Monitoring of the besieged water vapor on the basis of the processing of GNSS data

2011 ◽  
Vol 17 (4) ◽  
pp. 65-73 ◽  
Author(s):  
N.I. Kablak ◽  
Keyword(s):  
Water Vapor ◽  
Gnss Data

METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

2021 ◽  
pp. 273
Author(s):  
Syachrul Arief ◽  
Ihsan Muhamad Muafiry
Keyword(s):  
Water Vapor ◽  
Data Processing ◽  
Average Distance ◽  
Heavy Rain ◽  
Research Area ◽  
High Rainfall ◽  
Interesting Pattern ◽  
Surrounding Areas ◽  
Gnss Data ◽  
Second Peak

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

Intercomparison and Validation of GNSS-IWV Derived with G-Nut and Bernese Software

2017 ◽  
Author(s):  
Pawel Golaszewski ◽  
Pawel Wielgosz ◽  
Katarzyna Stepniak
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Severe Weather ◽  
Final Solution ◽  
Weather Forecasts ◽  
Software Packages ◽  
Gnss Measurements ◽  
Gnss Data ◽  
Permanent Station

GNSS is an important source of meteorological data. GNSS measurements can provide tropospheric Zenith Wet Delays (ZWD) over wide area covered with permanent stations. In addition, when using surface synoptical data, GNSS can provide Integrated Water Vapor (IWV) which is very valuable information utilized in weather forecasts and severe weather monitoring. Hence, there is a need to test and validate various algorithms and software used for ZWD estimation. In this research, the accuracy of the ZWD estimates was tested using two different software packages: Bernese GNSS Software v.5.2 and G-Nut/Tefnut. In addition, their computational load was evaluated. The GNSS data were obtained from POTS permanent station, which is located in Potsdam, Germany. To validate the estimation results, the derived ZWD was transformed into the IWV, and afterwards compared to the reference IWV measured by the collocated Microwave Radiometer. In addition, the ZWD estimates were also compared to the EUREF final solution.

scholarly journals Validating HY-2A CMR Precipitable Water Vapor Using Ground-based and Shipborne GNSS Observations

2020 ◽  
Author(s):  
Zhilu Wu ◽  
Yanxiong Liu ◽  
Yang Liu ◽  
Jungang Wang ◽  
Xiufeng He ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
High Temporal Resolution ◽  
Satellite Systems ◽  
The Indian Ocean ◽  
Global Navigation Satellite ◽  
Gnss Data ◽  
Gnss Observations

Abstract. The calibration microwave radiometer (CMR) onboard Haiyang-2A satellite provides wet tropospheric delays correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. Ground-based Global Navigation Satellite Systems (GNSS) provide precise PWV with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 IGS stations along the coastline and 56-day shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made to the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm in RMS within 100 km. Geographically, the RMS is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an RMS of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well with no obvious system error.

scholarly journals Validation of GNSS data about the integrated water vapor in Europe using sun photometers

2019 ◽  
Vol 55 (4) ◽  
pp. 58-63
Author(s):  
V. V. Kalinnikov ◽  
O. G. Khutorova
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Standard Deviation ◽  
Seasonal Course ◽  
Standard Deviations ◽  
Gnss Data

In the article the comparison of time series of integrated water vapor (IWV) for 2015-2017 at 8 pair stations of GNSS and solar photometers of AERONET network in Europe is carried out. The distance between pairs of stations didn’t exceed 20 km. It is shown that bias and standard deviations of divergences have the seasonal course. In the winter GNSS-photometer bias was from –0.61 to 0.34 mm. In the summer the GNSS overestimates IWV relative to photometers by values from 0.52 to 2.26 mm. The standard deviation is maximal in summer and is from 1.31 to 1.64 mm, in winter it decreases to 0.49-0.86 mm that is 5-6% of IWV.

scholarly journals GNSS-based water vapor estimation and validation during the MOSAiC expedition

2021 ◽  
Author(s):  
Benjamin Männel ◽  
Florian Zus ◽  
Galina Dick ◽  
Susanne Glaser ◽  
Maximilian Semmling ◽  
...  
Keyword(s):  
Water Vapor ◽  
Very Long Baseline Interferometry ◽  
Precise Point Positioning ◽  
Radiosonde Data ◽  
Weather Data ◽  
Radio Telescopes ◽  
Long Baseline ◽  
Gnss Data ◽  
Level Of Agreement ◽  
Good Agreement

Abstract. Within the transpolar drifting expedition MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate), GNSS was used among other techniques to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked GNSS data including GPS, GLONASS, and Galileo, epoch-wise coordinates and hourly zenith total delays (ZTD) were determined using a kinematic precise point positioning (PPP) approach. The derived ZTD values agree to 1.1 ± 0.2 mm (RMS of the differences 10.2 mm) with the numerical weather data of ECMWF’s latest reanalysis, ERA5, computed for the derived ship’s locations. This level of agreement is also confirmed by comparing the on-board estimates with ZTDs derived for terrestrial GNSS stations in Bremerhaven and Ny Ålesund and for the radio telescopes observing Very Long Baseline Interferometry in Ny Ålesund. Preliminary estimates of integrated water vapor derived from frequently launched radiosondes are used to assess the GNSS-derived integrated water vapor estimates. The overall difference of 0.08 ± 0.04 kg m−2 (RMS of the differences 1.47 kg m−2) demonstrates a good agreement between GNSS and radiosonde data. Finally, the water vapor variations associated with two warm air intrusion events in April 2020 are assessed.

scholarly journals An improved pixel-based water vapor tomography model

2019 ◽  
Vol 37 (1) ◽  
pp. 89-100
Author(s):  
Yibin Yao ◽  
Linyang Xin ◽  
Qingzhi Zhao
Keyword(s):  
Water Vapor ◽  
Three Dimensional ◽  
Satellite System ◽  
Reference Station ◽  
Weather Forecasts ◽  
Satellite Positioning ◽  
Optimal Polynomial ◽  
Improved Model ◽  
Global Navigation Satellite ◽  
Gnss Data

Abstract. As an innovative use of Global Navigation Satellite System (GNSS), the GNSS water vapor tomography technique shows great potential in monitoring three-dimensional water vapor variation. Most of the previous studies employ the pixel-based method, i.e., dividing the troposphere space into finite voxels and considering water vapor in each voxel as constant. However, this method cannot reflect the variations in voxels and breaks the continuity of the troposphere. Moreover, in the pixel-based method, each voxel needs a parameter to represent the water vapor density, which means that huge numbers of parameters are needed to represent the water vapor field when the interested area is large and/or the expected resolution is high. In order to overcome the abovementioned problems, in this study, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) by adaptive training for water vapor retrieval. Tomography experiments were carried out using the GNSS data collected from the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 25 March to 25 April 2014 under different scenarios. The tomographic results are compared to the ECMWF data and validated by the radiosonde. Results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.

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.

Water vapor satellite products in the European Arctic: An inter-comparison against GNSS data

2020 ◽  
Vol 741 ◽  
pp. 140335
Author(s):  
Javier Vaquero-Martínez ◽  
Manuel Antón ◽  
Roberto Román ◽  
Victoria E. Cachorro ◽  
Huiqun Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
European Arctic ◽  
Gnss Data

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.

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