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: 2136940
• 负责人: Dava Newman
• 金额: $ 8.2万
• 依托单位: Massachusetts Institute of Technology
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136940&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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助（公共法第117-2章）。策略变化正在促进格陵兰和南极洲的融化增加，导致全球海平面上升。了解是什么推动了冰盖的增加和融水的数量，这对于提高我们的技能来投射未来的海平面上升和相关后果至关重要。南极融化主要发生在冰架上（冰的舌头漂浮在水中）。当它们融化时，它们不会直接促进海平面，但是它们的消失使冰川在顶部的速度更快地流向海洋，从而增加了南极对海平面上升的贡献。卫星数据只能为正在发生的事情提供部分视图，要么是因为覆盖范围有限，要么是由于云的存在，这通常会妨碍世界的这一地区的视野。另一方面，模型可以提供估计值，但是它们可以提供的空间细节仍然受许多因素的限制。该项目将使用人工智能克服这些问题，并将卫星数据和模型输出合并，以生成每日的表面熔化图，并以前所未有的细节生成表面熔化。这些技术类似于手机中用于增强图像或创建看起来“真实”但仅存在于“计算机世界”中的景观的技术，但是它们从未应用于南极洲的融化以改善海平面上升的估计。已经显示，南极洲的融水会通过破裂和柔性过程影响冰架的稳定性。图像稀缺经常迫使社区使用一般气候和区域气候模型来探索液压特征。尽管模型在过去几年中经过精心完善，但它们仍然需要改进，以捕获推动能量平衡的过程，最重要的是，驾驶员之间的反馈以及驱动氢气过程的能量平衡项。此外，空间分辨率仍然太粗糙，无法正确捕获水文过程，尤其是在冰架上。机器学习（ML）工具可以在这方面有所帮助，尤其是在计算上不可行的情况下，在时空的所需分辨率上运行基于物理的模型，例如冰架表面水文学。该项目将使用区域气候模型和遥感数据的输出来训练生成的对抗网络（GAN），以生成表面熔化的前所未有的高分辨率（100 m）地图。除了改善空间解决方案外，还为极地社区提供了长期以来的至关重要的数据集，此处提议的工具还可以每天提供类似卫星的地图，因此还解决了与缺乏空间报道有关的问题，这些奖项反映了NSF的法定任务和众多的支持，这是通过评估的范围来进行的，这是众多的支持者的支持。