Atmospheric modeling and data assimilation

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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.