Advanced Applications of Radar Remote Sensing and Artificial Intelligence in Meteorology and Hydrology
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 46
Special Issue Editor
Interests: radar meteorology; dual-polarization radar and wind profiler observations; typhoon structure analysis, precipitation physics and dynamics; orographic rainfall process; artificial intelligence for radar data interpretation; machine learning and deep learning for quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF), radar nowcasting and hydrological prediction; hydrometeor classification using radar and AI technique, disdrometer and ground validation studies, global and regional hydrology of remote sensing-based precipitation estimation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the integration of advanced radar remote sensing technologies and artificial intelligence (AI) methodologies for improved analysis and forecasting in meteorology and hydrology. With increasing global concerns over extreme weather, flooding, and water resource management, radar-based observations combined with intelligent algorithms present an innovative path forward.
Key topics include dual-polarization radar, wind profilers, disdrometers, and their applications in real-time storm tracking, typhoon structure analysis, and precipitation classification. Furthermore, AI-based techniques such as deep learning, random forests, and support vector machines are rapidly transforming how we interpret complex radar signals and derive meaningful hydrometeorological insights.
The scope of this issue is global, encouraging submissions from all climate regions—tropical, temperate, arid, and polar—and from both developed and developing nations. We seek original research, comprehensive reviews, and methodological studies that showcase diverse geophysical environments and reflect different challenges, such as monsoons, snowstorms, hurricanes, and flash floods.
This issue also welcomes comparative studies across regions, including urban vs. rural rainfall detection, AI-enhanced radar analysis in mountainous vs. flat terrain, and cross-validation of AI models using radar data from different continents.
Prof. Dr. Dong-In Lee
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. 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
- AI and deep learning models for radar-based rainfall estimation
- global case studies of radar-aided flood and storm forecasting
- machine learning for hydrometeor classification using dual-pol radar
- variational wind retrieval using radar networks
- AI-driven analysis of tropical cyclones, hurricanes, and typhoons
- orographic precipitation modeling across diverse terrains
- integration of radar data with satellite or reanalysis products
- cross-continental comparison of precipitation microphysics using disdrometers
- radar and AI applications for snow, hail, and mixed-phase precipitation systems
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