Hybrid data assimilation for coupled atmosphere-ocean models

对地球气候的监测以及在数周至数月甚至数十年的时间尺度上预测环境变化的可能性，对社会越来越重要。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，影响到人类生活的许多方面，包括农业、防洪设施和人类健康。对于决策者来说，需要更准确地了解地球的变化情况，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，空间机构近年来对地球观测方案进行了大量投资，并计划在未来十年继续进行投资（例如，欧洲航天局哨兵卫星，这是作为欧洲地球观测方案哥白尼的一部分而开发的）。这导致卫星提供的测量数据数量大幅增加，覆盖地球系统的许多不同组成部分，包括大气层、海洋、陆地和冰冻圈。协同使用这些测量数据，有可能增进对整个地球系统运作的了解，并提高预测能力。数据同化是将来自不同数据源的观测结果与计算机模型预测相结合的科学，以便从现有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，许多业务中心，如气象局，目前正在开发同化技术，将大气层和海洋的观测结果结合起来，以估计这一综合系统的状况。为了从测量中获得最佳影响，重要的是描述计算机模型预测中误差的统计特性。特别是，在处理耦合的大气-海洋系统时，需要适当地表示大气和海洋模式预报误差之间的关系。在这个项目中，我们将开发新的方法来估计这些误差统计，并将这些信息纳入数据同化方案。气象局和欧洲中期天气预报中心参与该项目将使知识迅速转化为业务实践。

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.