Collaborative Research: Standards-Based Cyberinfrastructure for Hydrometeorologic Modeling: US-European Research Partnership

该项目是基于标准的水文气象学网络基础设施(SCIHM)，旨在将两个学科--水文学和气象学--联系起来，每个学科都已在各自的学科内建立了复杂的信息基础设施。这种联系将通过欧洲和美国的水文气象学用例来实现，这些用例将在欧洲和美国的网格计算环境中使用联合数据和计算标准来执行。该项目与几个美国和欧洲机构的研究和开发合作伙伴一起，旨在利用基于标准的CI应用于水文气象应用。为此，我们将培育一个统一的基于标准的水文气象基础设施，欧洲和美国的研究人员和学生可以在其中利用可按需扩展的基于云的高性能计算资源和共享数据空间，快速模拟复杂的物理过程，并预测极端天气事件及其水文、环境和社会影响。计算层和存储层将与基于标准的领域数据服务、分析工具和模型无缝集成，使研究人员和从业者能够根据他们感兴趣的领域快速调整预测模型，发现和访问分布式信息源，并参与预测结果的协作分析和解释。该项目将通过CUAHSI和UCAR、这些学科的大学联盟以及欧洲合作伙伴，吸引更广泛的水文和气象研究界的参与。该项目的学术价值在于查明现有社区水文气象预报系统的工作流程中目前存在的不足，并通过战略性的网络基础设施改进来弥补这些不足。目前迫切需要地球系统科学家拥有可访问、可扩展和可扩展的计算系统，以解决广泛的研究和预测问题。该项目将直接将支持基于标准的高级计算和存储资源管理的高性能计算与面向分布式服务的网络基础设施设计联系起来。建立这种能力将能够在模型中更好地描述复杂的地球系统过程，改进模型和数据不确定性的特征，并将极大地促进假设检验，最终导致改进预测。该项目的一个主要目标是通过基于标准的网络基础设施发展，极大地扩大数据获取和计算获取。实现这一目标的更广泛影响是将强大的环境预测工具交到世界各地的研究人员和决策者手中，而这些地方目前根本不存在这种能力。该项目提供的网络基础设施改进将极大地简化水文气象建模过程，并加速地球科学中的云计算。在严格的以标准为基础的环境中发展这一网络基础设施，也将鼓励可直接从中受益的运作中的政府机构采用它。最终，改进的建模能力将通过建立一个更准确的预测系统，捕捉控制水运输的基本物理和生物过程，从而推动改进对洪水和干旱的预测能力。

This project, Standards-based CyberInfrastructure for HydroMeteorology (SCIHM), seeks to link two disciplines--hydrology and meteorology--each of which has a sophisticated CI already developed within their respective disciplines. This linkage will be accomplished with hydrometeorology use cases in Europe and America that will be executed in both the European and American grid computing environments using federated data and computing standards. With research and development partners from several American and European institutions, the project is designed to take advantage of standards-based CI for hydrometeorological applications. In doing so, we will foster a unified standards-based hydrometeorological infrastructure where researchers and students from Europe and the US can rapidly simulate complex physical processes and predict extreme weather events and their hydrological, environmental and societal impacts, taking advantage of scalable on demand high-performance cloud-based computational resources and shared data space. Computational and storage layers will be seamlessly integrated with standards-based domain data services, analysis tools and models, enabling researchers and practitioners to quickly tune predictive models to their areas of interest, discover and access distributed sources of information, and engage in a collaborative analysis and interpretation of prediction results. This project will engage the broader hydrologic and meteorologic research community through CUAHSI and UCAR, the respective university consortia for these disciplines, as well as European partners. The intellectual merit of this project is to identify current shortcomings in the workflow of existing community-based hydrometeorological prediction systems and to remedy those shortcomings through strategic cyberinfrastructure enhancements. There is presently a dire need for earth system scientists to have at their disposal computational systems that are accessible, extensible and scalable for a wide range of research and prediction problems. This project will directly link high-performance computing that supports standards- based management of advanced computing and storage resources, with distributed service-oriented cyberinfrastructure designs. Creating such a capability will permit better depiction of complex earth system processes in models, allow for improved characterization of model and data uncertainties and will greatly facilitate hypothesis testing, ultimately resulting in improved predictions. One principle goal of this project is to vastly expand data access and computational access through standards-based cyberinfrastructure development. The broader impact of meeting this goal is placing powerful environmental prediction tools into the hands of researchers and decision makers around the world, in places where such capabilities simply cannot presently exist. The cyberinfrastructure enhancements delivered by this project will greatly streamline the hydrometeorological modeling process and accelerate cloud computing in the earth sciences. Development of this cyberinfrastructure within a rigorous standards-based environment will also encourage its adoption by operational government agencies which can directly benefit from it. Ultimately, the improved modeling capacity should drive improved predictive capabilities for floods and droughts by fostering a more accurate prediction system that captures the underlying physical and biological processes controlling water transport.