Analysis of the Results of Astroclimate Measurements in the Millimeter Wavelength Range Using Machine Learning Methods

• Published in: Astrophysical bulletin Vol. 79; no. 2; pp. 340 - 349
• Main Authors: Khabarova, T. A., Zemlyanukha, P. M., Dombek, E. M., Marukhno, A. S., Vdovin, V. F.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01-06-2024; Springer Nature B.V
• Subjects: Astronomy; Astrophysics and Cosmology; Machine learning; Microwave radiometers; Physics; Physics and Astronomy; Radiometry; Satellite observation; Water vapor; millimeter astronomy—methods: data analysis
• ISSN: 1990-3413; 1990-3421
• DOI: 10.1134/S1990341324700299

This paper presents a method for estimating precipitable water vapor from radiometric data using machine learning methods. The results of a study of precipitated water vapor for the territory of Chirag (Dagestan), Terskol peak (Elbrus region), Badary observatory (Buryatia) and the Spitsbergen archipelago are presented. A comparative analysis of the assessment of precipitable water vapor for the territory of ‘‘Badary’’ was carried out using GNSS, MERRA-2, water vapor radiometer data and predicting values using machine learning methods based on data from the MIAP-2 microwave radiometer.