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充分了解和掌握降水天气系统发生发展是实施人工增雨作业的基础,有助于人工增雨作业时机、地点以及增雨方式的选择并有效实施作业。在人工增雨作业过程中根据天气条件选择正确有利的作业时机取得好的人工增雨效果是至关重要的。本文就几种降水天气系统背景下,对如何开展人工增雨作业进行讨论,并提出有效的解决方案。 相似文献
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拉萨地区开展人工增雨作业的有利条件探讨 总被引：1，自引：0，他引：1
本文根据我们两年(2000～2001年)来在拉萨地区开展人工增雨作业试验研究中积累的成功经验,对有利于拉萨地区人工增雨作业开展的天气系统类型、积状云和层状云作业天气条件、增雨作业时机及相关的组织管理措施等进行了较为系统的归纳、总结,以供在拉萨地区开展人工增雨作业时参考. 相似文献
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《内蒙古科技与经济》2021,(16)
基于通辽市2012年-2016年五年飞机人工增雨作业个例数据和气象数据,分析了开展飞机人工增雨作业时天气系统条件、水汽条件、雷达回波结构特点,总结出了适合通辽地区飞机人工增雨作业指标。 相似文献
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本文以拉萨市1970—l999年的地面观测资料及l995—l999年的探空资料，对拉萨市各种降雨云出现伴有降水产生、降水产生伴有降雨出现及不同降水强度下，积雨云伴随出现的云高、云厚、云顶温度等状况进行了较为系统地统计分析，旨在为我市人工增雨理论的深化研究、减少增雨作业盲目性、确定最佳作业时机及提高增雨作业总体效益等提供参考依据。 相似文献
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建立一套适合我国国情和切实可行的人工增雨科学技术体系和人工增雨规范化作业流程,以提高人工增雨实际效益。 相似文献
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经济的发展推动科技的进步,人工影响天气作业越来越多的被应用到气象灾害的防治中,通过合理的气候资源利用,满足地区需求情况。人工影响天气作业的增雨效果能够为有需求地区达到良好的抗旱作用。在贵州省部分地区容易出现干旱灾害,通过人工影响天气作业有效降低灾害损失。本文将就贵州的飞机人工增雨效果进行分析,总结人工增雨作业带来的效益,以及提出为了更好的开展人工影响天气作业技术的对策建议。 相似文献
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增雨防雹火箭作业系统是新一代人工影响天气作业的理想工具,通过对火箭增雨防雹作业相关工作进行系统的梳理,将有效规范火箭增雨防雹作业,从而提高作业效率,保证作业安全,充分达到增雨防雹目的。 相似文献
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《国家科学评论(英文版)》2014,(3)
Network science has atracted much atention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-proile publications, and it seems that since 2010 studies of‘network of networks'(NON), sometimes called multilayer networks or multiplex, have atracted more and more atention. he analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensively in physics and mathematics in the last 50 years is a speciic limit of the rich and very diferent general case of n coupled networks. Since then, properties and dynamics of interdependent and interconnected networks have been studied extensively, and scientists are inding many interesting results and discovering many surprising phenomena. Because most natural and engineered systems are composed of multiple subsystems and layers of connectivity, it is important to consider these features in order to improve our understanding of such complex systems. Now the study of NON has become one of the important directions in network science.In this paper, we review recent studies on the new emerging area—NON. Due to the fast growth of this ield, there are many deinitions of diferent types of NON, such as interdependent networks,interconnected networks, multilayered networks, multiplex networks and many others. here exist many datasets that can be represented as NON, such as network of diferent transportation networks including light networks, railway networks and road networks, network of ecological networks including species interacting networks and food webs, network of biological networks including gene regulation network,metabolic network and protein–protein interacting network, network of social networks and so on. Among them, many interdependent networks including critical infrastructures are embedded in space, introducing spatial constraints. hus, we also review the progress on study of spatially embedded networks. As a result of spatial constraints, such interdependent networks exhibit extreme vulnerabilities compared with their non-embedded counterparts. Such studies help us to understand, realize and hopefully mitigate the increasing risk in NON. 相似文献
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Recently, series elasticity has been realized using pneumatics in human-robot interaction systems. Pneumatic circuits provide not only a flexible power transmission, but also the elastic element in a series elastic actuator (SEA). Pneumatic series elastic systems involve more than twice the number of parameters that influence system behaviors in comparison with rigid robotic systems. In this study, a position controller that eliminates the need of identifying a system model by employing the time delay estimation (TDE) technique is proposed for pneumatic SEA systems. The TDE technique is effective in compensating for system dynamics and all uncertainties involved in system behaviors without imposing computation load. TDE error is cancelled out through a learning way, which improves control performance and leads to asymptotic stability. A simulation study demonstrates the robustness of the proposed controllers against uncertainties imposed on the motor system as well as uncertainties on the end-effector. The simulation shows the efficacy of the learning compensation for TDE error. 相似文献
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《国家科学评论(英文版)》2014,(3)
Network science has atracted much atention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-proile publications, and it seems that since 2010 studies of‘network of networks'(NON), sometimes called multilayer networks or multiplex, have atracted more and more atention. he analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensively in physics and mathematics in the last 50 years is a speciic limit of the rich and very diferent general case of n coupled networks. Since then, properties and dynamics of interdependent and interconnected networks have been studied extensively, and scientists are inding many interesting results and discovering many surprising phenomena. Because most natural and engineered systems are composed of multiple subsystems and layers of connectivity, it is important to consider these features in order to improve our understanding of such complex systems. Now the study of NON has become one of the important directions in network science.In this paper, we review recent studies on the new emerging area—NON. Due to the fast growth of this ield, there are many deinitions of diferent types of NON, such as interdependent networks,interconnected networks, multilayered networks, multiplex networks and many others. here exist many datasets that can be represented as NON, such as network of diferent transportation networks including light networks, railway networks and road networks, network of ecological networks including species interacting networks and food webs, network of biological networks including gene regulation network,metabolic network and protein–protein interacting network, network of social networks and so on. Among them, many interdependent networks including critical infrastructures are embedded in space, introducing spatial constraints. hus, we also review the progress on study of spatially embedded networks. As a result of spatial constraints, such interdependent networks exhibit extreme vulnerabilities compared with their non-embedded counterparts. Such studies help us to understand, realize and hopefully mitigate the increasing risk in NON. 相似文献
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