scholarly journals Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods

2022 ◽  
Author(s):  
Mir-Reza Ghaffari Razin ◽  
Behzad Voosoghi
Keyword(s):  
Machine Learning ◽  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Gps Observations

GPS Observations of Precipitable Water Vapor in Deep Convective Tropical Regimes

2009 ◽  
Author(s):  
David K. Adams ◽  
E. Robert Kursinski ◽  
Richard A. Bennett
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Gps Observations

Accuracy of absolute precipitable water vapor estimates from GPS observations

1998 ◽  
Vol 103 (D22) ◽  
pp. 28701-28710 ◽  
Author(s):  
Paul Tregoning ◽  
Reinout Boers ◽  
Denis O'Brien ◽  
Martin Hendy
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Gps Observations

scholarly journals Machine learning methods for Precipitable Water Vapor estimation by radiometric data in millimetre wavelength

2021 ◽  
Vol 2015 (1) ◽  
pp. 012024
Author(s):  
Grigoriy Bubnov ◽  
Peter Zemlyanukha ◽  
Evgeniy Dombek ◽  
Vyacheslav Vdovin
Keyword(s):  
Machine Learning ◽  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Coefficient Of Determination ◽  
Data Series ◽  
Convergence Results ◽  
Artificial Neural Network Method ◽  
Water Vapor Radiometer ◽  
Radiometric Data

Abstract This work deals with the first try to calculate the amount of Precipitable Water Vapor (PWV) in atmosphere by using machine learning and AI methods. We use the detector voltages series measured by radiometric system “MIAP-2” as the initial data for machine learning. The radiometer MIAP-2 works by “atmospheric dip method” in 2mm and 3mm atmospheric transparency windows. We also have PWV data series collected by Water Vapor Radiometer and GNSS receiver for data validation. The best convergence results were demonstrated by the independent component analysis (ICA) method with coefficient of determination R2= 0.53 and artificial neural network method (ANN) with R2= 0.8. These methods allow to reduce the systematic errors due to direct PWV calculation from raw radiometric data avoiding unnecessary steps opacity calculation.

scholarly journals A Comparison of Precipitable Water Vapor Estimates by an NWP Simulation and GPS Observations

1999 ◽  
Vol 38 (7) ◽  
pp. 941-956 ◽  
Author(s):  
Xiaohua Yang ◽  
Bent H. Sass ◽  
Gunnar Elgered ◽  
Jan M. Johansson ◽  
T. Ragne Emardson
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Gps Observations

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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