Special Issue "GNSS Remote Sensing in Atmosphere and Environment"

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

Deadline for manuscript submissions: 30 June 2023 | Viewed by 912

Special Issue Editors

1. Geomatics Engineering, School of Geographical Science and Geomatics Engineering, Shihu Campus, Suzhou University of Science and Technology, Suzhou 215009, China
2. Research Center of BeiDou Navigation and Environmental Remote Sensing, Shihu Campus, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: GNSS meteorology; GNSS precise positioning; rainstorm disaster monitoring
GNSS Research Centre, Wuhan University, Wuhan, China
Interests: GNSS data processing and high-precision positioning; GNSS meteorology; space-based GNSS radio occultation

Special Issue Information

Dear Colleagues,

Global navigation satellite systems (GNSSs) have become one of the predominant remote sensing systems. GNSS remote sensing can be used to accurately measure precipitable water vapor (PWV), slant water vapor (SWV), zenith total delay (ZTD), slant total delay (STD), horizontal gradients, atmospheric refractivity, snow depth, soil moisture and wave height in the atmosphere and environment. GNSS remote sensing has become a new era of atmospheric sounding as well as severe climate and weather monitoring. GNSS remote sensing not only leads to a better understanding of climate and weather changes, but also helps to monitor and accurately forecast severe weather events, mitigate natural disasters and save human lives.

We present a Special Issue of Atmosphere titled “GNSS Remote Sensing in Atmosphere and Environment”. We invite you to contribute to this Special Issue with original research and review articles on topics including, but not limited to:

  • Water vapor retrievals based on GNSS, radiosonde, microwave radiometer, and other observation systems;
  • Multi-sensor data assimilation and model optimization;
  • Weather, climate, and environment monitoring using GNSS remote sensing;
  • Short-term rainstorm monitoring and forecasting based on GNSS-derived tropospheric parameters (ZTD, ZWD or PWV);
  • Machine learning, artificial intelligence and deep learning algorithms and applications in weather prediction and climate analyses using GNSS remote sensing;
  • New research and applications of GNSS remote sensing in atmosphere and environment.

Dr. Li Li
Dr. Pengfei Xia
Guest Editors

Manuscript Submission Information

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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 2000 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
  • remote sensing
  • atmosphere
  • troposphere
  • water vapor
  • data assimilation
  • environment
  • precipitation forecast
  • nowcasting of severe weather events
  • GNSS reflectometry

Published Papers (2 papers)

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Research

Article
Global Navigation Satellite System-Based Retrieval of Precipitable Water Vapor and Its Relationship with Rainfall and Drought in Qinghai, China
Atmosphere 2023, 14(3), 517; https://doi.org/10.3390/atmos14030517 - 07 Mar 2023
Viewed by 302
Abstract
Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation [...] Read more.
Qinghai Province is situated deep in inland China, on the Qinghai-Tibet plateau, and it has unique climate change characteristics. Therefore, understanding the temporal and spatial distributions of water vapor in this region can be of great significance. The present study applied global navigation satellite system (GNSS) technology to retrieve precipitable water vapor (PWV) in Qinghai and analyzed its relationship with rainfall and drought. Firstly, radiosonde (RS) data is used to verify the precision of the surface pressure (P) and temperature (T) from the fifth-generation atmosphere reanalysis data set (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the zenith troposphere delay (ZTD), calculated based on the data from continuously operating reference stations (CORS) in Qinghai. Secondly, a regional atmospheric weighted mean temperature (Tm) (QH-Tm) model was developed for Qinghai based on P, T, and relative humidity, as well as the consideration of the influence of seasonal changes in Tm. Finally, the PWV of each CORS in Qinghai was calculated using the GNSS-derived ZTD and ERA5-derived meteorological data, and its relationship with rainfall and drought was evaluated. The results show that the ERA5-derived P and T have high precision, and their average root mean square (RMS), mean absolute error (MAE) and bias were 1.06/0.85/0.01 hPa and 2.98/2.42/0.03 K, respectively. The RMS, MAE and bias of GNSS-derived ZTD were 13.2 mm, 10.3 mm and −1.8 mm, respectively. The theoretical error for PWV was 1.98 mm; compared with that of RS- and ERA5-derived PWV, the actual error was 2.69 mm and 2.16 mm, respectively. In addition, the changing trend of GNSS-derived PWV was consistent with that of rainfall events, and it closely and negatively correlated with the standardized precipitation evapotranspiration index. Therefore, the PWV retrieved from GNSS data in this study offers high precision and good feasibility for practical applications; thus, it can serve as a crucial tool for investigating water vapor distribution and climate change in Qinghai. Full article
(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)
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Article
Enhancing GNSS-R Soil Moisture Accuracy with Vegetation and Roughness Correction
Atmosphere 2023, 14(3), 509; https://doi.org/10.3390/atmos14030509 - 06 Mar 2023
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Abstract
Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface [...] Read more.
Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm3cm−3, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stability. Full article
(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)
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