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 Constructing Precipitable Water Vapor Map from Regional GNSS Network Observations without Collocated Meteorological Data

2018 ◽  
Author(s):  
Biyan Chen ◽  
Wujiao Dai ◽  
Zhizhao Liu ◽  
Lixin Wu ◽  
Cuilin Kuang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Precipitable Water ◽  
Satellite System ◽  
Precipitable Water Vapor ◽  
Weighted Mean Temperature ◽  
Synoptic Observations ◽  
Gnss Network ◽  
Gnss Data ◽  
Better Than

Abstract. Surface pressure (Ps) and weighted mean temperature (Tm) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from global navigation satellite system (GNSS) data. The lack of Ps or Tm information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate Ps and Tm at the GNSS station using nearby synoptic observations. Ps and Tm obtained at the nearby synoptic sites are interpolated onto the location of GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in Ps and Tm calculation are estimated from ERA-Interim reanalysis profiles. In addition, we present a method of constructing high quality PWV map through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the Ps and Tm retrieval and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby sites in 2015 were processed to extract the PWV so as to subsequently generate PWV map. The retrieved Ps and Tm and constructed PWV maps were assessed by the results derived from radiosonde and ERA-Interim reanalysis. The results show that (1) accuracies of Ps and Tm derived by synoptic interpolation are within the range of 1.7–3.0 hPa and 2.5–3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA reanalysis data with overall accuracy better than 3 mm; and (3) PWV maps can well reveal the moisture advection, transportation and convergence during heavy rainfall.

Topographic amplification of crustal subsidence by the rainwater load of the 2018 heavy rain in SW Japan

2021 ◽  
Author(s):  
Syachrul Arief
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Heavy Rain ◽  
Satellite System ◽  
Rain Gauge ◽  
Precipitable Water Vapor ◽  
Surface Load ◽  
Rain Area ◽  
Movement Data ◽  
Sw Japan

<p>The huge amount of water vapor in the atmosphere caused disastrous heavy rain and floods in early July 2018 in SW Japan. Here I present a comprehensive space geodetic study of water brought by this heavy rain done by using a dense network of Global Navigation Satellite System (GNSS) receivers. </p><p>First, I reconstruct sea level precipitable water vapor above land region on the heavy rain. The total amount of water vapor derived by spatially integrating precipitable water vapor on land was ~25.8 Gt, which corresponds to the bucket size to carry water from ocean to land. I then compiled the precipitation measured with a rain radar network. The data showed the total precipitation by this heavy rain as ~22.11 Gt.</p><p>Next, I studied the crustal subsidence caused by the rainwater as the surface load. The GNSS stations located under the heavy rain area temporarily subsided 1-2 centimeters and the subsidence mostly recovered in a day. Using such vertical crustal movement data, I estimated the distribution of surface water in SW Japan. </p><p>The total amount of the estimated water load on 6 July 2018 was ~68.2 Gt, which significantly exceeds the cumulative on-land rainfalls of the heavy rain day from radar rain gauge analyzed precipitation data. I consider that such an amplification of subsidence originates from the selective deployment of GNSS stations in the concave places, e.g. along valleys and within basins, in the mountainous Japanese Islands.</p>

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 Determination of zenith hydrostatic delays and the development of new global long-term GNSS-derived precipitable water vapor

2016 ◽  
Author(s):  
Xiaoming Wang ◽  
Kefei Zhang ◽  
Suqin Wu ◽  
Changyong He ◽  
Yingyan Cheng ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Pressure ◽  
Meteorological Data ◽  
Accurate Determination ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
Average Value ◽  
Rms Errors

Abstract. Surface pressure is a vital meteorological variable for the accurate determination of precipitable water vapor (PWV) using Global Navigation Satellite Systems (GNSS). The lack of pressure observations is a big issue for the study of climate using historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either an empirical model (e.g. Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g. ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band of −30 to 30° and the average value of Root-Mean-Square (RMS) errors across all the stations in this region is 2.4 mb. Correspondingly, an error of 5.6 mm and 1.0 mm in its resultant zenith hydrostatic delay (ZHD) and PWV is expected. In addition, GPT2w-derived pressure usually has a larger error in the cold season due to large diurnal ranges, which is not considered in the GPT2w model. The average value of the RMS errors of the ERA-Interim-derived pressure across all the 108 stations is 1.1 mb, which will lead to an equivalent error of 2.5 mm and 0.4 mm in its resultant ZHD and PWV respectively. Our research also indicates that the ERA-Interim-derived pressure has the potential to be used as a useful meteorological data source to obtain high accuracy PWV on a global scale for climate studies and the GPT2w-derived pressure can be potentially used for climatology as well although it may be only suitable for the tropical regions.

scholarly journals Estimating trends in atmospheric water vapor and temperature time series over Germany

2017 ◽  
Vol 10 (9) ◽  
pp. 3117-3132 ◽  
Author(s):  
Fadwa Alshawaf ◽  
Kyriakos Balidakis ◽  
Galina Dick ◽  
Stefan Heise ◽  
Jens Wickert
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Time Windows ◽  
Temporal Trends ◽  
Precipitable Water ◽  
Satellite System ◽  
Reanalysis Data ◽  
Mean Value ◽  
Global Navigation Satellite ◽  
Gnss Time Series

Abstract. Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil–Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between −1.5 and 2.3 mm decade−1 with standard deviations below 0.25 mm decade−1. These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade−1 with standard errors below 0.03 mm decade−1. The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the Clausius–Clapeyron equation.

scholarly journals Intercomparison review of IPWV retrieved from INSAT-3DR sounder, GNSS and CAMS reanalysis data

2021 ◽  
Vol 14 (7) ◽  
pp. 4857-4877
Author(s):  
Ramashray Yadav ◽  
Ram Kumar Giri ◽  
Virendra Singh
Keyword(s):  
Water Vapor ◽  
Monsoon Season ◽  
Winter Season ◽  
Precipitable Water ◽  
Satellite System ◽  
Reanalysis Data ◽  
Regional Variability ◽  
Post Monsoon ◽  
Post Monsoon Season ◽  
Gnss Data

Abstract. The spatiotemporal variations of integrated precipitable water vapor (IPWV) are very important in understanding the regional variability of water vapor. Traditional in situ measurements of IPWV in the Indian region are limited, and therefore the performance of satellite and Copernicus Atmosphere Meteorological Service (CAMS) retrievals with the Indian Global Navigation Satellite System (GNSS) as reference were analyzed. In this study the CAMS reanalysis data of 1 year (2018) and the Indian GNSS and INSAT-3DR sounder retrieval data for 1.5 years (January 2017 to June 2018) were utilized, and statistics were computed. It is noticed that seasonal correlation coefficient (CC) values between INSAT-3DR and Indian GNSS data mainly lie within the range of 0.50 to 0.98 for all the selected 19 stations except Thiruvananthapuram (0.1), Kanyakumari (0.31) and Karaikal (0.15) during the monsoon season and Panjim (0.2) during the post-monsoon season. The seasonal CC values between CAMS and GNSS IPWV range from 0.73 to .99 except for Jaipur (0.16) and Bhubaneswar (0.29) during the pre-monsoon season, Panjim (0.38) during the monsoon season, Nagpur (0.50) during the post-monsoon season, and Dibrugarh (0.49) Jaipur (0.58) and Bhubaneswar (0.16) during the winter season. The root mean square error (RMSE) values are higher under the wet conditions (pre-monsoon and monsoon season) than under dry conditions (post-monsoon and winter season), and we found differences in magnitude and sign of bias for INSAT-3DR and CAMS with respect to GNSS IPWV from station to station and season to season. This study will help to improve understanding and utilization of CAMS and INSAT-3DR data more effectively along with GNSS data over land, coastal and desert locations in terms of the seasonal flow of IPWV, which is an essential integrated variable in forecasting applications.

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

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.

scholarly journals Multi-year Precipitable Water Vapor Observations in East Manila, Philippines using Radiosonde and Global Navigation Satellite System

2020 ◽  
Vol 199 ◽  
pp. 00002
Author(s):  
Agana Louisse S. Domingo ◽  
Ernest P. Macalalad
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Global Navigation Satellite System ◽  
Precipitable Water ◽  
Satellite System ◽  
Precipitable Water Vapor ◽  
Type I ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Global Navigation Satellite

Precipitable water vapor (PWV) is a parameter that used to describe the water vapor content in the atmosphere has the potential to become a precipitation. Thus, it is important to measure PWV and investigate its trends and variability for potential forecasting precipitation. This study presents the variation of PWV at Tanay Upper Station (14°34’52.8”N, 121°22’08.9”E) from radiosonde operated by the Philippine Atmospheric, Geophysical and Astronomical Services Administration and at PIMO station (14°38’08.5”N, 121°04’39.4”E) using Global Navigation Satellite System (GNSS) operated by NASAJet Propulsion Laboratory under the International GNSS Service (IGS) network from 2015-2017. Moreover, there is no significant difference (p-values < 0.05) among PWV radiosonde, GNSS-PWV and rainfall as a function of year of observation. Monthly mean variation conforms to the Coronas climate classification, Climate Type I, in terms of the amount of precipitation. It is shown that PWV is high during wet months and least during dry months (November to April). Further, monthly mean variation is moderate correlated with surface temperature from radiosonde (R = +0.589). Evaporation rate depends on the surface temperature, which contributes in forming water vapor. The results showed that PWV from radiosonde gave reasonable values to be considered during wet and dry season as well as the seasonal variation of rainfall.

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