scholarly journals GPS-based atmospheric precipitable water vapor estimation using meteorological parameters interpolated from NCEP global reanalysis data

2008 ◽  
Vol 113 (D3) ◽  
Author(s):  
Sridevi Jade ◽  
M. S. M. Vijayan
Keyword(s):  
Water Vapor ◽  
Meteorological Parameters ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Global Reanalysis

scholarly journals Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

2016 ◽  
Vol 8 (5) ◽  
pp. 389 ◽  
Author(s):  
Peng Jiang ◽  
Shirong Ye ◽  
Dezhong Chen ◽  
Yanyan Liu ◽  
Pengfei Xia
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Global Reanalysis

scholarly journals Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Qin Zhang ◽  
Junhua Ye ◽  
Shuangcheng Zhang ◽  
Fei Han
Keyword(s):  
Water Vapor ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Reanalysis Dataset ◽  
High Positive Correlation ◽  
Advantages And Disadvantages ◽  
Ecmwf Reanalysis

Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all R2 values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.

scholarly journals Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5566 ◽  
Author(s):  
Qingzhi Zhao ◽  
Xiongwei Ma ◽  
Wanqiang Yao ◽  
Yang Liu ◽  
Zheng Du ◽  
...  
Keyword(s):  
Water Vapor ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Precipitable Water ◽  
Satellite System ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Drought Monitoring ◽  
The Loess Plateau ◽  
Monitoring Index

Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman–Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is −0.66, while that between T and ET for cases of T larger or less than 0 °C are −0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979–2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

scholarly journals Investigation of Precipitable Water Vapor Obtained by Raman Lidar and Comprehensive Analyses with Meteorological Parameters in Xi’an

2018 ◽  
Vol 10 (6) ◽  
pp. 967 ◽  
Author(s):  
Yufeng Wang ◽  
Liu Tang ◽  
Jing Zhang ◽  
Tianle Gao ◽  
Qing Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Meteorological Parameters ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Raman Lidar

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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