为研究人员方便获取气候模拟:IS-ENES Climate4Impact

C. Pagé, W. S. D. Cerff, M. Plieger, A. Spinuso, Xavier Pivan
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引用次数: 2

摘要

更容易获得气候数据对于气候变化影响研究界来说是非常重要的。对于这些用户来说,许多方面都很重要,例如广泛的指导、对数据集的透明访问、按需处理能力(特别是数据缩减)。为实现这一目标,在欧盟资助的IS-ENES项目中开发了climate4impact (http://climate4impact.eu/)门户网站和服务,针对气候变化影响建模者、影响和适应顾问以及使用气候变化数据的其他专家。它通过量身定制的服务为用户提供对气候模式数据的统一访问。climate4impact的主要目标之一是提供可在其他门户中重用的标准化web服务和工具。这些服务包括web处理服务、web覆盖服务和web映射服务。量身定制的门户在使用这些服务时可以针对特定的社区和/或国家/地区。最近,很明显,为了满足用户对按需数据处理和计算的需求,climate4impact平台必须能够使用现有的研究和电子基础设施,以提供可扩展和灵活的服务。在气候科学数据集数据量大量增加的当前背景下,这一点尤其正确。为了方便地容纳异构系统,设想了一种容器化和模块化的方法。最后,在数据处理委托的上下文中,需要一种健壮的元数据、来源和沿袭方法。
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Ease Access to Climate Simulations for Researchers: IS-ENES Climate4Impact
Easier access to climate data is very important for the climate change impact research communities. Many aspects are important for those users, such as extensive guidance, transparent access to datasets, on-demand processing capabilities (notably for data reduction). To fulfill this objective, the climate4impact (http://climate4impact.eu/) web portal and services has been developed in the European Union funded IS-ENES projects, targeting climate change impact modellers, impact and adaptation consultants, as well as other experts using climate change data. It provides to users harmonized access to climate model data through tailored services. One of the main objectives of climate4impact is to provide standardized web services and tools that are reusable in other portals. These services include web processing services, web coverage services and web mapping services. Tailored portals can be targeted to specific communities and/or countries/regions while making use of those services. Recently, it became obvious that to fulfill users' needs regarding on-demand data processing and calculations, the climate4impact platform had to be able to use existing research and e-infrastructures in order to offer scalable and flexible services. This is especially true in the current context of a large increase in the data volumes of climate science datasets. To easily accommodate heterogeneous systems, a containerized and modular approach is envisioned. Finally, in the context of data processing delegation, a robust approach for metadata, provenance and lineage is required.
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