GNSS and InSAR Meteorology: Algorithm and Application
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".
Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 234
Special Issue Editors
Interests: GNSS meteorology; InSAR meteorology; rainfall forecast
Interests: GNSS meteorology; radio occultation; data assimilation
Special Issue Information
Dear Colleagues,
Currently, the global navigation satellite system (GNSS) technique has been considered to be the most promising method for retrieving atmospheric water vapor. Meanwhile, the “atmospheric delay error” along SAR signals can also be investigated as a valuable source of information with which to determine water vapor content. Due to the limitations of GNSS station distributions, regional high-resolution water vapor distributions cannot be reconstructed by using a GNSS; however, InSAR data are able to retrieve high-horizontal-resolution water vapor information, improving the low spatial resolution of GNSS measurements. Therefore, it is expected that GNSS–InSAR observation systems with more observations can contribute to the optimal retrieval of high-spatiotemporal-resolution atmospheric water vapor, making up for the gap between the two techniques and improving the application value of GNSS–InSAR-based water vapor products. In this Special Issue of Applied Sciences we will collect a wide range of articles covering, but not limited to, many aspects of GNSS and InSAR meteorology: data processing, technological developments, theoretical studies, and innovative applications.
Prof. Dr. Shubi Zhang
Dr. Möller Gregor
Guest Editors
Dr. Wenyuan Zhang
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- global navigation satellite system
- interferometric synthetic aperture radar
- GNSS water vapor tomography
- InSAR water vapor retrieval
- water vapor time series analysis and prediction
- radio occultation
- data assimilation
- numerical nowcasting based on multisource atmospheric data
- rainfall forecast
- climate change
