Elements: Software. icepack: an open-source glacier flow modeling library in Python

该项目支持一个名为“冰包”的软件包的开发，该软件包将能够模拟冰川（如格陵兰岛、南极洲和世界各地的山脉）如何随着周围环境的变化而流动。冰川学家使用软件工具进行模拟，这样他们就可以预测格陵兰岛和南极冰盖未来的大小。有了这些预测，科学家可以为决策者和公众提供未来几十年海平面上升的更好预测。虽然运行模拟的能力对于提高我们对科学的理解至关重要，但这样做需要大量的编程和科学专业知识。这个项目的目标是降低进入门槛。在一位早期职业科学家的带领下，来自华盛顿大学的团队将开发一种工具，使研究人员和学生更容易使用，无论他们是专家还是新手。该软件应用程序将免费提供，并采用开源许可证。Icepack可以估算遥感无法观测到的参数，如基础摩擦或内部流变。冰川学家使用像冰袋这样的模拟工具来(1)探索尚未完全了解的冰盖物理方面，(2)从观测数据中推断，(3)预测冰盖的未来状态，以估计未来的海平面上升。虽然建模是实践冰川学家的重要工具，但它仍然是一项复杂的工作。除了支持开发更多的功能和改进冰包，我们将创建一个广泛的教程材料的研讨会针对研究生和早期职业研究人员如何使用冰包。此外，研究人员将在icepack中实现新的参数估计和不确定性量化算法。这将使研究人员能够利用对冰盖的整个时间序列的观测，而目前的算法在可以使用的数据数量上是有限的，并且可以更好地了解对冰盖当前和未来状态估计的统计传播。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由美国国家科学基金会地球科学理事会的交叉项目和由美国国家科学基金会地球科学理事会和先进网络基础设施办公室联合赞助的EarthCube项目共同支持。该奖项反映了美国国家科学基金会的法定使命，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。

This project supports the development of a software package named "icepack", that will enable simulations of how glaciers, such as those in Greenland, Antarctica, and mountain ranges around the world, will flow in response to the environment around them. Glaciologists use software tools to run simulations so that they can make predictions of how large the Greenland and Antarctic ice sheets will be in the future. With these predictions, scientists can give policy-makers and the public better predictions on the sea level rise in the coming decades. While the ability to run simulations is essential for advancing our understanding of science, doing so requires a significant programming and scientific expertise. The goal of this project is to lower this barrier to entry. Led by an early career scientist, the team, from University of Washington will develop a tool that is easier to use for researchers and students, whether they are experts or novices. The software applications will be freely available and an open source license.icepack allows for estimating parameters, such as a basal friction or internal rheology, that are not observable via remote sensing. Glaciologists use simulation tools like icepack for (1) exploring aspects of the physics of ice sheets that are not completely understood, (2) drawing inferences from observational data, and (3) making predictions of the future state of the ice sheets in order to estimate future sea-level rise. While modeling is an essential tool for practicing glaciologists, it is still a complex endeavor. In addition to supporting development of more features and improvements to icepack, we will create an extensive set of tutorial materials for a workshop aimed at graduate students and early-career researchers on how to use icepack. Additionally, the investigators will implement novel algorithms for parameter estimation and uncertainty quantification in icepack. These will allow the investigators to leverage the entire time series of observations of the ice sheets, while current algorithms are limited in how much data they can use, and to get a better idea of the statistical spread on estimates of the current and future states of the ice sheets.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Program within the NSF Directorate for Geosciences, and the EarthCube Program jointly sponsored by the NSF Directorate for Geosciences and the Office of Advanced Cyberinfrastructure.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.