Multi-source Meteorological Data Fusion and Assimilation Methods
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".
Deadline for manuscript submissions: 27 May 2024 | Viewed by 1539
Special Issue Editor
Interests: precipitation data fusion; land data assimilation; land–atmosphere coupled data assimilation; short-term climate prediction
Special Issue Information
Dear Colleagues,
We are pleased to announce a Special Issue dedicated to exploring the latest advancements in the field of multi-source meteorological data fusion and assimilation. This rapidly evolving research area focuses on integrating diverse data from multiple sources to enhance our understanding and improve the accuracy of weather, hydrological, and ocean forecasting and analysis.
The objective of this Special Issue is to showcase cutting-edge research, methodologies, and practical applications related to the fusion and assimilation of meteorological data from various sources. We encourage researchers, scientists, and practitioners in the field of meteorology to contribute their original work to this Special Issue, thereby fostering interdisciplinary collaboration and driving advancements in this crucial domain.
Topics of interest include, but are not limited to:
- Innovative data fusion techniques for integrating meteorological observations from different sources;
- Advanced assimilation methods for incorporating data into numerical weather prediction models;
- Applications of multi-source data fusion and assimilation in improving weather forecasting accuracy and extreme weather event prediction;
- Evaluation and validation of data fusion and assimilation techniques using observational and model data;
- Challenges and future directions in multi-source meteorological data fusion and assimilation.
By bringing together diverse perspectives and expertise, we aim to provide a comprehensive overview of the state-of-the-art in this field and inspire new research avenues. In this context, we are calling for submissions related but not limited to the above topics. We invite researchers to join us in this endeavor to present novel findings, theoretical developments, and practical insights that push the boundaries of multi-source meteorological data fusion and assimilation. We look forward to receiving your contributions and working together to advance the field of multi-source meteorological data fusion and assimilation.
Dr. Suping Nie
Guest Editor
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
- multi-source data fusion
- assimilation methods
- meteorological observations
- model–data integration
- weather forecasting