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的是调和大气的测量值，并从数值模型中进行预测，该模型代表了大气的动力学到我们所知。同化考虑了观测值和预测的错误统计数据。在过去的二十年中，数据同化系统已经发展起来，现在可以摄入大量的数据（每天约107个），并考虑到时间维度。在过去的几年中，我一直在制作原型卫星仪器，以测量远距离感染的辐射。为了优化仪器的配置以从这些测量值中获取最多信息，已经基于信息理论开发了一种简单的方法，以评估仪器的不同配置。初步研究允许确定应该选择哪些波段。鉴于将乐器放在卫星上的高成本，拥有帮助我们做出正确选择的工具非常有帮助。将进行有关新类型的观察类型的工作，以比较1D方法，以进行更详细的观测系统实验，其中通过在几周到几个月内添加或减去两个实验之间的观察结果，通过在最先进的系统中吸收了现有的观察结果。目的是评估人们对一维更简单方法的信心程度。拟议的研究的另一个方面与更简单模型的受控实验中的不同数据同化策略有关，以评估其优势和劣势。通过观察到可能与高影响天气事件有关的快速发展灾害的出现，将重点放在检测能力上。为了能够捕获如此弱的信号，需要新的方法，但它们需要大量资源才能适用于复杂的操作天气预报系统，这些系统现在将大气与其他组件（例如海洋，陆地）相结合。但是从理论到应用都有很大的飞跃。在数据同化整合（JEDI）的联合努力中，合作伙伴（例如NASA）卫星数据同化联合中心（JCSDA）之间的合作开发，可以开发一种策略，其中可以测试不同的同化方法并与简单的模型或完整的NWP模型相比。目的是用于在更好的控制环境中开发的数据同化，因为我提出的一个可以通过更复杂的系统进一步测试。科学界认为这是数据同化系统开发的第一步骤和必要步骤。在受控环境中使用简单的模型可以帮助更好地理解天气系统快速发展的动态，或者气候有时会缓慢而突然发展的原因。这就是这项研究的总体目标。