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Digital meteorological service (DMET) in support of trajectory optimization and ATM automation

机译:支持轨迹优化和ATM自动化的数字气象服务(DMET)

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摘要

An improvement is required in the meteorological support for the future Air Traffic Management (ATM) System. New concepts of weather services must be tailored to enable a safer and more efficient aviation: better accuracy, increased data availability, real time support, digital service and shared information are some of the foundational elements underlying trajectory optimization, automation of operations, and fuel & time cost-reductions. A Digital Meteorological Service (DMET) is cornerstone in a net-centric service-oriented ATM system architecture where available data, air-ground connectivity and modern computational resources are taken advantage of to attain a 4D predictive model specifically designed for real-time support to aircraft operations. The effort presented here consists on the development of a prototype DMET service that computes atmospheric data from several sources to produce predicted 4D atmosphere scenarios regularly available to subscribers. By using many data sources -such as forecasts from global and mesoscale weather models, in-situ observations and the introduction of local airborne parameters, a well-tailored forecast product is developed. It consists of a 4D grid of pressure, temperature and wind data fields that are valid into an airspace cube of about 150×150×20km, within a time interval of 2.5 hours. On top of this model, minimum time, minimum consumption and other interesting weather-based optimization functions are covered, all being processed in parallel for a future migration to a supercomputing centre.
机译:需要对未来的空中交通管理(ATM)系统的气象支持进行改进。必须量身定制气象服务的新概念,以实现更安全,更高效的航空:更高的准确性,更高的数据可用性,实时支持,数字服务和共享信息是轨迹优化,操作自动化以及燃油和燃油成本的一些基本要素。减少时间成本。数字气象服务(DMET)是面向以网络为中心的面向服务的ATM系统架构的基石,在该架构中,利用可用数据,空地连接性和现代计算资源来获得专为实时支持而设计的4D预测模型。飞机运营。此处介绍的工作基于DMET原型服务的开发,该服务可从多个来源计算大气数据,以生成定期提供给订户的预测4D大气情景。通过使用许多数据源,例如来自全球和中尺度天气模型的预报,现场观测以及引入本地机载参数,开发了一种量身定制的预报产品。它由压力,温度和风数据字段的4D网格组成,这些数据字段在2.5小时的时间间隔内有效进入约150×150×20km的空域立方体。在此模型之上,涵盖了最小时间,最小消耗和其他有趣的基于天气的优化功能,所有这些功能均被并行处理,以便将来迁移到超级计算中心。

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