Hourly GNSS-derived integrated moisture in the global tropics for the years 2001-2018

Global tropics are essential in formulating weather patterns and climate across various latitudes through atmospheric teleconnections. Since water vapour is an essential parameter in atmospheric convection and, thus, latent heat release, its tropical variability on different time scales is crucial in understanding weather and climate changes. The provided dataset consists of a global navigation satellite system (GNSS) integrated water vapour (IWV) time series delivered from 42 tropical stations belonging to the International GNSS Service (IGS) network. The delivered time series of GNSS IWV have at least 16 years of data and cover the 01.2001 – 12.2018 time span. Still, particular stations may have different beginning and end of analysed periods (related to the data completeness at the given station). The GNSS IWV time series were calculated according to the following rules: (i) estimation of zenith tropospheric delay (ZTD) from raw GNSS observations, (ii) estimating of a posteriori zenith hydrostatic delay (ZHD) using meteorological parameters sourced from ERA5, (iii) subtracting a posteriori ZHD from GNSS ZTD to obtain zenith wet delay (ZWD) time series, and (iv) converting ZWD to IWV. The GNSS ZTDs were estimated using 30-second observations and precise ephemeris (orbits and clocks) from the second Center for Orbit Determination in Europe (CODE) reprocessing. All calculations were performed in Bernese GNSS Software 5.2, using a precise point positioning approach to prevent error propagation between stations. Additionally, observations from satellites below 5° elevation were excluded from the processing to reduce the multipath effect. The a priori ZHD and tropospheric mapping functions were taken from Vienna Mapping Function 1 (VMF1). In all those cases where it was possible, the individual GNSS antenna calibration was used; otherwise, the type means were adopted. In that way, hourly ZTD data were obtained. Next, the ERA5 temperature and pressure hourly time series were bilinearly interpolated to the GNSS station location and then used for a posteriori ZHD estimation according to Saastamoinen's (1972) formula. After subtracting the ZHD time series from the ZTD ones, the resulting ZWD time series were converted to the IWV time series with the approach proposed by Bevis et al. (1994). The water vapour weighted mean temperature was taken from ERA5, while the air refractivity index values were taken from Rüger (2002). Final PWV results have been saved in NetCDF format.
Thanks to the time resolution and length of collected data, the provided dataset of GNSS IWV time series can be successfully used for any study from interannual to daily time scale. With this dataset, we would also like to promote the GNSS technique as a weather-independent, cost-effective, and stuff-free method for integrated moisture estimation with a high temporal resolution. Fig. Distribution of the GNSS stations for which PWV was estimated and included in the dataset.