Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data

TLDR

Troposphere mapping functions convert zenith hydrostatic and wet delays to any elevation angle in GPS and VLBI analyses, with the Niell Mapping Function (NMF) being the most widely used. This study introduces the Global Mapping Function (GMF), derived from ECMWF numerical weather model data. GMF coefficients are obtained by expanding Vienna Mapping Function (VMF1) parameters into spherical harmonics on a global grid, requiring only station coordinates and day of year as inputs. Compared to 6‑hourly VMF1, GMF shows a slight loss in short‑term precision but markedly reduces NMF’s regional height biases and annual errors.

Abstract

Troposphere mapping functions are used in the analyses of Global Positioning System and Very Long Baseline Interferometry observations to map a priori zenith hydrostatic and wet delays to any elevation angle. Most analysts use the Niell Mapping Function (NMF) whose coefficients are determined from site coordinates and the day of year. Here we present the Global Mapping Function (GMF), based on data from the global ECMWF numerical weather model. The coefficients of the GMF were obtained from an expansion of the Vienna Mapping Function (VMF1) parameters into spherical harmonics on a global grid. Similar to NMF, the values of the coefficients require only the station coordinates and the day of year as input parameters. Compared to the 6‐hourly values of the VMF1 a slight degradation in short‐term precision occurs using the empirical GMF. However, the regional height biases and annual errors of NMF are significantly reduced with GMF.