Analysis of the Precipitable Water Vapor Observation in Yunnan–Guizhou Plateau during the Convective Weather System in Summer

The ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts (ECMWF) in the summers from 2015 to 2020 was used to compare and analyze the features of the precipitable water vapor (PWV) observed by six ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in the Yunnan–Guizhou Plateau. The correlation coefficients of the two datasets ranged between 0.804 and 0.878, the standard deviations ranged between 4.686 and 7.338 mm, and the monthly average deviations ranged between −4.153 and 9.459 mm, which increased with the altitude of the station. Matching the quality-controlled ground precipitation data with the PWV in time and space revealed that most precipitation occurred when the PWV was between 30 and 65 mm and roughly met the normal distribution. We used the vertical integral of divergence of moisture flux (∇p) and S-band Doppler radar networking products combined with the PWV to study the convergence and divergence process and the water vapor delivery conditions during the deep convective weather process from August 24 to 26, 2020, which can be used to analyze the real-time observation capability and continuity of PWV in small-scale and mesoscale weather processes. Furthermore, the 1 h precipitation and the cloud top temperature (ctt) data at the same site were used to demonstrate the effect of PWV on the transit of convective weather systems from different time−space scales.

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

Journal of Sensors ◽

10.1155/2018/3428303 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 6

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.

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A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i2.pp744-753 ◽

2021 ◽

Vol 24 (2) ◽

pp. 744

Author(s):

Wayan Suparta ◽

Aris Warsita ◽

Ircham Ircham

Keyword(s):

Water Vapor ◽

Meteorological Data ◽

Low Cost ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Weather Station ◽

Automatic Weather Station ◽

Weather System ◽

Spatial And Temporal Scales

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I<sup>2</sup>C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

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Evaluation and Calibration of MODIS Near-Infrared Precipitable Water Vapor over China Using GNSS Observations and ERA-5 Reanalysis Dataset

Remote Sensing ◽

10.3390/rs13142761 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2761

Author(s):

Dantong Zhu ◽

Kefei Zhang ◽

Liu Yang ◽

Suqin Wu ◽

Longjiang Li

Keyword(s):

Water Vapor ◽

Near Infrared ◽

Grid Point ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

Calibration Model ◽

Systematic Bias ◽

Reanalysis Dataset ◽

The Mean

Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China.

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Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

Atmosphere ◽

10.3390/atmos12121698 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1698

Author(s):

Zofia Baldysz ◽

Grzegorz Nykiel ◽

Beata Latos ◽

Dariusz B. Baranowski ◽

Mariusz Figurski

Keyword(s):

Water Vapor ◽

Southern Oscillation ◽

Linear Trend ◽

Regional Climate ◽

Singular Spectrum Analysis ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Small Scale ◽

Climate Indices ◽

Single Station

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

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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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