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

Sharifi, Mohammad Ali; Khaniani, Ali Sam; Joghataei, Mohammad Taghi

doi:10.1007/s00703-015-0383-3

Cited by 30 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PWV is defined as the vertically integrated water vapor column (units: mm) within a layer and can be calculated as [6]:…”

Section: Rereieval Methods For Lidar Datamentioning

confidence: 99%

“…Ground-based GPS observations have high temporal resolution, long-term stability, and are independent of all weather conditions. The accuracy of estimated PWV obtained from ground-based GPS observations has been investigated in many studies [6][7][8]. Sun photometers are an important tool for atmospheric research and meteorological observations and have advantages in studying the optical and physical properties of aerosol particles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

View full text Add to dashboard Cite

To evaluate the potential of Raman lidar observations for measuring precipitable water vapor (PWV), PWV variations and distribution characteristics were investigated in Xi'an (34.233 • N, 108.911 • 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.

show abstract

“…PWV is defined as the vertically integrated water vapor column (units: mm) within a layer and can be calculated as [6]:…”

Section: Rereieval Methods For Lidar Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Li et al [13] suggested that meteorological parameters like temperature along with PWV could be useful for prediction of rainfall. Similarly, Sharifi et al [14] proposed to use the relative humidity anomaly along with the PWV anomaly values to improve the prediction accuracy of rainfall events. In the literature, there are only a few papers that combine different weather features and use a machine-learning based methodology for rainfall prediction [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach for Accurate Rainfall Prediction

Manandhar

Dev

Lee

et al. 2019

IEEE Trans. Geosci. Remote Sensing

114

View full text Add to dashboard Cite

In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

show abstract

“…However, luminous images at salt marshes and low accuracy in the spatial resolution in the microwave bands present a solution combining both of these two bands to achieve the desired result with remote sensing data. Moreover, some investigations have been conducted to improve the precision and correlation of the parameters involved in estimating precipitable water vapor [15][16][17], such as the effects of climate change on precipitable water vapor [18,19], or the correlation of the maximum monthly precipitable water vapor with precipitation [20,21].…”

Section: Introductionmentioning

confidence: 99%

Forecasting probable maximum precipitation using innovative algorithm to estimate atmosphere precipitable water vapor

Mohammadkhani¹,

Shishegaran²,

Shokrollahi³

2019

Math. models, eng.

View full text Add to dashboard Cite

Total Precipitable Water Vapor (TPW) has an impact on many atmospheric and hydrological processes which can be calculated by the spatial and temporal resolution of weather conditions. Moreover, precipitable water vapor plays a significant role in predicting the weather so that climate change can be constantly monitored by spatial and temporal variations. Water vapor is one of the most abundant greenhouse gases that has an increasing effect on the heat of the earth. Therefore, zonation of precipitable water vapor map in global scale improves the understanding of hydrologists from the hydrological cycle, Earth and atmosphere reactions, the energy cost, and climate change through greenhouse gas emissions. The complex reactions between water vapor, aeroes and clouds, and difficulties in estimating their true amounts make it impossible to evaluate the effect of water vapor on heightening the heat generated by greenhouse gases. One of the most common methods for estimating the precipitable water vapor is the use of remote sensing technique since satellite images are captured continuously within a spatial area. The most crucial advantage of estimating precipitable water vapor by using microwave data over other methods such as optical data is its application and availability on cloudy days. Since microwaves are capable of crossing the clouds, algorithms developed based on them remain functional, whereas optical-based algorithms do not show appropriate performance on the cloudy days. In this study, the efficiency of the remote sensing microwave data in estimating precipitable water vapor parameter has been evaluated in different areas of Iran in order to achieve an algorithm which can predict the desired parameter precisely at spatial resolution and within extreme weather conditions as well as drought.

show abstract

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

Cited by 30 publications

References 27 publications

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

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

A Data-Driven Approach for Accurate Rainfall Prediction

Forecasting probable maximum precipitation using innovative algorithm to estimate atmosphere precipitable water vapor

Contact Info

Product

Resources

About