Validation of MODIS Terra, AIRS, NCEP/DOE AMIP-II Reanalysis-2, and AERONET Sun photometer derived integrated precipitable water vapor using ground-based GPS receivers over India

Abstract. We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the PollyXT Raman lidar is 6.56 g kg−1 ± 0.72 g kg−1 (with a statistical uncertainty of 0.08 g kg−1 and an instrumental uncertainty of 0.72 g kg−1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10–20 %.

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Analysis of the performance characteristics of the five-channel Microtops II Sun photometer for measuring aerosol optical thickness and precipitable water vapor

Journal of Geophysical Research Atmospheres ◽

10.1029/2001jd001302 ◽

2002 ◽

Vol 107 (D13) ◽

Cited By ~ 144

Author(s):

Charles Ichoku

Keyword(s):

Water Vapor ◽

Optical Thickness ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Aerosol Optical Thickness ◽

Performance Characteristics ◽

Sun Photometer

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Determination of Zenith Tropospheric Delay and Precipitable Water Vapor using GPS Technology

Mathematical Modelling in Civil Engineering ◽

10.1515/mmce-2016-0007 ◽

2016 ◽

Vol 12 (2) ◽

pp. 21-26 ◽

Cited By ~ 1

Author(s):

Sorin Nistor ◽

Aurelian Stelian Buda

Keyword(s):

Water Vapor ◽

Mapping Function ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Tropospheric Delay ◽

Neutral Atmosphere ◽

Dual Frequency ◽

Gps Receivers ◽

Global Mapping

Abstract In order to be able to be process GPS data, the GPS signal it has to pass the entire terrestrial atmosphere – both neutral atmosphere and ionosphere – which may cause an alteration of the GPS receiver to perform, resulting in large errors in the final position estimate. The dual frequency GPS receivers are affected by the influence of the atmosphere, especially by the troposphere. To estimate the delay caused by the troposphere and to obtain a high degree of accuracy, mapping function has to be used in the estimation process, which opens the door for remote sensing the atmosphere. Because the wet component from the hydrostatic and non-hydrostatic part, is only 10% of the total neutral atmospheric part, its influence is considerate significant in the application of high-precision positioning in which GPS receivers are employed. The article presents the determination of the precipitable water vapor using relative using four permanent GPS stations. The estimation were done by using the Global Mapping Function - GMF and the apriori pressure and temperature from the GPT2 model.

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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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