[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125296-en":3,"doc-seo-125296-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125296,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Wake vortex lidar measurement processing with large-eddy simulations and machine learning","Aircraft wake vortices can threaten aircraft that follow during final approach. Lidar instruments combined with tailored processing algorithms characterize wake-vortex position and strength, yet field measurements suffer from missing ground truth, limiting achievable accuracy. This paper proposes lidar simulations grounded in large-eddy simulations as labeled reference data, paired with a machine learning–based lidar processing algorithm. The resulting model generalizes across comparable simulation and field datasets, enabling wake vortex detection rates above 90% and practical characterization accuracies while reducing reliance on field truth.","Research Article  \nVol. 33, No. 12/16 Jun 2025/Optics Express 26473  \nWake vortex lidar measurement processing with large-eddy simulations and machine learning  \nNIKLAS WARTHA , 1,2,*  ANTON STEPHAN , 1 AND FRANK HOLZÄPFEL1   \n1 Institut für Physik derAtmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Oberpfaffenhofen, Germany  \n2 Institute of Aerospace Systems, RWTH Aachen University, 52062 Aachen, Germany  \n*  \n[Niklas.Wartha@dlr.de](Niklas.Wartha@dlr.de)  \nAbstract: Aircraft generated wake vortices may pose a hazard to following aircraft during final approach. Light detection and ranging (lidar) instruments and appropriate processing algorithms are employed for characterizing the position and strength of wake vortices. Unavailable ground-truths in field measurements limit obtainable accuracies of processing algorithms. In this paper the employment of lidar simulations with known ground-truths from large-eddy simulations enhanced by a machine learning based lidar processing algorithm is proposed. This processing algorithm generalization offers wake vortex detection rates above 90% and practical characterization accuracies for comparable simulation and field measurement datasets, decreasing the need for the latter.  \nPublished by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \n1. Introduction  \nThe study of wake vortices, rotations of air directly produced as a consequence of aircraft lift generation, constitutes an unavoidable component for enhancing runway throughput at airports whilst ensuring safe operations [1] . Field measurements, computational fluid dynamics, as well as deterministic and probabilistic fast-time prediction models are tools to reach the unified goal of determining and/or adjusting the life, strength, and path of the wake vortices. Factors influencing the characteristics of wake vortices spread from the aircraft geometry to the prevailing atmospheric conditions and ground topography prevailing at the landing site.  \nA wake vortex encounter occurs when a follower aircraft flies into a wake vortex produced by a generator aircraft flying ahead. Encounters are most common and dangerous during the final approach and landing phase of an aircraft, as aircraft share similar glide paths and their low altitude does not allow for major piloting maneuvers [2] . Also emerging air vehicles, such as air taxis, may experience wake vortex encounters when a vertiport is installed in runway vicinity. For studying or operationally monitoring wake vortex evolutions at airports light detection and ranging (lidar) instruments, in recent years particularly pulsed coherent Doppler lidars (PCDLs), are employed next to the glide path of approaching aircraft. In particular, PCDLs (used interchangeable with lidar in this paper) with a laser wavelength of λ ≈ 1.5 µm are most modern, delivering high spatial resolution and highly accurate line of sight (LOS) velocity measurements. Most wake vortex related undertakings involve lidar measurements at some stage during their development (for example the plate line concept in Ref. [3]) or during the approval process by authorities. Lidar measurements consist of LOS velocities to and from the instrument by evaluating the frequency shift between the emitted laser beam and the echo signal backscattered by aerosols. A range height indicator (RHI) scan geometry is sketched in Fig. 1, delivering a two-dimensional velocity field (LOS velocities, Vr ) perpendicular to the ground, characterized by the azimuth angle perpendicular to the runway, elevation angle φ, and radial distance r, where  \n\\#562553 [https://doi.org/10.1364/OE.562553](https://doi.org/10.1364/OE.562553)  \nJournal © 2025 Received 18 Mar 2025; revised 5 Jun 2025; accepted 5 Jun 2025; published 12 Jun 2025  \nResearch Article  \nVol. 33, No. 12/16 ","cbCaik8X33Os8txo","https://ap.wps.com/l/cbCaik8X33Os8txo","pdf",5276424,1,26,"English","en",105,"# Introduction\n## Wake vortex hazard and measurement need\n## Lidar measurement principle and RHI scan geometry\n## Vortex strength metrics and limitations of LOS data\n## Processing algorithms: physical vs data-driven","[{\"question\":\"Why is wake vortex characterization important during aircraft final approach?\",\"answer\":\"Wake vortex encounters are most common and dangerous during final approach and landing because low altitude limits major piloting maneuvers. Accurate position and strength assessment supports safer operations and runway throughput.\"},{\"question\":\"What problem do lidar measurements have for directly computing vortex centroids and circulation?\",\"answer\":\"Lidar provides only a line-of-sight velocity component, so vortex centroids and circulation cannot be computed straightforwardly from the vorticity or LOS velocity field. Circulation from measurements often relies on averaged formulations over disc radii.\"},{\"question\":\"How does the paper improve lidar processing when ground truth is unavailable in field data?\",\"answer\":\"The study uses lidar simulations with known ground truth derived from large-eddy simulations, then trains a machine-learning-based lidar processing algorithm. This enables wake vortex detection above 90% and practical characterization accuracy across simulation and field datasets.\"}]","Wake vortex lidar measurement processing with large-eddy simulations and machine learning | PDF",1785898036,66,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"wake-vortex-lidar-measurement-processing-with-large-eddy-simulations-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/wake-vortex-lidar-measurement-processing-with-large-eddy-simulations-and-machine-learning/125296/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is wake vortex characterization important during aircraft final approach?","Question",{"text":75,"@type":76},"Wake vortex encounters are most common and dangerous during final approach and landing because low altitude limits major piloting maneuvers. Accurate position and strength assessment supports safer operations and runway throughput.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem do lidar measurements have for directly computing vortex centroids and circulation?",{"text":80,"@type":76},"Lidar provides only a line-of-sight velocity component, so vortex centroids and circulation cannot be computed straightforwardly from the vorticity or LOS velocity field. Circulation from measurements often relies on averaged formulations over disc radii.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper improve lidar processing when ground truth is unavailable in field data?",{"text":84,"@type":76},"The study uses lidar simulations with known ground truth derived from large-eddy simulations, then trains a machine-learning-based lidar processing algorithm. 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