Developing data assimilation methods for Glaciology

开发冰川学数据同化方法

• 批准号: RGPIN-2014-06417
• 负责人: Pimentel, Samuel
• 金额: $ 1.89万
• 依托单位: Trinity Western University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567789
• 关键词: Developing; data; assimilation; methods; Glaciology

Data assimilation seeks to optimally combine observational data with numerical (computer) model output to determine the best estimate of the state of a system. This proposed project seeks to develop and apply data assimilation techniques for use in Glaciology (the study of glaciers and ice-sheets). The application of data assimilation methods is well established within other Earth Science disciplines, particularly Atmospheric and Ocean Sciences. For example, it is essential for weather forecasting. However, at present the development of data assimilation tools for glaciological research has been limited. Scholars have yet to realize the full potential of the mathematical methods of data assimilation to advance glaciological modelling capabilities. Without such methods, we are left with an inadequate analysis that fails to utilize all available information at our disposal. Over the last decade increased resources have been dedicated to improving observational coverage and model development within Glaciology. This proposed work seeks to make best use of current and future observational data and state-of-the-art ice-flow models. Research output from this proposed project will result in novel applications of data assimilation theory and play an important role in improving ice-sheet modelling capabilities. Improving our understanding of glacier and ice-sheet dynamics is essential for monitoring and predicting changes in Arctic, subarctic and alpine areas of Canada. This includes reducing uncertainty in sea-level rise estimates, assessing risk associated with glacier-related hazards, as well as improving knowledge of changes to glacier runoff and its effect on fresh water supply. These are pressing research problems of global significance.

数据同化寻求最佳的联合收割机观测数据与数值（计算机）模式输出，以确定系统状态的最佳估计。这个拟议的项目力求发展和应用数据同化技术，用于冰川学（冰川和冰盖的研究）。数据同化方法的应用在其他地球科学学科，特别是大气和海洋科学中得到了很好的确立。例如，它对天气预报至关重要。然而，目前用于冰川学研究的数据同化工具的发展受到限制。学者们尚未充分认识到数据同化数学方法在提高冰川学建模能力方面的潜力。如果没有这种方法，我们就只能进行不充分的分析，无法利用我们掌握的所有可用信息。在过去的十年中，更多的资源被用于改善冰川学的观测范围和模型开发。这项拟议的工作旨在充分利用当前和未来的观测数据和最先进的冰流模型。这一拟议项目的研究成果将导致数据同化理论的新应用，并在提高冰盖建模能力方面发挥重要作用。提高我们对冰川和冰盖动态的认识对于监测和预测加拿大北极、亚北极和高山地区的变化至关重要。这包括减少海平面上升估计的不确定性，评估与冰川有关的灾害的风险，以及提高对冰川径流变化及其对淡水供应影响的认识。这些都是具有全球意义的紧迫研究问题。