform,mixed,and light precipitation according to the ground disdrometer data,which is in agreement with the vertical profile of the radar reflectivity detected by the MRR.Vertical winds may be the main source of MRR retrieval error during convective precipitation.Convective precipitation has the shortest duration but makes the largest contribution to the cumulative precipitation.Collision-coalescence is the main microphysical process of stratiform precipitation and light precipitation below the bright band observed by the MRR.It is worth noting that as Typhoon Yagi(201814)transformed into an extratropical cyclone,its raindrop size distributions no longer had the characteristics of maritime precipitation,but become more typical of the characteristic of continental precipitation,which represents a very different raindrop size distribution from that which is normally observed in a landfalling typhoon.展开更多
The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon service...The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.展开更多
In order to balancing based on data achieve dynamic load flow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The appro...In order to balancing based on data achieve dynamic load flow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the OpenFlow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What＇s more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput.展开更多
Cloud structure and evolution of Mesoscale Convective Systems （MCSs） retrieved from the Tropical Rainfall Measuring Mission Microwave Imager （TRMM TMI） and Precipitation Radar （PR） were investigated and compared...Cloud structure and evolution of Mesoscale Convective Systems （MCSs） retrieved from the Tropical Rainfall Measuring Mission Microwave Imager （TRMM TMI） and Precipitation Radar （PR） were investigated and compared with some pioneer studies based on soundings and models over the northern South China Sea （SCS）. The impacts of Convective Available Potential Energy （CAPE） and environmental vertical wind shear on MCSs were also explored. The main features of MCSs over the SCS were captured well by both TRMM PR and TMI. However, the PR-retrieved surface rainfall in May was less than that in June, and the reverse for TMI. TRMM-retrieved rainfall amounts were generally consistent with those estimated from sounding and models. However, rainfall amounts from sounding-based and PR-based estimates were relatively higher than those retrieved from TRMM-TMI data. The Weather Research and Forecasting （WRF） modeling simulation underestimated the maximum rain rate by 22% compared to that derived from TRMM-PR, and underestimated mean rainfall by 10.4% compared to the TRMM-TMI estimate, and by 12.5% compared to the sounding-based estimate. The warm microphysical processes modeled from both the WRF and the Goddard Cumulus Ensemble （GCE） models were quite close to those based on TMI, but the ice water contents in the models were relatively less compared to that derived from TMI. The CAPE and wind shear induced by the monsoon circulation were found to play critical roles in maintaining and developing the intense convective clouds over SCS. The latent heating rate increased more than twofold during the monsoon period and provided favorable conditions for the upward transportation of energy from the ocean, giving rise to the possibility of inducing large-scale interactions.展开更多
In the study of warm clouds,there are many outstanding questions.Cloud droplet size distributions are much wider,and warm rain is initiated in a shorter time and with a shallower cloud depth than theoretical expectati...In the study of warm clouds,there are many outstanding questions.Cloud droplet size distributions are much wider,and warm rain is initiated in a shorter time and with a shallower cloud depth than theoretical expectations.This review summarizes the studies related to the effects of turbulent fluctuations and turbulent entrainment-mixing on the broadening of droplet size distributions and warm rain initiation,including observational,laboratorial,numerical,and theoretical achievements.Particular attention is paid to studies by Chinese scientists since the 1950s,since most results have been published in Chinese.The review reveals that high-resolution observations and simulations,and laboratory experiments,are needed because knowledge of the detailed physical processes involved in the effects of turbulence and entrainment-mixing on cloud microphysics still remains elusive.The effects of turbulent fluctuations and entrainment-mixing processes have been unrealistically separated in most theoretical studies.They could be unified by further advancement of a systems theory into a predictive theory.Developing parameterizations for the effects of fluctuations and entrainment-mixing processes is still in its infancy,and more studies are warranted.展开更多
A 2D axisymmetric bin model is used to conduct idealized numerical experiments of cloud seeding.The simulations are performed for two clouds that differ in their initial wind shear.Results show that,although cloud see...A 2D axisymmetric bin model is used to conduct idealized numerical experiments of cloud seeding.The simulations are performed for two clouds that differ in their initial wind shear.Results show that,although cloud seeding with an ice concentration of 1000 Lin a regime that has relatively high supercooled liquid water can obtain a positive effect,the rainfall enhancement seems more pronounced when the cloud develops in a wind shear environment.In no-shear environment,the change in the microphysical thermodynamic field after seeding shows that,although more graupel is produced via riming and this can increase the surface rainfall intensity,the larger drag force and cooling of melting graupel is unfavorable for the development of cloud.On the contrary,when the cloud develops in a wind shear environment,since the main downdraft is behind the direction of movement of the cloud,its negative effect on precipitation is much weaker.展开更多
Based on cloud-probe data and airborne Ka-band cloud radar data collected in Baoding on 5 August 2018,the microphysical structural characteristics of cumulus(Cu)cloud at the precipitation stage were investigated.The c...Based on cloud-probe data and airborne Ka-band cloud radar data collected in Baoding on 5 August 2018,the microphysical structural characteristics of cumulus(Cu)cloud at the precipitation stage were investigated.The cloud droplets in the Cu cloud were found to be significantly larger than those in stratiform(STF)cloud.In the Cu cloud,most cloud particles were between 7 and 10μm in diameter,while in the STF cloud the majority of cloud particles grew no larger than 2μm.The sensitivity of cloud properties to aerosols varied with height.The cloud droplet effective radius showed a negative relationship with the aerosol number concentration(Na)in the cloud planetary boundary layer(PBL)and upper layer above the PBL.However,the cloud droplet concentration(Nc)varied little with decreased Na in the high liquid water content region above 1500 m.High Na values of between 300 and 1853 cm-3 were found in the PBL,and the maximum Na was sampled near the surface in August in the Hebei region,which was lower than that in autumn and winter.High radar reflectivity corresponded to large FCDP(fast cloud droplet probe)particle concentrations and small aerosol particle concentrations,and vice versa for low radar reflectivity.Strong updrafts in the Cu cloud increased the peak radius and Nc,and broadened cloud droplet spectrum;lower air temperature was favorable for particle condensational growth and produced larger droplets.展开更多
A set of remote sensing instruments of Peking University, which includes mainly a dual-channel(22.235GHz and 35.5GHz) microwave radiometer, a 8mm microwave and a 5mm microwave radiometer, has been developed for the We...A set of remote sensing instruments of Peking University, which includes mainly a dual-channel(22.235GHz and 35.5GHz) microwave radiometer, a 8mm microwave and a 5mm microwave radiometer, has been developed for the Western North-Pacific Cloud-Radiation Experiment (WENPEX). The instruments were used to observe the cloud and marine atmospheric boundary-layer in the southwest sea area of Japan in winter time from 1989 to 1991.In the weather change process, the characteristics of the marine atmospheric boundary-layer and liquid water content in cloud of this area in winter time are studied from observation data. A one-dimensional mixed layer model is presented for the growth and evolution of a cloud-topped marine boundary-layer. The model is used to study in the WENPEX. The simulation results are in agreement with observation data, especially the integral water in cloud.展开更多
Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved c...Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.展开更多
The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the diff...The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 42175099 and 42027804]The appointment of Chunsong Lu at Nanjing University of Information Science&Technology was partially supported by the Jiangsu Specially-Appointed Professor[grant number R2024T01].
文摘Cloud type profoundly affects precipitation,but few studies have explored its impact on precipitation scale height.The authors calculated the ratio of the volume of each cloud type to the total cloud volume and partitioned the tropical region based on the dominant cloud types.Based on this,tropical regions were categorized into altocumulus control regions,stratocumulus control regions,deep convective cloud control regions,and transition regions.These regions exhibit unique characteristics:high precipitation scale heights and low surface precipitation rates in altocumulus control regions;low precipitation scale heights and low surface precipitation rates in stratocumulus control regions;and moderate precipitation scale heights with high surface precipitation rates in deep convective cloud regions.These features arise from differences in cloud characteristics,precipitation probability,and intensity,influenced by varying water vapor structures.In terms of physical mechanisms,altocumulus,stratocumulus,and deep convective cloud regions are characterized by total dryness,upper-level dryness with lower-level wetness,and total wetness,respectively.Upper-layer dryness leads to low cloud and precipitation structures,reducing the precipitation scale height,while lower-layer dryness increases it.Different humidity conditions in the upper and lower layers lead to variations in cloud type and volume distribution,ultimately affecting precipitation scale heights.This finding aids the mechanistic study of cloud precipitation physics in the tropics,providing valuable insights for developing numerical models and parameterizations.
基金funded by Multimedia University(Ref:MMU/RMC/PostDoc/NEW/2024/9804).
文摘Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.
基金supported by the fund for building world-class universities(disciplines)of Renmin University of China.
文摘Cloud-native data warehouses have revolutionized data analysis by enabling elasticity,high availability and lower costs.And the increasing popularity of artificial intelligence(AI)drives data warehouses to provide predictive analytics besides the existing descriptive analytics.Consequently,more vendors start to support training and inference of AI models in data warehouses,exploiting the benefits of near-data processing for fast model development and deployment.However,most of the existing solutions are limited by a complex syntax or slow data transportation across engines.In this paper,we present GaussDB-AISQL,a composable SQL system with AI capabilities.GaussDB-AISQL adopts a composable system design that decouples computing,storage,caching,DB engine and AI engine.Our system offers all the functionality needed by end-to-end model training and inference during the model lifecycle.It also enjoys the simplicity and efficiency by providing a SQL-like syntax and removes the burden of manual model management.When training an AI model,GaussDB-AISQL benefits from highly parallel data transportation by concurrent data pulling from the distributed shared memory.The feature selection algorithms in GaussDB-AISQL make the training more data-efficient.When running model inference,GaussDB-AISQL registers the trained model object in the local data warehouse as a user-defined-function,which avoids moving inference data out of the data warehouse to an external AI engine.Experiments show that GaussDB-AISQL is up to 19×faster than baseline approaches.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB2501000).
文摘Predictive cruise control(PCC)is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance.With the continuous development of cloud control platforms(CCPs)and telematics boxes(T-boxes),cloud-based predictive cruise control(CPCC)systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side.In this study,a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box.This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering,enabling rolling optimization of the system and commending parsing at the vehicle end.This approach significantly improves the anti-interference capability and resolution efficiency of the system.On the CCP side,a predictive fuel-saving speed-planning(PFSP)algorithm that considers the throttle input,speed variations,and time efficiency based on the waypoint structure is proposed.It features a forward optimization search without requiring weight adjustments,demonstrating robust applicability to various road conditions and vehicles equiped with constant cruise(CC)system.On the vehicle-side T-box,based on the reference control sequence with the global navigation satellite system position,the recommended speed is analyzed and controlled using the acute angle principle.Through analyzing the differences of the PFSP algorithm compared to dynamic programming(DP)and Model predictive control(MPC)algorithms under uphill and downhill conditions,the results show that the PFSP achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations,demonstrating strong adaptability to the CC system.Finally,by building an experimental platform and running field tests over a total of 2000 km,we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm.The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%-4.39%compared to CC.
基金supported by the National Natural Science Foundation of China[grant number 41590874]and[grant number41590875]
文摘Satellite remote sensing is very important to obtain a variety of cloud properties. However, the data quality from satellites varies with different satellite characteristics. From December 2015 to January 2016, ground-based air quality index （AQI） data showed severe haze events occurred successively in eastern China, particularly in the Beijing-Tianjin-Hebei region. During those days, a red alert （the most serious level）, orange alert （the second-highest level）, and yellow alert （the third-highest level） for haze, were issued in Beijing. Cloud detection from four sensors onboard the ＇A-Train＇satellite constellation were compared for two severe haze episodes, on 21 and 30 December 2015 respectively. Results showed that the MODIS sensor onboard the Aqua satellite misclassified aerosol as cloud, while the other three sensors-AIRS onboard Aqua, the cloud profiling radar onboard CloudSat, and CALIOP onboard CALIPSO-did not observe cloud over the same location. Through the high-AQI haze region in the CALIPSO and CloudSat orbit track, MODIS marked cloud close to the surface, while the MODIS true-color image and CALIOP observed an aerosol layer over the same location, suggesting MODIS falsely observed cloud there. Over the haze region in eastern China, MODIS observed 36% on average greater cloud fraction than AIRS, suggesting haze pollution induces a greater MODIS cloud amount.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41275147 and 41875173)the STS Program of Inner Mongolia Meteorological Service, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences and Institute of Atmospheric Physics, Chinese Academy of Sciences (Grant No. 2021CG0047)
文摘The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parameter in the commonly used bulk double-moment schemes, the cloud droplet spectra cannot reasonably be described during the condensation process. Therefore, a newly-developed triple-parameter condensation scheme with the shape parameter diagnosed through the number concentration, cloud water content, and reflectivity factor of cloud droplets can be applied to improve the evolution of the cloud droplet spectrum. The simulation with the new parameterization scheme was compared to those with a high-resolution Lagrangian bin scheme, the double-moment schemes in a parcel model, and the observation in a 1.5D Eulerian model that consists of two cylinders. The new scheme with the shape parameter varying with time and space can accurately simulate the evolution of the cloud droplet spectrum. Furthermore, the volume-mean radius and cloud water content simulated with the new scheme match the Lagrangian analytical solutions well, and the errors are steady, within approximately 0.2%.
基金Shandong Provincial Natural Science Foundation(ZR2020MD054)the Key Laboratory for Cloud Physics of the China Meteorological Administration(LCP/CMA,Grant No.2017Z016)+2 种基金the National Key Research and Development Program of China(Grant No.2018YFC1507903)the National Natural Science Foundation of China(Grant No.41475028)the Shandong Meteorological Bureau project(Grant Nos.2020sdqxz08,2020sdqxm10,2018SDQN09,2017sdqxz05)。
文摘The development and evolution of precipitation microphysical parameters and the vertical structure characteristics associated with Typhoon Yagi(201814)are analyzed in the city of Jinan,Shandong Province based primarily on the observations of a micro rain radar(MRR),a cloud radar,and a disdrometer.The precipitation process is further subdivided into four types:convective,stratiform,mixed,and light precipitation according to the ground disdrometer data,which is in agreement with the vertical profile of the radar reflectivity detected by the MRR.Vertical winds may be the main source of MRR retrieval error during convective precipitation.Convective precipitation has the shortest duration but makes the largest contribution to the cumulative precipitation.Collision-coalescence is the main microphysical process of stratiform precipitation and light precipitation below the bright band observed by the MRR.It is worth noting that as Typhoon Yagi(201814)transformed into an extratropical cyclone,its raindrop size distributions no longer had the characteristics of maritime precipitation,but become more typical of the characteristic of continental precipitation,which represents a very different raindrop size distribution from that which is normally observed in a landfalling typhoon.
基金supported by National Natural Science Foundation of China(Nos.61861013,61662018)Science and Technology Major Project of Guangxi(No.AA18118031)+2 种基金Guangxi Natural Science Foundation of China(No.2018 GXNSFAA050028)the Doctoral Research Foundation of Guilin University of Electronic Science and Technology(No.UF19033Y)Director Fund project of Key Laboratory of Cognitive Radio and Information Processing of Ministry of Education(No.CRKL190102)。
文摘The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.
基金supported by the National Natural Science Foundation of China(No.61163058No.61201250 and No.61363006)Guangxi Key Laboratory of Trusted Software(No.KX201306)
文摘In order to balancing based on data achieve dynamic load flow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the OpenFlow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What＇s more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput.
基金sponsored by the Chinese Natural Science Foundation (Grant Nos. 40575003 and 40333033)the special foundation of the Chinese Academy of Meteorological Sciences (2011Z005)
文摘Cloud structure and evolution of Mesoscale Convective Systems （MCSs） retrieved from the Tropical Rainfall Measuring Mission Microwave Imager （TRMM TMI） and Precipitation Radar （PR） were investigated and compared with some pioneer studies based on soundings and models over the northern South China Sea （SCS）. The impacts of Convective Available Potential Energy （CAPE） and environmental vertical wind shear on MCSs were also explored. The main features of MCSs over the SCS were captured well by both TRMM PR and TMI. However, the PR-retrieved surface rainfall in May was less than that in June, and the reverse for TMI. TRMM-retrieved rainfall amounts were generally consistent with those estimated from sounding and models. However, rainfall amounts from sounding-based and PR-based estimates were relatively higher than those retrieved from TRMM-TMI data. The Weather Research and Forecasting （WRF） modeling simulation underestimated the maximum rain rate by 22% compared to that derived from TRMM-PR, and underestimated mean rainfall by 10.4% compared to the TRMM-TMI estimate, and by 12.5% compared to the sounding-based estimate. The warm microphysical processes modeled from both the WRF and the Goddard Cumulus Ensemble （GCE） models were quite close to those based on TMI, but the ice water contents in the models were relatively less compared to that derived from TMI. The CAPE and wind shear induced by the monsoon circulation were found to play critical roles in maintaining and developing the intense convective clouds over SCS. The latent heating rate increased more than twofold during the monsoon period and provided favorable conditions for the upward transportation of energy from the ocean, giving rise to the possibility of inducing large-scale interactions.
基金supported by the National Key Research and Development Program of China[grant number 2017YFA060 4000]the China Meteorological Administration Special Public Welfare Research Fund[grant number GYHY201406001]+5 种基金the National Natural Science Foundation of China(NSFC)[grant number 91537108]the Natural Science Foundation of Jiangsu Province,China[grant number BK20160041]the U.S.Department of Energy’s BER Atmospheric System Research Program[grant number DE-SC00112704]the Six Talent Peak Project in Jiangsu,China[grant number 2015-JY-011]the 333 High-level Talents Training Project in Jiangsu[grant number BRA2016424]the NSFC[grant number 41305120]
文摘In the study of warm clouds,there are many outstanding questions.Cloud droplet size distributions are much wider,and warm rain is initiated in a shorter time and with a shallower cloud depth than theoretical expectations.This review summarizes the studies related to the effects of turbulent fluctuations and turbulent entrainment-mixing on the broadening of droplet size distributions and warm rain initiation,including observational,laboratorial,numerical,and theoretical achievements.Particular attention is paid to studies by Chinese scientists since the 1950s,since most results have been published in Chinese.The review reveals that high-resolution observations and simulations,and laboratory experiments,are needed because knowledge of the detailed physical processes involved in the effects of turbulence and entrainment-mixing on cloud microphysics still remains elusive.The effects of turbulent fluctuations and entrainment-mixing processes have been unrealistically separated in most theoretical studies.They could be unified by further advancement of a systems theory into a predictive theory.Developing parameterizations for the effects of fluctuations and entrainment-mixing processes is still in its infancy,and more studies are warranted.
基金This study was jointly supported by the National Key Research and Development Program of China[grant number 2018YFC1507900]the National Natural Science Foundation of China[grant numbers 41875172 and 42075192].
文摘A 2D axisymmetric bin model is used to conduct idealized numerical experiments of cloud seeding.The simulations are performed for two clouds that differ in their initial wind shear.Results show that,although cloud seeding with an ice concentration of 1000 Lin a regime that has relatively high supercooled liquid water can obtain a positive effect,the rainfall enhancement seems more pronounced when the cloud develops in a wind shear environment.In no-shear environment,the change in the microphysical thermodynamic field after seeding shows that,although more graupel is produced via riming and this can increase the surface rainfall intensity,the larger drag force and cooling of melting graupel is unfavorable for the development of cloud.On the contrary,when the cloud develops in a wind shear environment,since the main downdraft is behind the direction of movement of the cloud,its negative effect on precipitation is much weaker.
基金funded by the National Key Research and Devel-opment Program of China[grant number 2017YFC1501405]the National Natural Science Foundation of China[grant numbers 41975180,41705119,and 41575131]the National Center of Meteorology,Abu Dhabi,AE(UAE Research Program for Rain Enhancement Science)。
文摘Based on cloud-probe data and airborne Ka-band cloud radar data collected in Baoding on 5 August 2018,the microphysical structural characteristics of cumulus(Cu)cloud at the precipitation stage were investigated.The cloud droplets in the Cu cloud were found to be significantly larger than those in stratiform(STF)cloud.In the Cu cloud,most cloud particles were between 7 and 10μm in diameter,while in the STF cloud the majority of cloud particles grew no larger than 2μm.The sensitivity of cloud properties to aerosols varied with height.The cloud droplet effective radius showed a negative relationship with the aerosol number concentration(Na)in the cloud planetary boundary layer(PBL)and upper layer above the PBL.However,the cloud droplet concentration(Nc)varied little with decreased Na in the high liquid water content region above 1500 m.High Na values of between 300 and 1853 cm-3 were found in the PBL,and the maximum Na was sampled near the surface in August in the Hebei region,which was lower than that in autumn and winter.High radar reflectivity corresponded to large FCDP(fast cloud droplet probe)particle concentrations and small aerosol particle concentrations,and vice versa for low radar reflectivity.Strong updrafts in the Cu cloud increased the peak radius and Nc,and broadened cloud droplet spectrum;lower air temperature was favorable for particle condensational growth and produced larger droplets.
文摘A set of remote sensing instruments of Peking University, which includes mainly a dual-channel(22.235GHz and 35.5GHz) microwave radiometer, a 8mm microwave and a 5mm microwave radiometer, has been developed for the Western North-Pacific Cloud-Radiation Experiment (WENPEX). The instruments were used to observe the cloud and marine atmospheric boundary-layer in the southwest sea area of Japan in winter time from 1989 to 1991.In the weather change process, the characteristics of the marine atmospheric boundary-layer and liquid water content in cloud of this area in winter time are studied from observation data. A one-dimensional mixed layer model is presented for the growth and evolution of a cloud-topped marine boundary-layer. The model is used to study in the WENPEX. The simulation results are in agreement with observation data, especially the integral water in cloud.
文摘Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.
基金supported in part by the National Key R&D Program of China under Grant 2017YFB1302400the Jinan“20 New Colleges and Universities”Funded Scientific Research Leader Studio under Grant 2021GXRC079+2 种基金the Major Agricultural Applied Technological Innovation Projects of Shandong Province underGrant SD2019NJ014the Shandong Natural Science Foundation under Grant ZR2019MF064the Beijing Advanced Innovation Center for Intelligent Robots and Systems under Grant 2019IRS19.
文摘The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.
