GNSS Meteorology: Algorithm, Modelling, Assessment and Application

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 3 October 2024 | Viewed by 2429

Special Issue Editors

College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: GNSS meteorology; water vapor tomography; atmospheric modelling
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Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: GNSS meteorology; atmosphere remote sensing; weather monitoring

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Guest Editor
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541004, China
Interests: GNSS precise positioning; GNSS atmospheric sounding; tropospheric modeling
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School of Geodesy and Geomatics, Wuhan University, Wuhan 430079,China
Interests: GNSS tropospheric delay; mapping function; atmospheric asymmetry; gradient

Special Issue Information

Dear Colleagues,

GNSS meteorology refers to the use of the effect of the atmosphere on the propagation of GNSS radio signals to derive information on the state of the neutral atmosphere. With the continuous observations from GNSS receivers from both satellite platforms and ground permanent stations, it has become an excellent tool for studying the earth atmosphere, snow depth, soil moisture, and terrestrial water storage variations with the advantages of continuous operation, a low cost, all-weather conditions, high accuracy, and high temporal resolution. For instance, not only can the two-dimensional content of the precipitable water vapor (PWV) be retrieved by GNSS, the three-dimensional distribution of water vapor density can be reconstructed by the water vapor tomography. It has a wide range of applications, such as meteorology, climatology, nowcasting, 4D monitoring of weather events and hydrological phenomena.

In this Special Issue, we are looking for articles that discuss the recent trends, current progress and future directions for GNSS meteorology, and articles that describe the algorithm, model and applications related to GNSS meteorology. We welcome original research on topics including, but not limited to:

  • Water vapor retrievals based on multi-GNSS;
  • Derivation of GNSS atmospheric parameters;
  • PWV/IWV time series analysis and prediction;
  • Modeling for the atmospheric parameters;
  • Water vapor tomography;
  • Numerical nowcasting based on GNSS observations;
  • GNSS data assimilation and application;
  • Climate change;
  • GNSS radio occultation;
  • Monitoring of the rainfall/drought/particle using GNSS PWV/ZWD/ZTD;
  • Comprehensive utilization of multi-source atmospheric data;
  • Parameter inversion using GNSS-IR.

Dr. Fei Yang
Dr. Ming Shangguan
Dr. Liangke Huang
Dr. Di Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • GNSS
  • atmosphere
  • troposphere
  • precipitable water vapor
  • weighted mean temperature
  • GNSS reflectometry
  • GNSS RO
  • ray tracing
  • data assimilation
  • tomography
  • numerical modelling
  • weather forecast
  • numerical forecast
  • climate change

Published Papers (2 papers)

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Research

17 pages, 2343 KiB  
Article
A Refined Atmospheric Weighted Average Temperature Model Considering Multiple Factors in the Qinghai–Tibet Plateau Region
by Kunjun Tian, Si Xiong, Zhengtao Wang, Bingbing Zhang, Baomin Han and Bing Guo
Atmosphere 2023, 14(12), 1760; https://doi.org/10.3390/atmos14121760 - 29 Nov 2023
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Abstract
The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict [...] Read more.
The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict the Tm in the region. Therefore, this study constructed a weighted average temperature refinement model (XTm) related to surface temperature, water vapor pressure, geopotential height, annual variation, and semi-annual variation based on measured data from 13 radiosonde stations in the Qinghai–Tibet Plateau region from 2008 to 2017. Using the Tm calculated via the numerical integration method of radiosonde observations in the Qinghai–Tibet Plateau region from 2018 to 2019 as a reference value, the quality of the XTm model was tested and compared with the Bevis model and GPT2w (global pressure and temperature 2 wet) model. The results show that for 13 modeling stations, the bias and root-mean-square (RMS) values of the XTm model were −0.02 K and 2.83 K, respectively; compared with the Bevis, GPT2-1, and GPT2w-5 models, the quality of XTm was increased by 47%, 38%, and 47%, respectively. For the four non-modeling stations, the average bias and RMS values of the XTm model were 0.58 K and 2.78 K, respectively; compared with the other three Tm models, the RMS values and the mean bias were both minimal. In addition, the XTm model was also used to calculate the global navigation satellite system (GNSS) precipitable water vapor (PWV), and its average values for the theoretical RMSPWV and RMSPWV/PWV generated by water vapor calculation were 0.11 mm and 1.03%, respectively. Therefore, in the Qinghai–Tibet Plateau region, the XTm model could predict more accurate Tm values, which, in turn, is important for water vapor monitoring. Full article
(This article belongs to the Special Issue GNSS Meteorology: Algorithm, Modelling, Assessment and Application)
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14 pages, 3292 KiB  
Article
Verification and Accuracy Analysis of Single-Frequency Occultation Processing Based on the BeiDou Navigation System
by Ruimin Li, Qifei Du, Ming Yang, Haoran Tian, Yueqiang Sun, Xiangguang Meng, Weihua Bai, Xianyi Wang, Guangyuan Tan and Peng Hu
Atmosphere 2023, 14(4), 742; https://doi.org/10.3390/atmos14040742 - 19 Apr 2023
Cited by 2 | Viewed by 1062
Abstract
GNSS single-frequency occultation processing technology has the advantage of simple instrumentation, but it is not clear about the accuracy of the Beidou-based single-frequency occultation processing. This paper verifies the single-frequency occultation processing algorithm of the BeiDou navigation system (BDS) and analyzes its accuracy [...] Read more.
GNSS single-frequency occultation processing technology has the advantage of simple instrumentation, but it is not clear about the accuracy of the Beidou-based single-frequency occultation processing. This paper verifies the single-frequency occultation processing algorithm of the BeiDou navigation system (BDS) and analyzes its accuracy based on occultation observation data from the FY3E satellite. The research aimed to verify the single-frequency ionospheric relative total electron content (relTEC), analyze the accuracy of the reconstructed second frequency B3’s excess phase Doppler, and analyze the accuracy of the refractive index products. Results: (1) As for relTEC and excess phase Doppler, the correlation coefficient between single-frequency occultation processing and dual-frequency occultation processing is greater than 0.95. (2) The relative average deviations of the excess phase Doppler of B3 are mostly less than 0.2%, and the relative standard deviations are mostly around 0.5%. (3) The bias index and root mean square index of single/dual-frequency inversion have good consistency compared with ERA5 data. All the results show that the single- and dual-frequency inversion refractive index products have comparable accuracies, and the accuracy of the standard deviation of single-frequency inversion refractive index products over 25 km being slightly lower than that of dual-frequency inversion refractive index products. Full article
(This article belongs to the Special Issue GNSS Meteorology: Algorithm, Modelling, Assessment and Application)
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