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Article

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

1
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
2
Istituto per le Applicazioni del Calcolo (IAC), Consiglio Nazionale delle Ricerche (CNR), 70126 Bari, Italy
3
Department of Cartography and Geoinformatics, Institute of Earth Sciences, Saint Petersburg State University (SPSU), 199034 St. Petersburg, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1098; https://doi.org/10.3390/rs12071098
Received: 13 February 2020 / Revised: 26 March 2020 / Accepted: 27 March 2020 / Published: 30 March 2020
(This article belongs to the Section Atmosphere Remote Sensing)
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. View Full-Text
Keywords: GNSS meteorology; tropospheric delay; hydrostatic and wet delay; weighted mean temperature; surface air temperature; surface pressure; ERA5 data GNSS meteorology; tropospheric delay; hydrostatic and wet delay; weighted mean temperature; surface air temperature; surface pressure; ERA5 data
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MDPI and ACS Style

Mateus, P.; Catalão, J.; Mendes, V.B.; Nico, G. An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model. Remote Sens. 2020, 12, 1098. https://doi.org/10.3390/rs12071098

AMA Style

Mateus P, Catalão J, Mendes VB, Nico G. An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model. Remote Sensing. 2020; 12(7):1098. https://doi.org/10.3390/rs12071098

Chicago/Turabian Style

Mateus, Pedro; Catalão, João; Mendes, Virgílio B.; Nico, Giovanni. 2020. "An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model" Remote Sens. 12, no. 7: 1098. https://doi.org/10.3390/rs12071098

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