Date of Award
2026-08-01
Degree Name
Master of Science
Department
Computer Engineering
Advisor(s)
Hernan Moreno
Second Advisor
Miguel Velez-Reyes
Abstract
Uncrewed aerial systems (UAS) collect atmospheric data on fixed schedules, and endurance limits make blind searching costly. This thesis develops an unsupervised representation that summarizes a multi-decade radiosonde archive into a compact library of atmospheric states, giving a UAS an expectation of the column before it flies. The method standardizes both axes of a profile against the sounding's own surface conditions, which makes the representation independent of season and of station elevation. Applied to 27,270 soundings from Norman, Oklahoma, over the lowest 1.5 km of the atmosphere, 12 representative profiles reconstruct the record to within 1.0 °C of mean absolute error and 139 reach the 0.5 °C accuracy the World Meteorological Organization specifies for radiosonde temperature, or 0.51% of the archive. The representatives are observed soundings rather than synthetic averages. The methodology is demonstrated at one station and applies unchanged at others.
Language
en
Provenance
Received from ProQuest
Copyright Date
2026-08
File Size
115 p.
File Format
application/pdf
Rights Holder
Alejandro Medina
Recommended Citation
Medina, Alejandro, "Unsupervised Learning for Minimum Error Adaptive Sampling of Atmospheric Vertical Temperature Profiles" (2026). Open Access Theses & Dissertations. 4731.
https://scholarworks.utep.edu/open_etd/4731