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Article

HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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Academic Editor: Stefania Bonafoni
Remote Sens. 2021, 13(11), 2179; https://doi.org/10.3390/rs13112179
Received: 9 May 2021 / Revised: 30 May 2021 / Accepted: 31 May 2021 / Published: 2 June 2021
The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model. View Full-Text
Keywords: GNSS meteorology; tropospheric delay; zenith hydrostatic delay (ZHD); zenith wet delay (ZWD); zenith total delay (ZTD); precipitable water vapor (PWV); relative humidity; ERA5 GNSS meteorology; tropospheric delay; zenith hydrostatic delay (ZHD); zenith wet delay (ZWD); zenith total delay (ZTD); precipitable water vapor (PWV); relative humidity; ERA5
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MDPI and ACS Style

Mateus, P.; Mendes, V.B.; Plecha, S.M. HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity. Remote Sens. 2021, 13, 2179. https://doi.org/10.3390/rs13112179

AMA Style

Mateus P, Mendes VB, Plecha SM. HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity. Remote Sensing. 2021; 13(11):2179. https://doi.org/10.3390/rs13112179

Chicago/Turabian Style

Mateus, Pedro, Virgílio B. Mendes, and Sandra M. Plecha 2021. "HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity" Remote Sensing 13, no. 11: 2179. https://doi.org/10.3390/rs13112179

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