Machine learning methods for Precipitable Water Vapor estimation by radiometric data in millimetre wavelength

Abstract 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 R2= 0.53 and artificial neural network method (ANN) with R2= 0.8. These methods allow to reduce the systematic errors due to direct PWV calculation from raw radiometric data avoiding unnecessary steps opacity calculation.