Atmospheric modeling and data assimilation

大气科学中数据同化的目标是协调大气测量和数值模式的预报，据我们所知，数值模式代表了大气的动力学。同化考虑了观测和预报的误差统计。在过去二十年中，数据同化系统已经发展，现在可以摄取非常大量的数据(每天约107个)，并考虑到时间维度。在过去的几年里，我一直在研究一种用于测量远红外辐射的卫星仪器原型。为了优化仪器的配置，以便从这些测量中获得最多的信息，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。一项初步研究，以确定应该选择哪些波段。考虑到将仪器放在卫星上的高昂成本，拥有帮助我们做出正确选择的工具是非常有帮助的。将继续开展关于新型观测的工作，以期将一维方法与更复杂的观测系统实验进行比较，在最先进的系统中，通过在几周至几个月的时间内在两个实验之间增加或减少观测，吸收现有的观测。我们的目标将是评估人们对1D SIMPLE方法的信心程度。这项拟议研究的另一个方面是关于控制实验中不同的数据同化策略，使用更简单的模型来评估它们的优点和缺点。重点将放在通过观测发现可能与高影响天气事件有关的迅速发展的扰动的出现的能力。为了能够捕捉到如此微弱的信号，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)耦合在一起。但从理论到应用都有很大的飞跃。在卫星数据同化联合中心(JCSDA)的合作伙伴(例如美国宇航局)之间的合作开发的数据同化集成联合努力(JEDI)中，制定了一种战略，其中可以测试不同的同化方法，并将其与简单模式或完整的NWP模式进行比较。我们的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一步测试。科学界认为这是发展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的模型可以帮助更好地理解天气系统快速发展背后的动力，或者为什么气候演变缓慢但有时突然。这就是这项研究的总体目标所在。

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.