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Advanced Satellite Earth Observing Technologies for Weather and Climate Resilience

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2371

Special Issue Editors

School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: GNSS meteorology; GNSS atmospheric monitoring; data assimilation; numerical weather prediction; climate analysis and climate risks assessment
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Guest Editor
School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: precise satellite positioning and navigation; geodesy; disaster management; atmospheric remote sensing
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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: precise positioning; atmospheric remote sensing; GNSS meteorology; climate monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou, China
Interests: GNSS meteorology; satellite remote sensing, weather prediction; big data analytics

Special Issue Information

Dear Colleagues

Nowadays, the accelerating impacts of global warming and large-scale climate changes have significantly amplified the severity and frequency of weather and climate extremes worldwide. These hazardous events are leading to detrimental social and economic consequences and pose profound threats to the sustainable development of human societies. This highlights a pressing need to deepen our understanding of the intrinsic nature of these phenomena and to refine the existing methodologies for their effective detection and early warning.

Weather and climate monitoring depend on a global network of Earth observing systems, with satellites providing a unique perspective through comprehensive coverage that enables effective tracking and analysis of extreme events. Recent years have witnessed unprecedented advancements in satellite Earth observing technologies, such as weather satellite-based remote sensing and Global Navigation Satellite System (GNSS) atmospheric monitoring techniques. The rapid development of new satellite-based data streams is opening up new avenues for monitoring meteorological and climate hazards at multi-spatiotemporal scales, which also form the backbone of Earth system models. Therefore, it is imperative to fully harness the potential of these satellite Earth observing technologies to develop robust and reliable methodologies that promote innovative applications across the climate, meteorology and environmental communities. This Special Issue is aimed at increasing the utilization and uptake of satellite Earth observations, e.g., the International GNSS Service (IGS) data and products, to provide promising methodologies for improved monitoring of weather and climate extremes, thereby constructing climate-resilient communities and contributing to the United Nations Sustainable Development Goals of the 2030 Agenda. Emphasizing the use of advanced satellite Earth observing technologies, especially GNSS atmospheric monitoring techniques, the scope of this Special Issue includes, but is not limited to, the following:

  • Effective mining and analysis of multi-type satellite Earth observations and their derivatives;
  • Advanced multi-GNSS data processing, atmospheric monitoring and modeling;
  • Synthetic applications from the use of satellite Earth observations;
  • Data assimilation technique in operational Earth system models;
  • Advanced artificial intelligence (AI)-empowered and digital-twin approaches for climate analysis, weather prediction, and environmental monitoring;
  • Furthermore, miscellaneous interdisciplinary research and innovative applications towards the fields of meteorology, climatology, and environment are also welcome.

Dr. Haobo Li
Prof. Dr. Suelynn Choy
Dr. Yuriy Kuleshov
Dr. Xiaoming Wang
Dr. Longjiang Li
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • satellite Earth observations
  • GNSS atmospheric monitoring
  • International GNSS Service data and products
  • severe weather forecasting
  • climate risks assessment
  • environmental monitoring
  • numerical weather prediction model
  • artificial intelligence (AI)-empowered climate model
  • GNSS tropospheric tomography
  • miscellaneous advanced methodologies and applications

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Published Papers (1 paper)

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Research

24 pages, 21229 KiB  
Article
The Zenith Total Delay Combination of International GNSS Service Repro3 and the Analysis of Its Precision
by Qiuying Huang, Xiaoming Wang, Haobo Li, Jinglei Zhang, Zhaowei Han, Dingyi Liu, Yaping Li and Hongxin Zhang
Remote Sens. 2024, 16(20), 3885; https://doi.org/10.3390/rs16203885 - 18 Oct 2024
Viewed by 1648
Abstract
Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service [...] Read more.
Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction. Full article
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