Using deep learning to map sea level and ocean dynamics along the North Atlantic eastern boundary

利用深度学习绘制北大西洋东部边界的海平面和海洋动态图

• 批准号: 2124663
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2124663
• 关键词: Using; deep; learning; map; sea

Project Background Rising seas due to melting ice sheets and ocean warming pose a major threat to the large fraction of the world's population living in low-lying coastal regions. As the climate warms, large-scale adjustments in the ocean's circulation may also drive abrupt changes in coastal sea level, locally amplifying or ameliorating global mean sea level rise and its impacts. Therefore, while much sea level research has centred around its global mean value, there is a growing realisation the we must better understand the factors, such as the ocean's circulation, that drive regional variations in sea level. For example, ocean models suggest that sea level falls by about 60 cm along the eastern boundary of the North Atlantic between the equator and Northern Europe. However, due to measurement limitations, accurate observations of this change in coastal sea level, or the dynamics by which it is maintain, have yet to be made. Machine learning provides an opportunity for fundamental progress to be made in this exciting field.Project Aims and Methods The ultimate aim of this project is to identify the ocean processes sustaining the large drop in sea level along the eastern boundary of the North Atlantic, thereby explaining a poorly understood feature of the ocean's large-scale circulation with implications for sea level rise. This goal is hampered, however, by three primary difficulties: (i) obtaining a clean sea level signal in coastal zones using satellite altimetry; (ii) removing the much larger gravity signal from the SSH to obtain the small (~1%) residual related to ocean dynamics and (iii) relating the surface signal to sparse sub-surface observations to identify the boundary dynamics. Here we propose to employ machine learning techniques to overcome these difficulties. Correspondingly, the project will procced in three stages: 1. Use deep learning and image classification to reprocess altimetry data to determine with unprecedented accuracy the sea surface height (SSH) along the North Atlantic eastern boundary (NAEB).2. Exploit synergies between signal processing and machine learning to optimally combine and filter the SSH and gravity field to determine the absolute dynamic ocean topography along the NAEB. 3. Use deep learning trained on sparse observations and ocean models to identify the dynamical balances and controlling factors that maintain and modify the long-term sea level gradient along the NAEB.The aims of the project will be achieved through the development and application of novel machine learning techniques to a range of geodetic and oceanographic observations and model data sets, exploiting the power of Bristol University's advanced computing facilities. Many of the observations and techniques, particularly with respect to machine learning are only now reaching sufficient maturity for the aims of the project to be realised.

项目背景 由于冰盖融化和海洋变暖导致的海平面上升对世界上大部分生活在低洼沿海地区的人口构成了重大威胁。随着气候变暖，海洋环流的大规模调整也可能导致沿海海平面突然变化，局部放大或减轻全球平均海平面上升及其影响。因此，虽然许多海平面研究都集中在其全球平均值上，但人们越来越认识到我们必须更好地了解驱动海平面区域变化的因素，例如海洋环流。例如，海洋模型表明，赤道和北欧之间的北大西洋东部边界海平面下降了约 60 厘米。然而，由于测量的限制，尚未对沿海海平面的这种变化或其维持的动态进行准确的观测。机器学习为在这个令人兴奋的领域取得根本性进展提供了机会。项目目标和方法该项目的最终目标是确定维持北大西洋东部边界海平面大幅下降的海洋过程，从而解释海洋大规模环流的一个鲜为人知的特征及其对海平面上升的影响。然而，这一目标受到三个主要困难的阻碍：(i) 使用卫星测高在沿海地区获得干净的海平面信号； (ii) 从 SSH 中去除大得多的重力信号，以获得与海洋动力学相关的小量 (~1%) 残差，以及 (iii) 将表面信号与稀疏的地下观测相关联，以识别边界动力学。在这里，我们建议采用机器学习技术来克服这些困难。相应地，该项目将分三个阶段进行：1.利用深度学习和图像分类重新处理测高数据，以前所未有的精度确定北大西洋东部边界（NAEB）沿线的海面高度（SSH）。2.利用信号处理和机器学习之间的协同作用，以最佳方式组合和过滤 SSH 和重力场，以确定 NAEB 沿线的绝对动态海洋地形。 3. 使用经过稀疏观测和海洋模型训练的深度学习来确定维持和改变 NAEB 沿线长期海平面梯度的动态平衡和控制因素。该项目的目标将通过开发和应用新型机器学习技术到一系列大地测量和海洋学观测和模型数据集，利用布里斯托大学先进计算设施的力量来实现。许多观察和技术，特别是机器学习方面的观察和技术，现在才达到足够成熟，足以实现该项目的目标。