Collaborative Research: EAGER: Generation of High Resolution Surface Melting Maps over Antarctica using Regional Climate Models, Remote Sensing and Machine Learning

合作研究：EAGER：利用区域气候模型、遥感和机器学习生成南极洲高分辨率表面融化地图

• 批准号: 2136938
• 负责人: Marco Tedesco
• 金额: $ 14.45万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136938&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Generation; Resolution

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Climate change is promoting increased melting in Greenland and Antarctica, contributing to the global sea level rise. Understanding what drives the increase and the amount of meltwater from the ice sheets is paramount to improve our skills to project future sea level rise and associated consequences. Melting in Antarctica mostly occurs along ice shelves (tongues of ice floating in the water). They do not contribute directly to sea level when they melt but their disappearance allows the glaciers at the top to flow faster towards the ocean, increasing the contribution of Antarctica to sea level rise. Satellite data can only offer a partial view of what is happening, either because of limited coverage or because of the presence of clouds, which often obstruct the view in this part of the world. Models, on the other hand, can provide estimates but the spatial detail they can provide is still limited by many factors. This project will use artificial intelligence to overcome these problems and to merge satellite data and model outputs to generate daily maps of surface melting with unprecedented detail. These techniques are similar to those used in cell phones to sharpen images or to create landscapes that look “real” but are only existing in the “computer world,” but they have never been applied to melting in Antarctica for improving estimates of sea level rise. Meltwater in Antarctica has been shown to impact ice shelf stability through the fracturing and flexural processes. Image scarcity has often forced the community to use general climate and regional climate models to explore hydrological features. Notwithstanding models having been considerably refined over the past years, they still require improvements in capturing the processes driving the energy balance and, most importantly, the feedback among the drivers and the energy balance terms that drive the hydrological processes. Moreover, spatial resolution is still too coarse to properly capture hydrological processes, especially over ice shelves. Machine learning (ML) tools can help in this regard, especially when it is computationally infeasible to run physics-based models at desired resolutions in space and time, like in the case of ice shelf surface hydrology. This project will train Generative Adversarial Networks (GANs) with the outputs of a regional climate model and remote sensing data to generate unprecedented, high-resolution (100 m) maps of surface melting. Beside improving the spatial resolution, and hence providing a long-needed and crucial dataset to the polar community, the tool here proposed will be able to provide satellite-like maps on a daily basis, hence addressing also those issues related to the lack of spatial coverage.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。气候变化正在促进格陵兰岛和南极洲的融化，导致全球海平面上升。了解是什么驱动了冰盖的增加和融水的数量，对于提高我们预测未来海平面上升及其相关后果的技能至关重要。南极洲的融化主要发生在沿着冰架（漂浮在水中的冰舌）。它们融化时不会直接导致海平面上升，但它们的消失使顶部的冰川更快地流向海洋，增加了南极洲对海平面上升的贡献。卫星数据只能提供对正在发生的事情的部分看法，因为覆盖范围有限，或者因为云层的存在，云层往往阻碍了世界这一地区的看法。另一方面，模型可以提供估计数，但它们所能提供的空间细节仍然受到许多因素的限制。该项目将使用人工智能来克服这些问题，并将卫星数据和模型输出合并，以生成具有前所未有的细节的地表融化每日地图。这些技术类似于手机中用来锐化图像或创造看起来“真实的”但只存在于“计算机世界”的景观的技术，但它们从未被应用于南极洲的融化，以改善对海平面上升的估计。南极洲的融水已被证明通过断裂和弯曲过程影响冰架的稳定性。图像的缺乏往往迫使社区使用一般气候和区域气候模型来探索水文特征。尽管模型在过去几年中得到了相当大的改进，但在捕获驱动能量平衡的过程方面仍然需要改进，最重要的是，驱动水文过程的驱动因素和能量平衡项之间的反馈。此外，空间分辨率仍然太粗糙，无法正确捕捉水文过程，特别是在冰架上。机器学习（ML）工具可以在这方面提供帮助，特别是当在空间和时间上以所需的分辨率运行基于物理的模型在计算上不可行时，例如冰架表面水文学。该项目将利用区域气候模型和遥感数据的输出来训练生成对抗网络（GAN），以生成前所未有的高分辨率（100米）地表融化地图。除了提高空间分辨率，从而为极地社区提供长期需要的关键数据集外，这里提出的工具将能够每天提供类似卫星的地图，该奖项反映了美国国家科学基金会的法定使命，并被认为是值得通过利用基金会的智力价值和更广泛的影响审查进行评估来支持的的搜索.