Machine Learning‐Based Model for Real‐Time GNSS Precipitable Water Vapor Sensing

Abstract

AbstractGlobal Navigation Satellite Systems (GNSS) provide a promising opportunity for real‐time precipitable water vapor (PWV) sensing. However, relying on meteorological information restrains the implementation of real‐time inversion from tropospheric zenith total delays (ZTD) into PWV. In this study, a stacked machine learning model for mapping ZTD into PWV in the absence of meteorological parameters is developed. The model performance is assessed and validated with information offered by fifth generation European Centre for Medium‐Range Weather Forecasts reanalysis (ERA5) and radiosondes. An accuracy of better than 2.5 mm is achievable for the PWV values. Compared to the physical model which applies GPT3‐derived meteorological parameters, the proposed model reveals an enhanced performance, especially in the high‐latitude regions, with improvements of 28.1% and 22.2% when validated with ERA5 and radiosondes. This model is capable of fulfilling the demands of time‐critical meteorological applications and is also promising for real‐time PWV retrieval of other techniques that own the capability to sense ZTD.