Multi-Source Observations and Intelligent Data Assimilation for Improving High-Impact Weather Prediction
A Special Issue of Atmosphere (ISSN 2073-4433) belonging to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 991
Editors
Interests: doppler weather radar data assimilation; satellite remote sensing observation data assimilation; integrated variational hybrid assimilation system development; wind, solar and other renewable energy research
Special Issues, Collections and Topics in MDPI journals
Interests: satellite remote sensing observation data assimilation; radiance data application for cloud retrievals; ensemble–variational data assimilation; radar data assimilation
Special Issue Information
Dear Colleagues,
In recent years, with the rapid development of multi-source observation networks—including next-generation weather radar systems, geostationary and polar-orbiting satellites, ground-based GNSS, water vapor detection systems, UAVs, and surface sensor networks—atmospheric science has entered a data-rich era. At the same time, artificial intelligence (AI) and machine learning (ML) have shown great potential in model bias correction, observation fusion, and intelligent data assimilation.
This Special Issue will focus on how to integrate multi-source observational data and intelligent algorithms to improve the capability and accuracy of high-impact weather prediction, particularly for severe convective systems, extreme precipitation, typhoons, and other hazardous weather events. We aim to bring together contributions from both methodological innovation and application-oriented studies, encouraging cross-disciplinary approaches that combine atmospheric science, computational techniques, and artificial intelligence.
Topics of Interest
The topics of this Special Issue include, but are not limited to, the following:
Fusion and application of multi-source observational data (radar, satellite, UAV, ground-based networks, etc.);
Data assimilation methods and algorithmic innovations for emerging observation systems;
Applications of artificial intelligence and deep learning in weather forecasting and data assimilation;
Intelligent design of observation networks and strategies for optimal observing systems;
Improvement in extreme weather prediction through high-resolution numerical models and observation fusion;
Cross-scale observational applications in nowcasting and extended-range forecasts.
Dr. Feifei Shen
Dr. Dongmei Xu
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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 observations
- artificial intelligence in weather forecasting
- data assimilation
- high-impact weather prediction
- extreme weather events
- intelligent observation networks
- nowcasting and short-term forecasting
- machine learning for atmospheric science
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