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

该项目名为基于标准的水文气象网络基础设施 (SCIHM)，旨在将水文学和气象学这两个学科联系起来，每个学科都拥有在各自学科内开发的复杂 CI。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。该项目与来自多个美国和欧洲机构的研发合作伙伴合作，旨在利用基于标准的 CI 进行水文气象应用。在此过程中，我们将建立一个基于统一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利用可扩展的按需高性能云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快速调整预测模型以适应他们感兴趣的领域，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吸引更广泛的水文和气象研究界。该项目的智力价值是确定现有基于社区的水文气象预报系统工作流程中的当前缺陷，并通过战略性网络基础设施增强来弥补这些缺陷。目前，地球系统科学家迫切需要拥有可访问、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先进计算和存储资源的标准管理的高性能计算与面向服务的分布式网络基础设施设计联系起来。创建这样的能力将允许在模型中更好地描述复杂的地球系统过程，允许改进模型和数据不确定性的表征，并将极大地促进假设检验，最终改进预测。该项目的主要目标之一是通过基于标准的网络基础设施开发来大幅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，而这些工具目前根本不具备这种能力。该项目提供的网络基础设施增强功能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。在严格的基于标准的环境中开发这种网络基础设施也将鼓励可直接从中受益的政府运营机构采用它。最终，改进的建模能力应通过培育更准确的预测系统来捕获控制水输送的潜在物理和生物过程，从而提高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.