Retrieval of Precipitable Water Vapor Using GNSS Data Under Conditions Without Collocated Meteorological Observations

Abstract. Surface pressure (Ps) and weighted mean temperature (Tm) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from global navigation satellite system (GNSS) data. The lack of Ps or Tm information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate Ps and Tm at the GNSS station using nearby synoptic observations. Ps and Tm obtained at the nearby synoptic sites are interpolated onto the location of GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in Ps and Tm calculation are estimated from ERA-Interim reanalysis profiles. In addition, we present a method of constructing high quality PWV map through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the Ps and Tm retrieval and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby sites in 2015 were processed to extract the PWV so as to subsequently generate PWV map. The retrieved Ps and Tm and constructed PWV maps were assessed by the results derived from radiosonde and ERA-Interim reanalysis. The results show that (1) accuracies of Ps and Tm derived by synoptic interpolation are within the range of 1.7–3.0 hPa and 2.5–3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA reanalysis data with overall accuracy better than 3 mm; and (3) PWV maps can well reveal the moisture advection, transportation and convergence during heavy rainfall.

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Precipitable Water Vapor Estimates in the Australian Region from Ground-Based GPS Observations

Advances in Meteorology ◽

10.1155/2015/956481 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 10

Author(s):

Suelynn Choy ◽

Chuan-Sheng Wang ◽

Ta-Kang Yeh ◽

John Dawson ◽

Minghai Jia ◽

...

Keyword(s):

Water Vapor ◽

Standard Deviation ◽

Regional Climate ◽

Regional Distribution ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Mean Difference ◽

Climate Conditions ◽

Long Baseline ◽

Meteorological Observations

We present a comparison of atmospheric precipitable water vapor (PWV) derived from ground-based global positioning system (GPS) receiver with traditional radiosonde measurement and very long baseline interferometry (VLBI) technique for a five-year period (2008–2012) using Australian GPS stations. These stations were selectively chosen to provide a representative regional distribution of sites while ensuring conventional meteorological observations were available. Good agreement of PWV estimates was found between GPS and VLBI comparison with a mean difference of less than 1 mm and standard deviation of 3.5 mm and a mean difference and standard deviation of 0.1 mm and 4.0 mm, respectively, between GPS and radiosonde measurements. Systematic errors have also been discovered during the course of this study, which highlights the benefit of using GPS as a supplementary atmospheric PWV sensor and calibration system. The selected eight GPS sites sample different climates across Australia covering an area of approximately 30° NS/EW. It has also shown that the magnitude and variation of PWV estimates depend on the amount of moisture in the atmosphere, which is a function of season, topography, and other regional climate conditions.

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New meteorological products based on Sentinel-1 and GNSS

10.5194/egusphere-egu2020-19142 ◽

2020 ◽

Author(s):

Giovanni Nico ◽

Francesco Vespe ◽

Olimpia Masci ◽

Pedro Mateus ◽

João Catalão ◽

...

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Precipitable Water ◽

Propagation Delay ◽

Precipitable Water Vapor ◽

Sar Interferometry ◽

Extreme Weather Events ◽

Geophysical Research ◽

Gnss Data ◽

The Impact

Recently, the SAR Meteorology technique has demostrated the advantages of assimilating Sentinel-1 maps of Precipitable Water Vapor (PWV) in high resolution Numerical Weather Models (NWP) when forecasting extreme weather events [1]. The impact of Sentinel-1 information on NWP forecast depends on the acquisition parameters of Sentinel-1 images and the physical status of atmosphere [2]. Besides meteorological applications, enhancing NWP forecast could have an impact also on the mitigation of atmospheric artifacts in SAR interferometry applications based on NWP simulations [3].This work describes a methodology to provide measurements of microwave propagation delay in troposphere. The proposed methodology is based on the processing of Sentinel-1 and GNSS data. In particular, Sentinel-1 images are processed by means of SAR Interferometry technique to get measurements of propagation delay in troposphere over land assuming that phase contribution due terrain displacements can be neglected. To fulfil this condition, the interferometric processing is carried out on Sentinel-1 images having the shortest temporal baseline of six days. Interferometric coherence is used to select portions of the interferogram where to estimates of PWV and the corresponding precision are provided. GNSS measurements of propagation delay in atmosphere are used to validate the Sentinel-1 measurements and derive a quality figure of PWV maps. A procedure is presented to concatenate PWV maps in time in order to derive a time series of spatially dense PWV measurements and the corresponding precisions.Furthermore, Radio Occultation (RO) profiles are obtained by processing GNSS data. Profiles will be used to derive an estimate of propagation delay in troposphere over sea. In such a way, maps of propagation delay in atmosphere over both land and sea, even though characterized by a different spatial density of measurements, will be provided.The study area includes the Basilicata, Calabria and Apulia regions and the Gulf of Taranto, southern Italy.This work was supported by the Ministero dell'Istruzione, dell'Universit&#224; e della Ricerca (MIUR), Italy, under the project OT4CLIMA.References[1] P. Mateus, J. Catal&#227;o, G. Nico, &#8220;Sentinel-1 interferometric SAR mapping of precipitable water vapor over a country-spanning area&#8221;, IEEE Transactions on Geoscience and Remote Sensing, 55(5), 2993-2999, 2017.[2] P.M.A. Miranda, P. Mateus, G. Nico, J. Catal&#227;o, R. Tom&#233;, M. Nogueira, &#8220;InSAR meteorology: High&#8208;resolution geodetic data can increase atmospheric predictability&#8221;, Geophysical Research Letters, 46(5), 2949-2955, 2019.[3] G. Nico, R. Tome, J. Catalao, P.M.A. Miranda, &#8220;On the use of the WRF model to mitigate tropospheric phase delay effects in SAR interferograms&#8221;, IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4970-4976, 2011.

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

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.1033 ◽

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.

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

Remote Sensing ◽

10.3390/rs13112179 ◽

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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