Local-Scale Precipitable Water Vapor Retrieval from High-Elevation Slant Tropospheric Delays Using a Dense Network of GNSS Receivers

2015 ◽  
pp. 485-490 ◽  
Author(s):  
Eugenio Realini ◽  
Kazutoshi Sato ◽  
Toshitaka Tsuda ◽  
Masanori Oigawa ◽  
Yuya Iwaki ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
High Elevation ◽  
Local Scale ◽  
Precipitable Water Vapor ◽  
Dense Network ◽  
Gnss Receivers ◽  
Tropospheric Delays

scholarly journals A High-Resolution, Precipitable Water Vapor Monitoring System Using a Dense Network of GNSS Receivers

2013 ◽  
Vol 8 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Kazutoshi Sato ◽  
◽  
Eugenio Realini ◽  
Toshitaka Tsuda ◽  
Masanori Oigawa ◽  
...  
Keyword(s):  
Water Vapor ◽  
Standard Technique ◽  
Precipitable Water ◽  
Satellite System ◽  
High Elevation ◽  
Horizontal Resolution ◽  
Precipitable Water Vapor ◽  
Dense Network ◽  
Area Of Interest ◽  
Gnss Receivers

This work describes a system aimed at the near realtimemonitoring of precipitable water vapor (PWV) by means of a dense network of Global Navigation Satellite System (GNSS) receivers. These receivers are deployed with a horizontal spacing of 1-2 km around the Uji campus of Kyoto University, Japan. The PWV observed using a standard GPS meteorology technique, i.e., by using all satellites above a low elevation cutoff, is validated against radiosonde and radiometer measurements. The result is a RMS difference of about 2 mm. A more rigorous validation is done by selecting single GPS slant delays as they pass close to the radiosonde or the radiometer measuring directions, and higher accuracy is obtained. This method also makes it possible to preserve short-term fluctuations that are lost in the standard technique due to the averaging of several slant delays. Geostatistical analysis of the PWV observations shows that they are spatially correlated within the area of interest; this confirms that such a dense network can detect inhomogeneous distributions in water vapor. The PWV horizontal resolution is improved by using high-elevation satellites only, with the aim of exploiting at best the future Quasi-Zenith Satellite System (QZSS), which will continuously provide at least one satellite close to the zenith over Japan.

scholarly journals Estimation of Local-scale Precipitable Water Vapor Distribution Around Each GNSS Station Using Slant Path Delay

SOLA ◽  
2014 ◽  
Vol 10 (0) ◽  
pp. 29-33 ◽  
Author(s):  
Yoshinori Shoji ◽  
Hiroshi Yamauchi ◽  
Wataru Mashiko ◽  
Eiichi Sato
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Local Scale ◽  
Precipitable Water Vapor ◽  
Path Delay ◽  
Slant Path ◽  
Vapor Distribution

KORELASI MUSIMAN ANTARA ZENITH WET DELAY (ZWD) DAN DEBIT SUNGAI DI DAS CIKAPUNDUNG HULU, KAWASAN BANDUNG UTARA, JAWA BARAT

2019 ◽  
Vol 3 ◽  
pp. 741
Author(s):  
Wedyanto Kuntjoro ◽  
Z.A.J. Tanuwijaya ◽  
A. Pramansyah ◽  
Dudy D. Wijaya
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Zenith Wet Delay

Kandungan total uap air troposfer (precipitable water vapor) di suatu tempat dapat diestimasi berdasarkan karakteristik bias gelombang elektromagnetik dari satelit navigasi GPS, berupa zenith wet delay (ZWD). Pola musiman deret waktu ZWD sangat penting dalam studi siklus hidrologi khususnya yang terkait dengan kejadian-kejadian banjir. Artikel ini menganalisis korelasi musiman antara ZWD dan debit sungai Cikapundung di wilayah Bandung Utara berdasarkan estimasi rataan pola musimannya. Berdasarkan rekonstruksi sejumlah komponen harmonik ditemukan bahwa pola musiman ZWD memiliki kemiripan dan korelasi yang kuat dengan pola musiman debit sungai. Pola musiman ZWD dan debit sungai dipengaruhi secara kuat oleh fenomena pertukaran Monsun Asia dan Monsun Australia. Korelasi linier di antara keduanya menunjukkan hasil yang sangat kuat, dimana hampir 90% fluktuasi debit sungai dipengaruhi oleh kandungan uap air di troposfer dengan level signifikansi 95%. Berdasarkan spektrum amplitudo silang dan koherensi, kedua kuantitas ini nampak didominasi oleh siklus monsun satu tahunan disertai indikasi adanya pengaruh siklus tengah tahunan dan 4 bulanan.

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

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

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.

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