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Showalter Research\n\t\t\tTrust"}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["J. Opt. Soc. Am. A"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>The propagation of light through a turbulent flow field around an aircraft\n\t\t\t\tresults in optical distortions commonly known as aero-optic effects. The\n\t\t\t\tdevelopment of methods to mitigate these effects requires large amounts of\n\t\t\t\trealistic aero-optic data. However, methods for obtaining these data,\n\t\t\t\tincluding experiment, computational fluid dynamics, and simple phase\n\t\t\t\tscreen generation algorithms (e.g., boiling flow), each have significant\n\t\t\t\tdrawbacks such as high cost, high computation, limited quantity, and\/or\n\t\t\t\tinaccurate statistics. More recently, data-driven algorithms have been\n\t\t\t\tproposed that are computationally efficient and can synthesize aero-optic\n\t\t\t\tdata to match the statistics of measured data, but these approaches still\n\t\t\t\thave drawbacks, including limited quality, inaccurate statistics, and the\n\t\t\t\tuse of complicated algorithms. In this paper, we introduce ReVAR\n\t\t\t\t(Re-whitened Vector AutoRegression), a data-driven algorithm for\n\t\t\t\tgenerating synthetic aero-optic data that match the statistics of measured\n\t\t\t\tdata. A key contribution in this algorithm is long-range autoregression, a\n\t\t\t\tlinear predictive model that combines a standard autoregression with a set\n\t\t\t\tof low-pass filters of the data to fit both short-range and long-range\n\t\t\t\ttemporal statistics. ReVAR uses long-range AR together with a spatial\n\t\t\t\tre-whitening step to convert measured aero-optic data to temporally and\n\t\t\t\tspatially uncorrelated white noise. ReVAR can then generate synthetic\n\t\t\t\taero-optic data by reversing this process using white noise input. Using\n\t\t\t\ttwo measured turbulent boundary layer data sets, we demonstrate that ReVAR\n\t\t\t\tbetter matches the temporal power spectrum and other key metrics of the\n\t\t\t\tmeasured data than do two conventional phase screen generation methods and\n\t\t\t\tan existing single time-lag autoregressive model.<\/jats:p>","DOI":"10.1364\/josaa.600450","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T18:00:06Z","timestamp":1778522406000},"page":"C30","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ReVAR: a data-driven algorithm for generating aero-optic phase screens"],"prefix":"10.1364","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4960-4400","authenticated-orcid":true,"given":"Jeffrey\n\t\t\tW.","family":"Utley","sequence":"first","affiliation":[{"name":"Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregery T.","family":"Buzzard","sequence":"additional","affiliation":[{"name":"Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles A.","family":"Bouman","sequence":"additional","affiliation":[{"name":"Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0445-3093","authenticated-orcid":true,"given":"Matthew R.","family":"Kemnetz","sequence":"additional","affiliation":[{"name":"Air Force Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"josaa-43-7-C30-R1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1146\/annurev-fluid-120710-101152","type":"journal-article","volume":"44","author":"Wang","year":"2012","journal-title":"Annu. 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