{'fraction_non_alphanumeric': 0.0224642614023145, 'fraction_numerical': 0.0, 'mean_word_length': 5.419213973799127, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Tracking and Mapping Coherent Structures

Many atmospheric phenomena such as thermals, plumes, storms, jets, fronts, tornadoes and hurricanes can be described as coherent structures. We have pioneered new algorithms called Field Alignment and Coalescence that make difficult inference problems for coherent structures, namely assimilation and uncertainty quantification, tractable. In this paper, we examine the problem of mapping coherent structures, which is also difficult to solve using existing approaches. We propose a dynamic data-driven hybrid mapping approach and apply it in initial experiments to the study of plumes. Taking advantage of advances in dealing with coherent structures, our approach decomposes the mapping problem into two closed feedback loops. In the first loop, a coherent structure-following sampling plan is produced by tracking remote observations using an improved version of Scale-cascaded Field Alignment to better handle events in the life cycle of a coherent structure. In the second loop, in-situ measurements from sampling plans are used to produce analyses (maps) and the analyses are used to adapt sampling plans. For plume experiments, we use a Gaussian Process model for producing analyses and a Markov-Chain Monte-Carlo technique to adapt sampling plans. Together, the two loops yield a hybrid mapping strategy that is resilient, efficient, and scalable and makes tractable the mapping of many coherent structures of interest.