Hybrid data assimilation for coupled atmosphere-ocean models

大气-海洋耦合模型的混合数据同化

• 批准号: NE/M001482/1
• 负责人: Amos Lawless
• 金额: $ 34.68万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FM001482%2F1
• 关键词: Hybrid; data; assimilation; coupled; atmosphere

The monitoring of the climate of planet Earth and the possibility to predict environmental changes on time-scales of weeks to months, and even on decadal time-scales, is becoming of increasing importance to society. Changes in phenomena such as floods, droughts and sea-level rise are expected to have a large societal impact, affecting many aspects of human life, including agriculture, provision of flood defences and human health. For policymakers there is a need to understand more accurately how the planet is changing and to have improved predictions of future changes.As part of this goal to increase our knowledge of the Earth, space agencies have invested heavily in Earth observation programmes over recent years, with continued investment planned over the coming decade (for example, the European Space Agency Sentinel satellites, which are being developed as part of the European Earth Observation programme Copernicus). This has led to a huge rise in the number of measurements available from satellites covering many different components of the Earth system, including the atmosphere, ocean, land and cryosphere. The synergistic use of these measurements provides the possibility of an increased understanding of the workings of the whole Earth system and an improved predictive capability. Data assimilation is the science of combining observations from different data sources with a computer model forecast in order to extract the most information from the available measurements. In order to improve the capability of environmental monitoring and prediction, and to make better use of new satellite data, many operational centres, such as the Met Office, are now developing assimilation techniques that use observations of the atmosphere and ocean together in order to estimate the state of the combined system. In order to obtain optimal impact from the measurements it is important to characterize the statistics of the errors in the computer model forecast. In particular, when treating the coupled atmosphere-ocean system, a proper representation of the relationship between the errors in the atmosphere and ocean model forecasts is needed. In this project we will develop new methods for estimating these error statistics and for including this information within data assimilation schemes. The involvement of the Met Office and the European Centre for Medium-range Weather Forecasts in the project will allow rapid transfer of knowledge to operational practice.

对地球气候的监测以及在几周到几个月甚至十年的时间尺度上预测环境变化的可能性对社会变得越来越重要。洪水、干旱和海平面上升等现象的变化预计将产生巨大的社会影响，影响人类生活的许多方面，包括农业、防洪和人类健康。对于政策制定者来说，需要更准确地了解地球正在如何变化，并改进对未来变化的预测。作为增加我们对地球的了解这一目标的一部分，航天机构近年来在地球观测项目上投入了大量资金，计划在未来十年继续投资（例如，作为欧洲地球观测计划哥白尼计划的一部分正在开发的欧洲航天局哨兵卫星）。这导致覆盖地球系统许多不同组成部分（包括大气、海洋、陆地和冰冻圈）的卫星提供的测量数据数量大幅增加。这些测量的协同使用可以增强对整个地球系统运作的了解并提高预测能力。数据同化是将不同数据源的观测结果与计算机模型预测相结合的科学，以便从可用的测量中提取最多的信息。为了提高环境监测和预测的能力，并更好地利用新的卫星数据，英国气象局等许多业务中心正在开发同化技术，将大气和海洋的观测结合起来，以估计组合系统的状态。为了从测量中获得最佳影响，表征计算机模型预测中的误差统计数据非常重要。特别是，在处理大气-海洋耦合系统时，需要正确表示大气和海洋模型预测误差之间的关系。在这个项目中，我们将开发新的方法来估计这些误差统计并将这些信息包含在数据同化方案中。英国气象局和欧洲中期天气预报中心参与该项目将使知识快速转化为业务实践。