Abstract
The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.
Highlights
The electromagnetic waves emitted from a satellite source when propagating through the atmosphere are affected by the electron content in the ionosphere and by the neutral atom and molecule densities in the troposphere [1]
We propose an Hourly Global Pressure and Temperature (HGPT) model, based on the full horizontal, vertical, and temporal resolution of the latest climate reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the new ECMWF Reanalysis 5th Generation (ERA5)
The main differences between this model and the models described previously are (1) the introduction of a quarterly periodicity for the temperature to account for the intraseasonal signal fluctuations caused by the Madden–Julian oscillation (MJO) system; (2) the introduction of the rate of change to account for a global climate change scenario; (3) the use of a time-segmentation concept, which is focused on a set of hourly coefficients; and (4) the use of the high horizontal resolution offered by the numerical weather model (NWM)
Summary
The electromagnetic waves emitted from a satellite source when propagating through the atmosphere are affected by the electron content in the ionosphere and by the neutral atom and molecule densities in the troposphere [1]. Yao et al [18] implemented the improved tropospheric grid (ITG) model based on 10 years of a 2.5◦ × 2.5◦ global horizontal grid of ERA-Interim data and has a temporal resolution of 6 h This model considers the annual, semi-annual, and diurnal variations, and can provide temperature, pressure, weighted mean temperature Tm, zenith wet delay. The best horizontal grid spacing used by all these models was one degree and the highest temporal resolution was 6 h At these scales, the surface air temperature and pressure can fluctuate significantly depending on topography, atmospheric interactions between land and sea or large lakes, and in regions more affected by atmospheric turbulence due to global atmospheric circulation features.
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