The Usage of Observing System Simulation Experiments and Reinforcement Learning to Optimize Experimental Design and Operation [report]

Joseph Hardin
2021 unpublished
Various organizations regularly conduct field campaigns across the globe designed to probe and improve atmospheric process understanding. However, these campaigns are mostly designed ad-hoc, and rely on anecdotes and knowledge of what was successful in previous campaigns. Such ad-hoc experiment design results in sub-optimal instrument siting and operation strategies. Inadequate targeted data collection of extreme weather is a major limitation to Earth and Environmental Systems Science Division
more » ... EESSD)'s goals of data model integration (4.5.3) [8] , which limits the knowledge gained through the collected observations, holding back significant advances in predictability. While advances in instrument design are always chipping away at these limitations, we are not optimally utilizing the instrumentation we currently possess. For relatively infrequent but highly impactful severe weather, it is critical to maximize chances of observing details in a way that optimizes process-level understanding and leads to accurate predictions. For many similar challenges in the integrated water cycle, where multiple instrument data-streams must be synthesized together, this ad-hoc uncoordinated deployment of instruments is problematic. A more holistic design framework is needed to optimize accurate, detailed measurements and predictions. Currently, limitations in our data capture is one of the fundamental limiting factors in our understanding of atmospheric science. More recently, investigators have started to incorporate Observing System Simulation Experiments (OSSEs) [1], [2], where we combine Earth system model runs and instrument simulators to understand the effect of deployment decisions to capture details of the atmospheric state. However, OSSEs typically employ designs constrained by typical instrument deployment concepts; thus, they often do not explore innovative observing strategies that could maximize utility. We use OSSE's to reduce the cost and effort of trying different configurations in the field, a process which can be expensive and risk failure of an
doi:10.2172/1769782 fatcat:jzzuuzjgkvgehps6acd3ntxkse