scholarly journals Tropospheric Refractivity Profile Estimation by GNSS Measurement at China Big-Triangle Points

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1468
Author(s):  
Xiang Dong ◽  
Fang Sun ◽  
Qinglin Zhu ◽  
Leke Lin ◽  
Zhenwei Zhao ◽  
...  
Keyword(s):  
Refractive Index ◽  
Water Vapor ◽  
Vapor Pressure ◽  
Water Vapor Pressure ◽  
Pressure Profile ◽  
Radiosonde Data ◽  
Machine Method ◽  
Zenith Wet Delay ◽  
Comparison Results ◽  
Temperature And Pressure

Atmospheric radio refractivity has an obvious influence on the signal transmission path and communication group delay effect. The uncertainty of water vapor distribution is the main reason for the large error of tropospheric refractive index modeling. According to the distribution and characteristics of water vapor pressure, temperature, and pressure, which are the basic components of the refractive index, a method for retrieving atmospheric refractivity profile based on GNSS (Global Navigation Satellite System) and meteorological sensor measurement is introduced and investigated in this study. The variation of the correlation between zenith wet delay and water vapor pressure is investigated and analyzed in detail. The partial pressure profiles of water vapor are retrieved with relevance vector machine method based on tropospheric zenith wet delay calculated by single ground-based GPS (Global Positioning System) receiver. The atmospheric temperature and pressure is calculated with the least square method, which is used to fit the coefficients of the polynomial model based on a large number of historical meteorological radiosonde data of local stations. By combining the water vapor pressure profile retrieving from single ground-based GPS and temperature and pressure profile from reference model, the refractivity profile can be obtained, which is compared to radiosonde measurements. The comparison results show that results of the proposed method are consistent with the results of radiosonde. By using over ten years’ (through 2008 to 2017) historical radiosonde meteorological data of different months at China Big-Triangle Points, i.e., Qingdao, Sanya, Kashi, and Jiamusi radiosonde stations, tropospheric radio refractivity profiles are retrieved and modeled. The comparison results present that the accuracies of refractivity profile of the proposed method at Qingdao, Sanya, Kashi, and Jiamusi are about 5.48, 5.63, 3.58, and 3.78 N-unit, respectively, and the annual average relative RMSE of refractivity at these stations are about 1.66, 1.53, 1.49, and 1.23%, respectively.

scholarly journals A Refined Regional Model for Estimating Pressure, Temperature, and Water Vapor Pressure for Geodetic Applications in China

2020 ◽  
Vol 12 (11) ◽  
pp. 1713
Author(s):  
Junyu Li ◽  
Bao Zhang ◽  
Yibin Yao ◽  
Lilong Liu ◽  
Zhangyu Sun ◽  
...  
Keyword(s):  
Water Vapor ◽  
Vapor Pressure ◽  
Meteorological Parameters ◽  
Water Vapor Pressure ◽  
Regional Model ◽  
Radiosonde Data ◽  
New Model ◽  
Gnss Positioning ◽  
Global Pressure ◽  
Gpt2w Model

Pressure, temperature, and water vapor pressure are basic meteorological parameters that are frequently required in Global Navigation Satellite System (GNSS) positioning/navigation and GNSS meteorology. Although models like Global Pressure and Temperature (GPT) and Global Pressure and Temperature 2 wet (GPT2w) were developed for these demands, their spatial resolutions are lower than 0.75° and temporal resolutions are below 6 h, which limits their achievement. The publication of European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 hourly 0.25° × 0.25° data offers the opportunity to lift this limitation. In this work, the ERA5 surface data are used to evaluate the temporal variabilities of pressure, temperature, and water vapor pressure in the area of China. We characterize their diurnal variations using hourly data and take into account their geographical variations by 0.25° × 0.25° grids. In addition, we improve the height corrections for the three parameters employing the ERA5 pressure level data. Through these efforts, we build a new regional model named Chinese pressure, temperature, and water vapor pressure (CPTw), which has the advanced resolution of 0.25° × 0.25° and temporal resolution of 1 h. We evaluate the performance using ERA5 data and radiosonde data compared with the approved GPT2w model. Results demonstrate that the accuracies of the new model are superior to the GPT2w model in all meteorological parameters. The validation with the radiosonde data shows RMS for pressure, temperature, and water vapor pressure of the CPTw model is reduced by 14.1%, 25.8%, and 4.8%, compared with that of the GPT2w model. The new model catches especially well the diurnal changes in pressure, temperature, and water vapor pressure, which have never been realized before. Since the CPTw model can provide accurate empirical pressure, temperature, and water vapor pressure for any time and location in China and its surrounding areas, it can not only meet the need of empirical meteorological parameters in real-time geodetic applications like GNSS positioning and navigation, but it is also useful for GNSS meteorology.

scholarly journals Assessment of COSMIC radio occultation retrieval product using global radiosonde data

2012 ◽  
Vol 5 (6) ◽  
pp. 8405-8434
Author(s):  
B.-R. Wang ◽  
X.-Y. Liu ◽  
J.-K. Wang
Keyword(s):  
Water Vapor ◽  
Vapor Pressure ◽  
Relative Error ◽  
Radio Occultation ◽  
Water Vapor Pressure ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Cosmic Radio ◽  
Horizontal Distance ◽  
Matching Criteria

Abstract. The radio occultation retrieval product of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation sounding system was verified using the global radiosonde from 2007 to 2010. 4 yr of samples were used to collect quantities of data using much stricter matching criteria than previous studies to obtain more accurate results. The horizontal distance between the radiosonde station and the occultation event is within 100 km, and the time window is 1 h. The comparison was performed from 925 hPa to 10 hPa. The results indicated that the COSMIC's temperature data agreed well with the radiosonde data. The global mean temperature bias was −0.09 K, with a standard deviation (SD) of 1.72 K. The water vapor pressure of COSMIC showed a systematic bias in relation to radiosonde in higher layers. The mean specific humidity bias of 925–200 hPa is about −0.011 g kg−1, with a SD of about 0.662 g kg−1. The COSMIC quality control process could not detect some abnormal extremely small humidity data which occured frequently in subtropical zone. Despite the large relative error of water vapor pressure, the relative error of refractivity is small. This paper also provides a comparison of eight radiosonde types with COSMIC product. Because the retrieval product is affected by the background error which differed between different regions, the COSMIC retrieval product could be used as a benchmark if the precision requirement is not strict.

scholarly journals Assessment of COSMIC radio occultation retrieval product using global radiosonde data

2013 ◽  
Vol 6 (4) ◽  
pp. 1073-1083 ◽  
Author(s):  
B.-R. Wang ◽  
X.-Y. Liu ◽  
J.-K. Wang
Keyword(s):  
Water Vapor ◽  
Vapor Pressure ◽  
Relative Error ◽  
Radio Occultation ◽  
Water Vapor Pressure ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Cosmic Radio ◽  
The Mean ◽  
Matching Criteria

Abstract. The radio occultation retrieval product of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Radio Occultation sounding system was verified using the global radiosonde data from 2007 to 2010. Samples of 4 yr were used to collect quantities of data using much stricter matching criteria than previous studies to obtain more accurate results. The horizontal distance between the radiosonde station and the occultation event is within 100 km, and the time window is 1 h. The comparison was performed from 925 hPa to 10 hPa. The results indicated that the COSMIC's temperature data agreed well with the radiosonde data. The global mean temperature bias was −0.09 K, with a standard deviation (SD) of 1.72 K. According to the data filtration used in this paper, the mean specific humidity bias of 925–200 hPa is −0.012 g kg−1, with a SD of 0.666 g kg−1, and the mean relative error of water vapor pressure is about 33.3%, with a SD of 107.5%. The COSMIC quality control process failed to detect some of the abnormal extremely small humidity data which occurred frequently in subtropical zone. Despite the large relative error of water vapor pressure, the relative error of refractivity is small. This paper also provides a comparison of eight radiosonde types with COSMIC product. Because the retrieval product is affected by the background error which differed between different regions, the COSMIC retrieval product could be used as a benchmark if the precision requirement is not strict.

Estimation of Missing GPS Precipitable Water Vapor Data by Zenith Wet Delay and Meteorological Data

2014 ◽  
Vol 931-932 ◽  
pp. 703-708
Author(s):  
Prawit Uang-Aree ◽  
Sununtha Kingpaiboon ◽  
Kulyakorn Khuanmar
Keyword(s):  
Water Vapor ◽  
Linear Regression ◽  
Vapor Pressure ◽  
Surface Temperature ◽  
Meteorological Data ◽  
Water Vapor Pressure ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Dew Point ◽  
Zenith Wet Delay

This article presents a statistical correlation between GPS precipitable water vapor and meteorological data, i.e., surface temperature, air pressure, relative humidity, dew point temperature, and water vapor pressure by using linear regression. The data, recorded over a 4-year period, was used as an estimation of missing GPS precipitable water vapor data from discontinuous recordings. A multiple linear regression equation showed a correlation among zenith wet delay (ZWD), water vapor pressure (e) and surface temperature (T) was ZWD(e,T) = 17.4952e-0.8281T-93.164, with a coefficient of determination (R2) of 0.725, a mean absolute error of 8.71 mm, a root mean square error of 10.39 mm, and a mean absolute percentage error of 18.63%. The equation obtained can be used to estimate GPS precipitable water vapor data which is missing from recordings due to accident or technological error.

scholarly journals Water vapor pressure trends in south and southwest Iran

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 335-348
Author(s):  
YOUNES KHOSRAVI ◽  
HASAN LASHKARI ◽  
HOSEIN ASAKEREH
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Vapor Pressure ◽  
Water Vapor Pressure ◽  
Sen’S Slope Estimator ◽  
Sen’S Slope ◽  
Ground Station ◽  
Mk Test ◽  
Slope Estimator ◽  
The Persian Gulf

Recognitionanddetectionofclimaticparameters inhave animportant role inclimate change monitoring. In this study, the analysis of oneofthe most importantparameters, water vapor pressure (WVP), was investigated. For this purpose, two non-parametric techniques, Mann-Kendall and Sen's Slope Estimator, were used to analyze the WVP trend and to determine the magnitude of the trends, respectively. To analyze these tests, ground station observations [10 stations for period of 44 years (1967-2010)] and gridded data [pixels with the dimension of 9 × 9 km over a 30-year period (1981-2010)] in South and SouthwestofIran were used. By programming in MATLAB software, the monthly, seasonal and annual WVP time series were extracted and MK and Sen's slope estimator tests were done. The results of monthly MK test on ground station observations showed that the significant downward trends are more considerable than significant upward trends. It also showed that the WVP highest frequency was more in warm months, April to September and the highest frequency of significant trends slope was in February and May. The spatial distribution of MK test of monthly gridded WVP time series showed that the upward trends were detected mostly in western zone and near the Persian Gulf in August. On the other hand, the downward trends through months. The maximum and minimum values of positive trends slope occurred in warm months and cold months, respectively. The analysis of the MK test of the annual WVP time series indicated the upward significant trends in the southeast and southwest zones of study area.  

Sign in / Sign up

Export Citation Format

Share Document