Open AccessMachine learning methods for Precipitable Water Vapor estimation by radiometric data in millimetre wavelength
Author(s) -
Grigoriy Bubnov,
P. M. Zemlyanukha,
Evgeniy Dombek,
V. F. Vdovin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2015/1/012024
Subject(s) - water vapor , radiometer , radiometry , remote sensing , microwave radiometer , precipitable water , artificial neural network , radiometric dating , detector , computer science , environmental science , meteorology , artificial intelligence , telecommunications , physics , geology
This work deals with the first try to calculate the amount of Precipitable Water Vapor (PWV) in atmosphere by using machine learning and AI methods. We use the detector voltages series measured by radiometric system “MIAP-2” as the initial data for machine learning. The radiometer MIAP-2 works by “atmospheric dip method” in 2mm and 3mm atmospheric transparency windows. We also have PWV data series collected by Water Vapor Radiometer and GNSS receiver for data validation. The best convergence results were demonstrated by the independent component analysis (ICA) method with coefficient of determination R 2 = 0.53 and artificial neural network method (ANN) with R 2 = 0.8. These methods allow to reduce the systematic errors due to direct PWV calculation from raw radiometric data avoiding unnecessary steps opacity calculation.