An improved method for developing the precipitable water vapor vertical correction global grid model

Precipitable Water Vapor (PWV) is a crucial parameter in climate research. However, obtaining high precision PWV data remains a problem. Discrepancies in water vapor elevation data from diverse observation sources pose challenges, emphasizing the need for precise vertical correction. In this paper, we proposed the precipitable water vapor model with systematic difference correction by using ERA5 (the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis) data and GNSS (Global Navigation Satellite System) data. In this proposed method, two vertical correction models, employing cubic polynomial and exponential functions, were developed. The exponential function demonstrated superior overall performance with the RMS error of 0.96 mm, effectively capturing vertical characteristics across diverse regions, while the cubic polynomial function had the RMS (Root Mean Square) error of 2.77 mm validated by ERA5 PWV. The cubic polynomial function was found more suitable for low-elevation regions, while the exponential function excelled in highelevation regions. Validated by radiosonde PWV, the cubic polynomial function and the exponential function show a strong correlation with latitude. Both functions exhibit smaller RMS values at higher latitudes and larger RMS values at lower latitudes. Validated by GNSS PWV, the cubic polynomial function exhibits superior accuracy, with the RMS of 2.39 mm, compared to the exponential function, particularly in specific regions. Analyzing six years of data reveals significant systematic differences between ERA5 and GNSS PWV. This discrepancy exhibits a noticeable annual periodic variation. The global GNSS stations were organized into grids, with stations within the same region grouped together for correction. Models considering systematic differences exhibited substantial RMS reductions and better accuracy. These findings can provide reference in atmospheric correction and the study of extreme weather events.