Multisource Remote Sensing Data Fusion and Assimilation in Atmospheric Observations
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 2902
Special Issue Editors
Interests: data assimilation; multisource data fusion; geostatistics; machine learning; spatio-temporal data analysis
Special Issue Information
Dear Colleagues,
This Special Issue mainly explores “advanced” or “novel” data fusion and data assimilation techniques for atmospheric observations concerning air pollution, air temperature, precipitation, wind, etc. The availability of multi-resolution remote sensing data has promoted the development of different data fusion and assimilation. The aim of this issue is to collect new ideas on data fusion, data assimilation, machine learning, geostatistics, and quality assessment. The authors will be able to express their creativity without restrictions and ensure the scientific rigor of research. This Special Issue is, therefore, intended to strongly encourage creative endeavors in theory and practice. We are looking for techniques that may bring challenges and lead to breakthroughs, for example, the new methods in the initial experimental stage that have made basic advancements and potentially contributed to a paradigm shift. Recent developments, applications, and evaluations of remote sensing and observation techniques for atmospheric observations are preferred. Required topics include but are not limited to:
- Multi-source data fusion;
- Data assimilation into physical simulation;
- Machine learning;
- Deep learning with interpretability;
- Baysian statistics;
- Geostatistics;
- Microwave remote sensing;
- Spatio-temporal analysis;
- Multi-resolution analysis;
- Quality assessment;
- Model calibration;
- Data validation.
Dr. Jianhui Xu
Dr. Hong Shu
Guest Editors
Manuscript Submission Information
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Keywords
- data fusion of different atmospheric observations
- data Assimilation
- machine learning
- deep learning
- geostatistics
- multi-resolution analysis
- microwave remote sensing data
- machine learning interpretability in data fusion
- spatio-temporal analysis of atmospheric observations
- atmosphere environmental quality assessment
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