Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.9
Journals
Atmosphere
Special Issues
Advanced GNSS for Severe Weather Events and Climate Monitoring
share announcement

Advanced GNSS for Severe Weather Events and Climate Monitoring

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5088

Special Issue Editors

Prof. Dr. Tao Geng

E-Mail Website
Guest Editor
GNSS Research Center, Wuhan University, Wuhan 430079, China
Interests: GNSS; precise positioning; orbit determination; ionospheric modeling
Prof. Dr. Zishen Li

E-Mail Website
Guest Editor
Aerospace Information Research institute (AIR), Chinese Academy of Sciences, Beijing 100094, China
Interests: BDS/GNSS; ionosphere modeling and correction; differential code biases; precise positioning and integrity
Dr. Qiang Zhang

E-Mail Website
Guest Editor
GNSS Research Center, Wuhan University, Wuhan 430079, China
Interests: broadcast ionospheric models; GNSS ionospheric modeling; signal bias estimation

Special Issue Information

Dear Colleagues,

The global navigation satellite system (GNSS) is a well-established atmospheric remote sensing system which can accurately measure precipitable water vapor, zenith total delay, slant total delay, slant water vapor, gradient, bending angle, refractivity, etc. Advanced GNSS have heralded a new era of atmospheric sounding, severe weather monitoring, GNSS meteorology, and climatology. Effective monitoring and accurate forecasting of severe weather events and climate change can prevent disasters and save human lives. To take advantage of advanced GNSS techniques, this Special Issue mainly focuses on papers that address topics including but not limited to:

  • Advanced GNSS atmospheric sounding and data processing;
  • Data mining of atmospheric products;
  • Weather and climate monitoring using GNSS techniques;
  • Severe weather event forecasting;
  • Numerical weather prediction models;
  • Interdisciplinary research and new applications in the atmosphere, meteorology, and climatology fields.

Prof. Dr. Tao Geng
Prof. Dr. Zishen Li
Dr. Qiang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GNSS
  • severe weather event forecasting
  • climate monitoring
  • atmospheric modeling
  • numerical weather prediction model
  • tropospheric tomography
  • ionosphere
  • precipitable water vapor
  • miscellaneous applications

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 6311 KiB  
Article
Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique
by Ehsan Forootan, Masood Dehvari, Saeed Farzaneh and Sedigheh Karimi
Atmosphere 2023, 14(1), 112; https://doi.org/10.3390/atmos14010112 - 04 Jan 2023
Cited by 2 | Viewed by 1622
Abstract
Constructing accurate models that provide information about water vapor content in the troposphere improves the reliability of numerical weather forecasts and the position accuracy of low-cost Global Navigation Satellite System (GNSS) receivers. However, developing models with high spatial-temporal resolution demands compact observational datasets [...] Read more.
Constructing accurate models that provide information about water vapor content in the troposphere improves the reliability of numerical weather forecasts and the position accuracy of low-cost Global Navigation Satellite System (GNSS) receivers. However, developing models with high spatial-temporal resolution demands compact observational datasets in the regions of interest. Empirical models, such as the Global Pressure and Temperature 3 (GPT3w), have been constructed based on the monthly averaged outputs of numerical weather models. These models are based on the assimilation of existing measurements to provide estimations of atmospheric parameters. Therefore, their accuracy may be reduced over regions with a low resolution of radiosonde or continuous GNSS stations. By emerging and increasing the Low-Earth-Orbiting (LEO) satellites that measure atmospheric parameter profiles using the Radio Occultation (RO) technique, new opportunities have appeared to acquire high-resolution atmospheric observations at different altitudes. This study aims to apply these RO observations to improve the accuracy of the GPT3w model over Iran, which is sparse in terms of long-term GNSS and radiosonde measurements. The temperature, pressure, and water vapor pressure parameters from the GPT3w model have been used as the input layers of the Extremely Learning Machine (ELM) technique. The wet refractivity indices from the RO technique are considered target parameters in the output layer to train the ELM. The RO observations of 2007–2020 are applied for training, and those of 2020–2022 for evaluating the performance of the developed ELM. Our numerical results indicate that the developed ELM decreases the Root-Mean-Square Error (RMSE) values of the wet refractivity indices by about 17 percent, compared to the original GPT3w RMSE values. Additionally, the wet refractivity indices from ELM have revealed correlation coefficients of about 0.64, which is about 1.9 times those related to the original GPT3w model. The performance of ELM has also been examined by comparison with the data of six located radiosonde stations covering the year 2020. This comparison shows an improvement of about 14 percent in the average RMSE values of the estimated wet refractivity indices. Full article
(This article belongs to the Special Issue Advanced GNSS for Severe Weather Events and Climate Monitoring)
►▼ Show Figures

Figure 1

12 pages, 6759 KiB  
Article
An Improved Strategy for Real-Time Troposphere Estimation and Its Application in the Severe Weather Event Monitoring
by Lewen Zhao, Mingxuan Cui and Jia Song
Atmosphere 2023, 14(1), 46; https://doi.org/10.3390/atmos14010046 - 27 Dec 2022
Cited by 2 | Viewed by 1267
Abstract
The water vapor content in the atmosphere is highly correlated with rainfall events, which can be used as a data source for rainfall prediction or drought monitoring. The GNSS PPP (Precise Point Positioning) technique can be used to estimate the troposphere ZWD (Zenith [...] Read more.
The water vapor content in the atmosphere is highly correlated with rainfall events, which can be used as a data source for rainfall prediction or drought monitoring. The GNSS PPP (Precise Point Positioning) technique can be used to estimate the troposphere ZWD (Zenith Wet Delay) parameter which can be converted into precipitable water vapor (PWV). In this study, we first investigate the impacts of the weighting strategies, observation noise settings, and gradient estimation on the accuracy of ZWD and positions. A refined strategy is proposed for the troposphere estimation with uncombined raw PPP model, down-weighting of Galileo/GLONASS/BDS code observation by a factor of 3, using a sine2-type elevation-dependent weighting function and estimating the horizontal gradients. Based on the strategy, the correlation of the estimated tropospheric parameters with the rainfall is analyzed based on the data from the “7.20” rainstorm in Henan Province, China. The PWV is first calculated based on the hourly global pressure and temperature (HGPT) model and compared with the results from ERA5 products. Results show their good consistency during the rainfall period or the normal period with a standard deviation of 3 mm. Then, the high correlation between the PWV and the heavy rain rainfall event is validated. Results show that the accumulated PWV maintains a high level before the rainstorm if a sustainable water supply is available, while it decreased rapidly after the rainfall. In comparison, the horizontal gradients and the satellite residuals are less correlated with the water vapor content. However, the gradients can be used to indicate the horizontal asymmetry of the water vapor in the atmosphere. Full article
(This article belongs to the Special Issue Advanced GNSS for Severe Weather Events and Climate Monitoring)
►▼ Show Figures

Figure 1

20 pages, 7296 KiB  
Article
Prediction of CORS Water Vapor Values Based on the CEEMDAN and ARIMA-LSTM Combination Model
by Xingxing Xiao, Weicai Lv, Yuchen Han, Fukang Lu and Jintao Liu
Atmosphere 2022, 13(9), 1453; https://doi.org/10.3390/atmos13091453 - 08 Sep 2022
Cited by 3 | Viewed by 1504
Abstract
By relying on the advantages of a uniform site distribution and continuous observation of the Continuously Operating Reference Stations (CORS) system, real-time high-precision Global Navigation Satellite System/Precipitable Water Vapor (GNSS/PWV) data interpretation can be carried out to achieve accurate monitoring of regional water [...] Read more.
By relying on the advantages of a uniform site distribution and continuous observation of the Continuously Operating Reference Stations (CORS) system, real-time high-precision Global Navigation Satellite System/Precipitable Water Vapor (GNSS/PWV) data interpretation can be carried out to achieve accurate monitoring of regional water vapor changes. The study of the atmospheric water vapor content and distribution changes is the basis for the realization of rainfall forecasting and water vapor circulation research. Such research can provide data support for the effective forecasting of regional precipitation in megacities and the construction of a more sensitive flood prevention and warning system. Nowadays, a single model is often adopted for GNSS/PWV time series. This makes it challenging to match the high randomness characteristic of water vapor change. This study proposes a hybrid model that takes into account the linear and nonlinear aspects of water vapor data by using complete empirical mode decomposition (CEEMDAN) of adaptive noise, differential autoregressive integrated moving average (ARIMA), and the long-short-term memory network (LSTM). The CEEMDAN is used to decompose the water vapor data series. Then, the high- and low-frequency data are modeled separately, reducing the sequence’s complexity and non-stationarity. In selecting the prediction model, we use the ARIMA model for the high-frequency series and the ARIMA–GWO–LSTM ensemble model for the low-frequency sub-series and residual series. The model is verified using GNSS/PWV time series data collected at the Hong Kong CORS station in July 2021. The results show the following: (1) The LSTM model optimized by the grey wolf optimization algorithm (GWO) is comparable with the single LSTM model in the low-frequency sequence prediction process, and the error items are reduced by 30% after calculation. (2) During the process from CEEMDAN decomposition to the use of the combination model for prediction, the accuracy evaluation indexes of the station increase by more than 20%. The interpolation method can accurately determine the regional water vapor spatial variation, which is of practical significance for local rainfall forecasting. High-frequency data obtained by CEEMDAN decomposition demonstrate the dramatic changes in water vapor before and after the rainfall, which can provide ideas for improving the accuracy of rainfall forecasting. Full article
(This article belongs to the Special Issue Advanced GNSS for Severe Weather Events and Climate Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop