Element: Software: Data-Driven Auto-Adaptive Classification of Cryospheric Signatures as Informants for Ice-Dynamic Models

该项目的目标是建立地球观测数据与地球系统过程数字模型之间的联系并使之自动化。近年来，从卫星收集地球观测数据和模拟物理过程都取得了前所未有的进展。然而，数据衍生的信息并没有用于以系统和自动化的方式通知建模。这造成了一个瓶颈，随着数据革命而增长。该奖项支持软件网络基础设施的开发，旨在通过自动化分类和参数化来减少这一瓶颈。拟议的网络基础设施将以通用和可移动的方式实施，但其功能将通过解决冰川学中的一个具体开放问题来证明：冰川激增期间的加速度，其特征是流量增加到正常速度的100-200倍。冰川加速是重要的，因为它们构成了海平面上升评估中最大的不确定性。来自科罗拉多大学的团队将联合收割机结合他们的实地工作和数据收集的专业知识与他们在软件开发方面的背景，以生成一个高质量的应用程序，该应用程序将在开源许可证下提供给更广泛的科学界。该项目将使研究生和本科生参与软件开发，从而为未来几代科学家和网络基础设施专业人员的发展做出贡献。 预计拟议的数据驱动的自适应分类系统将为地球科学界提供一种工具，使其能够利用卫星图像和合成孔径雷达数据（WordView、Sentinel-1和Sentinel-2）中前所未有的细节，这些细节是提取以前无法识别的表面特性和过程信息所必需的。由于分类将自动适应时间和空间条件的变化，它将为空间过程提供一致的参数化，可用于推动地球系统过程的数值模拟。因此，将在数据分析和建模之间建立直接联系。在试点研究中，数据建模的连接将通过分类的裂缝模式，这是由于变形和优化的基础滑动参数在一个三维模型的冰川激增。因此，试点研究将促进对冰动力学的了解。第二个应用是海冰分类系统，旨在帮助绘制和了解北极海冰覆盖面积的变化。数据驱动的自动分类和模型参数优化之间的计划自动连接预计将为地球科学、大气和极地科学的数据建模世界的转型奠定基础。该项目还将通过实现计算机科学和网络基础设施的科学驱动方法来推进机器学习和空间统计。NSF高级网络基础设施办公室（OAC）的这一奖项得到了NSF地球科学理事会内的交叉项目的共同支持，OAC Cyberinfrastructure for Emerging Science and Engineering Research（CESER）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this project is to develop and automatize a connection between Earth observation data and numerical models of Earth system processes. Both collection of Earth observation data from satellites and modeling of physical processes have seen unprecedented advances in recent years. However, data-derived information is not used to inform modeling in a systematic and automated fashion. This creates a bottleneck that is growing with the data revolution. The award supports the development of a software cyberinfrastructure aimed at reducing this bottleneck by automating classification and parameterization. The proposed cyberinfrastructure will be implemented in a general and transportable way, but its functionality will be demonstrated by addressing a concrete open problem in glaciology: the acceleration during a glacier surge, which is characterized by an increase to 100-200 times the flow normal velocity. Glacial accelerations are important, because they constitute the largest uncertainty in sea-level-rise assessment. The team, from University of Colorado will combine their expertise of field work and data collection with their background in software development to generate a high-quality application that will be made available under an open source license to the broader scientific community. The project will engage graduate and undergraduate students in the software development, thus contributing to the development of future generations of scientists and cyberinfrastructure professionals. The results will also be used to inform activities in K-12 schools and other outreach efforts.The proposed data-driven auto-adaptive classification system is expected to provide a tool to the Earth Sciences community that allows it to employ the unprecedented detail in satellite image and SAR data (WordView, Sentinel-1 and Sentinel-2) necessary to extract information on surface properties and processes that were previously indiscernible. In that the classification will automatically adapt to changing conditions in time and space, it will provide a consistent parameterization of spatial processes that can be used to drive numerical simulations of Earth system processes. Thus, a direct connection will be established between data analysis and modeling. In a pilot study, the data-modeling connection will be demonstrated through classification of crevasse patterns, which result from deformation, and optimization of the basal sliding parameter in a three-dimensional model of a glacier surge. Hence the pilot study will advance understanding of ice dynamics. A second application is a sea-ice classification system, aimed to aid in mapping and understanding the changing Arctic sea-ice cover. The planned automated connection between data-driven automated classification and optimization of model parameters is expected to lay the foundation for a transformation of the data-modeling world in Earth sciences, atmospheric and polar sciences. The project will also advance machine learning and spatial statistics through realization of a science-driven approach to computer science and cyberinfrastructure.This award by the NSF Office of Advanced Cyberinfrastructure (OAC) is jointly supported by the Cross-Cutting Program within the NSF Directorate for Geosciences, The OAC Cyberinfrastructure for Emerging Science and Engineering Research (CESER) program and the EarthCube Program jointly sponsored by the NSF Directorate for Geosciences and the OAC.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.