Radiosonde Precipitable Water Vapor Research Articles

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor (PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence, based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV (ERA5-PWV) and the Global Navigation Satellite System-derived PWV (GNSS-PWV) were compared with the reference radiosonde PWV (RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure (P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode (IMTM) with different models (i.e., IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction (NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network (CMOTN). The results show that the root mean square (RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV, and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China

Precipitable water vapor (PWV) with high precision and high temporal resolution estimated by Global Navigation Satellite System (GNSS) is widely used in atmospheric research and weather forecasting. However, most previous works are not consensual concerning the characteristics of the PWV at different time scales and the identification of active and break spells during summ er monsoon climate in Guangxi, China. Taking radiosonde (RS) observations as reference, a strong correlation (R > 0.97) exists between GNSS PWV and RS PWV with a mean root mean square error (RMSE) of 2.68 mm. The annual, seasonal, monthly, and diurnal PWV variations of three years (2017, 2018 and 2020) over Guangxi in were comprehensively investigated using 104 GNSS stations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5). The mean annual bias and RMSE between GNSS PWV and ERA5 PWV are −1.04 mm and 2.63 mm, respectively. The monthly bias and RMSE range are −0.77 to 3.87 mm, 1.32 to 4.45 mm, and the daily range is −1.41 to 1.07 mm and 1.11 to 5.02 mm, respectively. Additionally, the adopted average standardized rainfall anomaly criteria also identified 7/7/3 active spells and 5/3/7 break spells during the summer monsoon (June–September) from 2017 to 2020, respectively. During the three-year period, the daily amplitude ranges for active spells varied from 1.41 to 2.49 mm, 0.69 to 5.4 mm, and 0.88 to 1.41 mm, while the ranges for break spells were 2.45 to 6.76 mm, 1.66 to 8.17 mm, and 1.48 to 2.99 mm, respectively. The results show a superior performance of GNSS PWV compared to ERA5 PWV in Guangxi, and the maximum, minimum and occurrence time of PWV anomaly vary slightly with the season and the topography of stations. Despite temperature primarily exhibiting a negative correlation with rainfall, acting as a dampener, a positive correlation remains evident between PWV and rainfall. Therefore, densely distributed GNSS stations exhibit excellent capabilities in quantifying atmospheric water vapor and facilitating real-time monitoring of small and medium-scale weather phenomena.

Evaluation of oceanic precipitable water vapor products from Microwave Radiation Imager (MWRI) onboard the Fengyun-3D satellite

Precipitable water vapor (PWV) products from the Microwave Radiation Imager (MWRI) onboard the Fengyun-3D (FY-3D), a second-generation polar orbit meteorological satellite of China, have a high spatiotemporal resolution and play an increasingly key role in the atmosphere and climate analysis. We focus on the FY-3D/MWRI PWV products in the global ocean area and comprehensively evaluate their quality by using the coastal radiosonde data from the Integrated Global Radiosonde Archive (IGRA) along with the PWV reanalysis products from the fifth generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5). With respect to the radiosonde data, the correlation coefficient is 0.97, the root mean squared error (RMSE) is 2.71 mm, and the mean bias and mean relative bias is 0.36 mm and 2.57% respectively. The discrepancy between FY-3D/MWRI and radiosonde PWV is smallest at high latitudes and is biggest in the ocean around Africa. The seasonal variation characteristics perform best in spring and worst in summer. We also find that FY-3D/MWRI PWV products are in consistent with the ERA5 PWV, with a RMSE of 2.39 mm and 2.85 mm for daily and monthly data, respectively. Among globally 551,213 daily oceanic PWV grid data, there are 11.3% of matched points, which are mainly located between 15°S and 30°N latitudes, has a RMSE larger than 3 mm, while 68.5% of points have a RMSE of less than 2 mm, meeting the requirements of climate research.

Spatial–Temporal Variability of Global GNSS-Derived Precipitable Water Vapor (1994–2020) and Climate Implications

Precipitable water vapor (PWV) is an important component in the climate system and plays a pivotal role in the global water and energy cycles. Over the years, many approaches have been devised to accurately estimate the PWV. Among them, global navigation satellite systems (GNSS) have become one of the most promising and fastest-growing PWV acquisition methods because of its high accuracy, high temporal and spatial resolution, and ability to acquire PWV in all weather and in near real time. We compared GNSS-derived PWV with a 5 min resolution globally distributed over 14,000 stations from the Nevada Geodetic Laboratory (NGL) from 1994 to 2020 with global radiosonde (RS) data, temperature anomalies, and sea height variations. Then, we examined the temporal and spatial variability of the global PWV and analyzed its climate implications. On a global scale, the average bias and root mean square error (RMSE) between GNSS PWV and RS PWV were ~0.72 ± 1.29 mm and ~2.56 ± 1.13 mm, respectively. PWV decreased with increasing latitude, and the rate of this decrease slowed down at latitudes greater than 35°, with standard deviation (STD) values reaching a maximum at latitudes less than 35°. The global average linear trend was ~0.64 ± 0.81 mm/decade and strongly correlated with temperature and sea height variations. For each 1 °C and 1 mm change, PWV increased by ~2.075 ± 0.765 mm and ~0.015 ± 0.005 mm, respectively. For the time scale, the PWV content peaked ~40 days after the maximum solar radiation of the year (the summer solstice), and the delay was ~40 days relative to the summer solstice.

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

This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWDSVM) and ANN (ZWDANN) is compared with the ZWD obtained from the GPS (ZWDGPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWVSVM, PWVANN) is compared with the radiosonde PWV (PWVradiosonde) and voxel-based PWV (PWVVBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations.

Temporal Analysis of GNSS-Based Precipitable Water Vapor during Rainy Days over the Philippines from 2015 to 2017

Precipitable water vapor (PWV) is a parameter used to estimate water vapor content in the atmosphere. In this study, estimates of PWV from PIMO, PLEG and PPPC global navigation satellite system (GNSS) stations are evaluated regarding the PWV obtained from its collocated radiosonde (RS) stations. GNSS PWV were highly correlated with RS PWV (R ~ 0.97). Mean bias error (MBE) between −0.18 mm and −13.39 mm, and root mean square error (RMSE) between 1.86 mm and 2.29 mm showed a good agreement between GNSS PWV and RS PWV. The variations of PWV are presented. Daily variations of PWV conformed to the daily data of rainfall which agrees to the climate types of Quezon City (Type I), Legaspi (Type II), and Puerto Princesa (Type III) based on the Coronas climate classification. Moreover, PWV monthly variation at all sites is high from May to October (~62 mm) and low from November to April (~57 mm). The relationship between PWV and rainfall at all stations showed positive correlation coefficients between +0.49 to +0.83. Meanwhile, it is observed that when PWV is high (low), its variability is low (high). This study shows the potential of GNSS to study water vapor and its contribution to weather analysis.

Evaluating the Accuracy of Jason-3 Water Vapor Product Using PWV Data From Global Radiosonde and GNSS Stations

Jason-3 is equipped with the Advanced Microwave Radiometer-2 (AMR-2) to account for the zenith wet delay (ZWD) caused by the troposphere in the altimeter signal, from which the precipitable water vapor (PWV) can be deduced. In order to investigate the accuracy of PWV from Jason-3 AMR-2 on a global scale, we adopted PWV observations from 263 radiosonde stations and 103 Global Navigation Satellite System (GNSS) stations as reference PWV. These reference PWVs are recorded during Jason-3 cycles 0-119 and are globally distributed in coastal and island regions. Over 60 000 Jason-3 PWV versus radiosonde PWV comparison points and over 380 000 Jason-3 PWV versus GNSS PWV comparison points are used in this study. For GNSS PWV, two PWV height reduction methods (Kouba empirical method and European Centre for Medium-Range Weather Forecasts (ECMWF) method) are used to reduce the PWV from height of station to sea level. The comparison results indicate that the root-mean-square error (RMSE) of Jason-3 PWV evaluated using radiosonde PWV is 3.4 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Jason-3 PWV has an RMSE of 3.0 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with GNSS PWV derived using ECMWF PWV height correction, while the RMSE between Jason-3 PWV and GNSS PWV derived using Kouba PWV height correction is 3.1 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In addition, the accuracy of Jason-3 PWV increases when the latitude of its footprints or the distance from its footprints to land increases.

PWV Estimation Using GPS and its Comparison with INSAT-3D Rainfall Data

Precipitable water vapor (PWV) plays an important role in understanding the atmosphere and weather. The advancement in Global Navigation Satellite System (GNSS) technology provides the possibility of computing PWV in near real time. In this study, the PWV was computed using the Zenith Tropospheric Delay (ZTD) from Global Positioning System (GPS) data for two International GNSS Service (IGS) stations (IISC & HYDE) for monsoon period (June–September) of 2014 and 2015. The GPS-derived PWV has been validated with reference to radiosonde data and University NAVSTAR Consortium (UNAVCO) PWV products. The coefficient of determination between GPS-derived PWV and radiosonde PWV is 0.73–0.83 for IISC station and 0.76–0.91 for HYDE station. For IISC station, the coefficient of determination between GPS-derived PWV and UNAVCO-derived PWV is 0.80–0.89. Also, in this study, GPS-derived PWV for IISC station has been compared with the rainfall events of Indian National Satellite System (INSAT-3D). From the analysis, we observed a direct relation between PWV and rainfall events from Hydro Estimator Method (HEM), INSAT Multi spectral Rainfall (IMR) products of INSAT-3D. This study highlights the satellite-based observations from GNSS and INSAT satellite for regional weather forecasting in near real time.

Evaluation of precipitable water vapor variation for east mediterranean using GNSS

The study of climate change is an important field of research. Monitoring of atmospheric variability especially the tropospheric precipitable water vapor (PWV) is a powerful way to investigate climate change. Global navigation satellite systems (GNSS) provide a good tool for studying atmospheric parameters as GNSS signals along its path from the satellites to the ground based receivers suffer a significant delay due to the refractivity of earth’s atmosphere. GNSS signals are not only delayed but also refracted in the neutral atmosphere. The zenith wet delays (ZWD) caused by the troposphere can be estimated during the geodetic processing of GNSS signals. Since the ZWD is tightly correlated to the PWV, GNSS observations can be used to study the changes of PWV. In this study GNSS data from the Egyptian permanent GNSS network (EPGN), International GNSS Service (IGS), EUREF Permanent Network (EPN) and Scripps Orbit and Permanent Array Center (SOPAC), for the years 2013 and 2014, were used. These GNSS stations provided a good geometrical coverage over the respective region. All GNSS data were processed using Bernese V 5.2 software. The needed product from GNSS data processing is the tropospheric zenith total delay (ZTD) over each GNSS site. The ZTD is divided based on physical parameters into zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD). The ZWD is the basic observable used to calculate PWV. The values of the PWV were calculated and its variation over the study area was investigated. The quality of calculated PWV values from GNSS data evaluated against traditional Radiosonde (RS) measurements. The results of GNSS PWV showed good agreement with RS PWV when the PWV values were low, but this agreement became worse at high PWV values as the differences between the two techniques increased. The correlation coefficient between RS PWV and GNSS PWV varied from 0.31 to 0.84. Standard deviation of the differences between RS PWV and GNSS PWV ranged from 2.369 to 5.973 mm. The PWV estimated from GNSS observations had annual cycle, and its cycle in 2013 was different from that in 2014. The PWV differences between the 2 years clarified that the water vapor content over the east Mediterranean in most of 2014 days was higher than that in 2013. Furthermore, PWV variations were noted on both temporal and spatial scales. The highest temporal variation value was 25.41 mm whereas the maximum value of the spatial variation was 19.67 mm. The present study illustrated the importance of using geodetic networks to provide atmospheric information.

An Operational Precipitable Water Vapor Retrieval Algorithm for Fengyun-2F/VLSSR Using a Modified Three-Band Physical Split-Window Method

The Visible and Infrared Spin-Scan Radiometer (VISSR) onboard the Fengyun-2 (FY-2) satellite can provide valuable thermal infrared observations to help create a precipitable water vapor (PWV) product with high spatial and temporal resolutions. The current FY-2/VISSR PWV product in operation is produced by using a traditional two-band physical split-window (PSW) method, which produces low quality results under dry atmospheric conditions. Based on the sensitivity characteristics of FY-2F/VISSR water vapor channel and two split-window channels to atmospheric water vapor, this study developed a new, robust operational PWV retrieval algorithm for FY-2F to improve the operational precision of the current PWV product. The algorithm uses a modified three-band PSW method, which adds a scale for the water vapor channel in the improved three-band PSW method. Integrated PWV products from the radiosonde data in 2016 are used here to validate the precision of the PWV retrieved by the modified three-band and traditional two-band PSW methods. The mean bias, root mean square error (RMSE), and correlation coefficient of the PWV retrieved by the modified three-band PSW method are 0.28 mm, 4.53 mm, and 0.969, respectively. The accuracy is much better than the PWV retrieved by the two-band method, whose mean bias, RMSE, and correlation coefficient are 12.67 mm, 29.35 mm, and 0.23. Especially, in mid- or high-latitude regions, the RMSE of the PWV is improved from 10 to 2 mm by changing the inversion in the two-band method to the modified three-band PSW method. Furthermore, the modified three-band PSW results show a better consistency with the radiosonde PWV at any zonal belt and season than the two-band PSW results. This new algorithm could significantly improve the quality of the current FY-2F/VISSR PWV product, especially at sites where the actual PWV are lower than 15 mm.

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

To evaluate the potential of Raman lidar observations for measuring precipitable water vapor (PWV), PWV variations and distribution characteristics were investigated in Xi&rsquo;an (34.233&deg;N, 108.911&deg;E), and its comparisons with meteorological parameters were also analysed. Comparisons of lidar PWV with radiosonde PWV verified the ability and accuracy of using Raman lidars for PWV measurements. The diurnal and monthly variation trends in PWV in different layers are first discussed via the statistical analysis of lidar data from November 2013 to July 2016; different proportions of PWV were found in different layers, and the PWV in each layer presented a slight diurnal change trend and consistent seasonal variation, which was relatively rich in summer, less so in spring and autumn, and relatively deficient in winter. Furthermore, correlation analyses between lidar PWV and meteorological parameters are explored. Water vapor pressure and surface temperature revealed the same inter-seasonal oscillation of PWV, with a correlation coefficient of ~0.90. However, incomplete synchronization was found between PWV and relative humidity and precipitation parameters. Higher humidity appeared in the late summer and the beginning of autumn of each year, which was also the case for precipitation and precipitation efficiency. In addition, atmospheric water vapor density profiles and the obtained PWV by Raman lidar are discussed employing a rainfall case, and a comprehensive analysis with meteorological parameters is undertaken. The intensifying characteristics of vertical change in water vapor and the accumulation of PWV in the lower troposphere can be captured by lidar before the onset of rainfall. In contrast to the obvious diurnal change trend, such meteorological parameters as relative humidity, water vapor pressure, and dew-point temperature difference are accompanied with stable trends with a change rate of close to 0 in the rainfall processes; they also show high correlated variations with lidar PWV. Thus, with the advantage of lidar detection, investigation of water vapor profiles and PWV by Raman lidar, and the comprehensive correlation analyses with synchronic meteorological parameters can prove to be good indications of rainfall.

Evaluation of radiosonde, MODIS-NIR-Clear, and AERONET precipitable water vapor using IGS ground-based GPS measurements over China

Water vapor is one of the major greenhouse gases in the atmosphere and also the key parameter affecting the hydrological cycle, aerosol properties, aerosol-cloud interactions, the energy budget, and the climate. This study analyzed the temporal and spatial distribution of precipitable water vapor (PWV) in China using MODerate resolution Imaging Spectroradiometer near-infrared (MODIS-NIR)-Clear PWV products from 2011 to 2013. We then compared the four PWV products (Global Positioning System PWV (GPS-PWV), radiosonde PWV (RS-PWV), MODIS-NIR-Clear PWV, and Aerosol Robotic Network sunphotometer PWV (AERONET-PWV)) at six typical sites in China from 2011 to 2013. The analysis of the temporal and spatial distribution showed that the PWV distribution in China has clear geographical differences, and its basic distribution characteristics gradually change from the coast in the southeast to inland in the northwest. Affected by the East Asian monsoon, the PWV over China showed clear seasonal distribution features, with highest values in the summer, followed by autumn and spring, and the lowest values in winter. Intercomparison results showed that GPS-PWV and RS-PWV had a slightly higher correlation (R2=0.975) at 0000 UTC than that at 1200 UTC (R2=0.967). The mean values of Bias, SD, and RMSE between GPS-PWV and RS-PWV (GPS-RS) were −0.03mm, 2.36mm, and 2.60mm at 0000 UTC, and −0.23mm, 2.76mm, and 2.95mm at 1200 UTC, respectively. This showed that GPS-PWV was slightly lower than RS-PWV, and this difference was more obvious during the nighttime. The MODIS-NIR-Clear PWV product showed a similar correlation coefficient (R2=0.88) with GPS-PWV compared with RS-PWV. In addition, MODIS-NIR-Clear PWV was greater than GPS-PWV and RS-PWV. The MODIS-NIR-Clear PWV showed a larger deviation from GPS-PWV (MODIS-GPS Bias=1.50mm, RMSE=5.76mm) compared with RS PWV (MODIS-RS Bias=0.75mm, RMSE=5.31mm). The correlation coefficients between AERONET-PWV and the PWV from GPS, RS, and MODIS-NIR-Clear were 0.970, 0.963, and 0.923 (with RMSE of 2.53mm, 3.67mm, and 4.39mm), respectively. In the Beijing area, the overall mean bias of the AERONET-PWV product with GPS-PWV, RS-PWV and MODIS-NIR-Clear PWV was −0.09mm, −1.82mm, and −1.54mm, respectively, which shows that the AERONET-PWV product was lower than the other three PWV products.

An improved physical split-window algorithm for precipitable water vapor retrieval exploiting the water vapor channel observations

This paper presents a new atmospheric precipitable water vapor (PWV) retrieval method based on three thermal infrared band observations from geostationary satellites. The proposed method is similar to the traditional physical split-window (PSW) retrieval technique, but a water vapor channel observation near 6.7μm was included. Sensitivity analyses and simulation retrievals were carried out respectively according to the instrument characteristics of the Stretched Visible and Infrared Spin Scan Radiometer onboard FengYun-2G (SVISSR/FY-2G) and the Moderate Resolution Imaging Spectroradiometer aboard Terra (MODIS/Terra). The results indicate that the proposed 3-band algorithm can significantly reduce PWV retrieval errors caused by surface emissivity uncertainty and observation errors, especially in dry atmospheric conditions (i.e., PWV<2cm). The proposed algorithm was validated using SVISSR/FY-2G and MODIS/Terra observations, and was compared with radiosonde and GPS PWV. The determination coefficient (R2), root mean square error (RMSE), and bias between the SVISSR retrieved PWV and the radiosonde PWV are 0.87, 0.43cm and 0.14cm, respectively. The R2, RMSE and bias of the MODIS retrieved PWV are 0.89, 0.10cm and −0.042cm, respectively, which are slightly better than the MODIS L2 thermal infrared and near-infrared PWV products.

Trends and Variability in Precipitable Water Vapor throughout North China from 1979 to 2015

This study analyzed the variability and trends in precipitable water vapor (PWV) in North China from 1979 to 2015. The spatial distribution of annual mean PWV was generally characterized by two high PWV centers in Eastern China and the Tarim Basin and two low PWV centers in Northern Tibet and Qinghai Province and in Inner Mongolia. The levels of seasonal mean PWV were highest in summer, followed by autumn and spring, and lowest in winter. The maximum monthly mean PWV occurred in July and August, while the minimum occurred in December to February. Increasing trends in PWV, with the trend magnitude ranging from 0.1 to 1.2 mm decade−1 over North China, were observed in the radiosonde, ERA-interim, and MERRA-2 PWV data from 1979 to 1999; but a slightly decreasing trend of −0.4 mm decade−1 from radiosonde was found in most regions of North China from 1979 to 2007. A monotonically increasing PWV trend was detected throughout North China between 1979 and 1999, with the maximum trend occurring in summer and the minimum occurring in winter. For the period of 1979–2007, a slightly but less marked decreasing trend was found at most stations in North China in all four seasons.

Comparison of GPS precipitable water vapor and meteorological parameters during rainfalls in Tehran

In this study, the sensing of water vapor using the global positioning system (GPS) was investigated in Tehran. Water vapor mean temperature T m is a key parameter in conversion of GPS zenith wet delay to precipitable water vapor (PWV). Then, by using 8 years of radiosonde and surface temperature data, we achieved a new site-specific T m model in Tehran. After the comparison of Bevis et al. (J Geophys Res 97(D14):15787–15801, 1992) and the site-specific models, a mean bias error of −1.3 K was found for Bevis model, while this is less than 0.1 K for the new local model. Therefore, PWV time series were generated for the ground-based GPS site in Tehran from the beginning of 2005 to the end of 2013. Comparing the GPS with the radiosonde PWV, it was shown that the GPS can be used to measure the PWV with high precision. The root mean square error for differences between the GPS and radiosonde was derived as 1.5 mm. The PWV has significant relationship with precipitation in our case study. The thresholds of the relative humidity anomaly with different PWV anomaly related to rainfall occurrences are also considered in this research. The analyses show that use of PWV anomaly condition together with surface meteorological parameters reduces the number of false rainfall recognitions significantly.

Radiosonde Sensors Bias in Precipitable Water Vapor From Comparisons With Global Positioning System Measurements

In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, for the years of 2006, 2008, 2010, and analyzed the radiosonde seasonal, diurnal bias according to radiosonde sensor types. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. Overall, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the nighttime except for 2006 winter, and in comparison for summer, RS92-SGP sensor showed the highest quality.

Validation of GPS PWV over UKM Bangi Malaysia for Climate Studies

This paper reported the tropospheric precipitable water vapor (PWV) validation as an essential tool for solar-climate studies over tropical region. The PWV is measured using a ground-based GPS receiver and the surface meteorological sensors. Validation of GPS PWV with radiosonde PWV along 3-week observation in November 2009 over UKM Bangi, Malaysia showed a similar trend with correlation coefficient 0.79, significantly at the 99% confidence level. The GPS PWV rate was measured lower below 1% than the radiosonde PWV and has similar characters. This implies that GPS PWV agreed very well with twice a day of radiosonde data that can be employed to monitor atmospheric variability as well as climate studies. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Bina Nusantara University.

A Study of the Atmospheric Weighting Mean Temperature in Northwest Plateau of China

The data from 11 meteorological radiosonde stations in 5 provinces including Shanxi, Shaanxi, Ningxia, Inner Mongolia and Hebei are divided into 9 different data collections which are used to deduce the linear regression models of atmospheric weighting mean temperature (Tm) for Ground-based GPS precipitable water vapor (PWV) retrieval. These 9 models, together with Bevis model, are used to retrieve the GPS PWV at station BGTY. In comparison with the correlations between the ground-based GPS PWV and radiosonde PWV at this station, the difference between these 10 different models of Tm is analyzed. The results show that the Bevis model of Tm can be used to retrieve the GPS PWV of the regions mentioned above. At the same time, the Tm model computed from the radiosonde measurements of specific regions and seasons can provide more accurate GPS PWV than the Bevis model.

Analysis of Radiosonde Daily Bias by Comparing Precipitable Water Vapor Obtained from Global Positioning System and Radiosonde

In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, from 0000 UTC, June 1, 2007 to 1200 UTC, May 31, 2009, and analyzed the radiosonde bias between the day and the night. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. In addition, for all the rainfall events, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased was significantly less distinctive in nighttime than in daytime. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the second year, regardless of nighttime or daytime rainfall, and the non-rainfall root mean square error (RMSE) was similar to that of the previous studies, while the rainfall RMSE was larger to a certain extent.

GPS 가강수량 결정을 위한 한국형 평균온도식 비교

평균온도식은 GPS 기상정보 산출에 핵심요소로서, GPS 기반 가강수량의 정확도 향상을 위해서는 지역형 평균온도식을 사용해야 한다. 이 연구에서는 Ha & Park(2008)에서 제안된 4가지 한국형 평균온도식인 HP, <TEX>$HP_M,\;HPt_Y$</TEX>, 그리고 <TEX>$HPt_M$</TEX>을 이용하여 각 모델이 GPS 가강수량 결정에 미치는 영향을 분석하였다. 정확도 검증을 위해 속초기상대의 라디오존데에서 관측된 가강수량과 비교하였다. 그 결과 GPS와 라디오존데의 가강수량은 4개 한국형 평균온도식 모두 유사한 경향을 보였으며, 대략 3mm수준의 편향과 3.6mm수준의 편향 제거 RMS가 산출되었다. 특히 지상기온 관측자료 없이 사용 가능한 <TEX>$HPt_Y$</TEX>과 <TEX>$HPt_M$</TEX>을 이용하여도 HP나 <TEX>$HP_M$</TEX>와 동일한 수준의 정확한 가강수량 산출이 가능하였다. 한편, 라디오존데 관측치에 존재하는 건조편향을 분석하기 위하여 속초기상대 라디오존데의 장비특성 및 관측시기에 따른 분석을 수행하였다. 결과 라디오존데 장비가 RS80-15L에서 GRS DFM-06으로 교체된 후 건조편향이 낮(KST 09시)과 밤(KST 21시)에 각각 18.2%와 16.1% 감소한 것으로 나타났다. The mean temperature equation is a key factor in calculating GPS meteorological information. A local mean temperature equation should be used to improve accuracy of GPS PWV (Precipitable Water Vapor). In this paper, four local mean temperature equations, HP, <TEX>$HP_M,\;HPt_Y,\;and\;HPt_M$</TEX> from Ha & Park (2008) were used to analyze the effects of local models in determining GPS PWV. Four different sets of GPS PWVs were compared with radiosonde PWV to validate the accuracies of local models. GPS PWVs of four local models have similar trends compared against radiosonde PWV. The bias and RMS error were the same level: the bias is <TEX>${\sim}3mm$</TEX> and the RMS is <TEX>${\sim}3.6mm$</TEX> after the bias was removed. Especially, with <TEX>$HPt_Y\;and\;HPt_M$</TEX> models one can obtain accurate PWVs even without surface temperature measurements. And we investigated dry bias of radiosonde measurements depending on sensor types and observation time at Sokcho weather station. After the radiosonde sensor equipment was changed from RS80-15L to GRS DFM-06, dry bias of radiosonde PWV decreased about 18.2% during daytime (KST 09:00), and 16.1% during nighttime (KST 21:00).