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

这个项目，基于标准的水文气象学网络基础设施（SCIHM），旨在连接两个学科-水文学和气象学-其中每一个都有一个复杂的CI已经在各自的学科。这种联系将通过欧洲和美国的水文气象用例来实现，这些用例将使用联合数据和计算标准在欧洲和美国的网格计算环境中执行。该项目与来自美国和欧洲几个机构的研发合作伙伴一起，旨在利用基于标准的CI进行水文气象应用。在此过程中，我们将建立一个统一的基于标准的水文气象基础设施，来自欧洲和美国的研究人员和学生可以快速模拟复杂的物理过程，预测极端天气事件及其水文，环境和社会影响，利用可扩展的按需高性能基于云的计算资源和共享数据空间。计算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，使研究人员和从业人员能够快速调整预测模型到他们感兴趣的领域，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，这些学科各自的大学联盟以及欧洲合作伙伴参与更广泛的水文和气象研究社区。该项目的智力价值在于查明现有社区水文气象预测系统工作流程中的现有缺陷，并通过战略性网络基础设施改进来弥补这些缺陷。目前，地球系统科学家迫切需要在他们的处置计算系统，可访问的，可扩展的和可扩展的范围广泛的研究和预测问题。该项目将把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分布式面向服务的网络基础设施设计直接联系起来。建立这种能力将能够更好地描述模型中复杂的地球系统过程，改进模型和数据不确定性的特征，并将大大促进假设检验，最终改进预测。该项目的一个主要目标是通过基于标准的网络基础设施开发，极大地扩展数据访问和计算访问。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，而这些能力目前根本不存在。该项目提供的网络基础设施增强将大大简化水文气象建模过程，并加速地球科学中的云计算。在严格的标准环境中开发这种网络基础设施还将鼓励能够直接从中受益的运营政府机构采用这种基础设施。最终，改进的建模能力将通过培养一个更准确的预测系统来提高洪水和干旱的预测能力，该系统将捕获控制水传输的基本物理和生物过程。

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.