Atmospheric modeling and data assimilation

大气建模和数据同化

• 批准号: RGPIN-2020-06602
• 负责人: Gauthier, Pierre
• 金额: $ 1.82万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746642
• 关键词: Atmospheric; modeling; data; assimilation

The objective of data assimilation in atmospheric science is to reconcile measurements of the atmosphere and a forecast from a numerical model which represents the dynamics of the atmosphere to the best of our knowledge. The assimilation takes into account the error statistics of both the observations and the forecast. Over the last two decades, data assimilation systems have evolved and can now ingest very large volumes of data (~107 per day) and take into account the time dimension. During the last few years, I have been working on a prototype satellite instrument to measure radiances in the far infrared. To optimize the configuration of the instrument to obtain the most information from those measurements, a simple approach has been developed based on information theory to evaluate different configurations of the instrument. A preliminary study allowed to determine which wavebands should be selected. Given the high cost of putting an instrument on a satellite, it is very helpful to have the tools to help us make the right choices. The work on new types of observations will be pursued with a view to compare the 1D approach to more elaborate Observing System Experiments  in which existing observations are assimilated in state-of-the-art systems by adding or subtracting observations between two experiments over periods of weeks to months. The objective will be to assess the degree of confidence one may have in the 1D simpler approach. Another aspect of the proposed research is concerned with different data assimilation strategies in controlled experiments with simpler models to evaluate their strengths and weaknesses. Emphasis will be put on the ability to detect through observations the emergence of rapidly developing disturbances that can be associated with high impact weather events. To be able to capture such a weak signal, new methods are needed but they require  a significant amount of resources to be applicable to complex operational weather forecast systems which now couples the atmosphere with other components (e.g., oceans, land). But there is a big leap from theory to applications. In the Joint Effort for Data assimilation Integration (JEDI), a collaborative development between partners (e.g., NASA) of the Joint Center for Satellite Data Assimilation (JCSDA), a strategy is developed in which different assimilation methods can be tested and compared with either a simple model or a complete NWP model. The objective is for data assimilation developed in better controlled environments, as the one I propose, can be further tested with more complex systems. The scientific community sees this as a first and necessary step in the development of data assimilation systems. Using simple models in a controlled environment can help to better understand the dynamics underlying the rapid development of a weather system or why climate evolves slowly but abruptly at times. This is what the overarching objective of this research is about.

大气科学中数据同化的目标是协调大气的测量结果和根据我们所知代表大气动态的数值模型的预测。同化考虑了观测和预测的误差统计。在过去二十年中，数据同化系统不断发展，现在可以摄取大量数据（每天约 107 个）并考虑时间维度。在过去的几年里，我一直致力于研制一种原型卫星仪器来测量远红外辐射率。为了优化仪器的配置以从这些测量中获取最多的信息，基于信息论开发了一种简单的方法来评估仪器的不同配置。初步研究可以确定应选择哪些波段。考虑到在卫星上安装仪器的成本很高，拥有工具来帮助我们做出正确的选择是非常有帮助的。将开展新型观测的工作，以便将一维方法与更复杂的观测系统实验进行比较，其中通过在几周到几个月的时间内添加或减去两个实验之间的观测结果，将现有观测结果同化到最先​​进的系统中。目标是评估人们对一维更简单方法的信心程度。 拟议研究的另一个方面涉及受控实验中的不同数据同化策略，使用更简单的模型来评估其优点和缺点。重点将放在通过观测发现可能与高影响天气事件相关的快速发展的干扰的能力上。为了能够捕获如此微弱的信号，需要新的方法，但它们需要大量的资源才能适用于复杂的业务天气预报系统，该系统现在将大气与其他组成部分（例如海洋、陆地）耦合起来。但从理论到应用有一个很大的飞跃。在数据同化集成联合努力 (JEDI) 中，卫星数据同化联合中心 (JCSDA) 的合作伙伴（例如 NASA）合作开发了一项策略，可以测试不同的同化方法，并与简单模型或完整的 NWP 模型进行比较。目标是在更好的受控环境中开发数据同化，正如我建议的那样，可以用更复杂的系统进行进一步测试。科学界认为这是开发数据同化系统的第一步也是必要的一步。在受控环境中使用简单模型有助于更好地理解天气系统快速发展的动态，或者为什么气候有时会缓慢但突然地演变。这就是本研究的总体目标。