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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: 14 April 2026 | Viewed by 1549

Special Issue Editors

Prof. Dr. Dong-In Lee

E-Mail Website
Guest Editor
Atmospheric Environmental Research Institute, Pukyong National University, Busan 48513, Republic of Korea
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
Prof. Dr. Kaoshen Chung

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, National Central University, Taoyuan, Taiwan
Interests: radar meteorology; short-term forecast errors; mesoscale/convective scale data assimilation
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
Prof. Dr. Kaoshen Chung
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-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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 2030 KB  
Article
Precipitation Phase Classification with X-Band Polarimetric Radar and Machine Learning Using Micro Rain Radar and Disdrometer Data in Grenoble (French Alps)
by Francesc Polls, Brice Boudevillain, Mireia Udina, Francisco J. Ruiz, Albert Garcia-Benadí, Eulàlia Busquets, Matthieu Vernay and Joan Bech
Remote Sens. 2026, 18(3), 433; https://doi.org/10.3390/rs18030433 - 29 Jan 2026
Viewed by 348
Abstract
Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not [...] Read more.
Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not been evaluated over long periods using real observational data, but mainly through simulations. This study addresses this gap by introducing a new methodology based on X-band polarimetric radar and by validating it against real precipitation events over an extended time period. The machine learning model is trained and tested using a four-year dataset including X-band radar, Micro Rain Radar, disdrometer, and temperature profile data from the Grenoble region (French Alps). To improve the classification accuracy, three temperature profile sources were tested: lapse rates obtained from automatic weather stations, interpolation of the temperature profile from the freezing level detected by the Micro Rain Radar, and temperature profiles from the operational AROME model forecast. Three different phase classification schemes were tested: two existing schemes based on fuzzy-logic, and the new method based on random forest. Results show that the random forest method, trained with radar polarimetric variables, AROME temperature profiles, and target labels derived from Micro Rain Radar observations, achieves the highest accuracy. Despite the overall good classification results, limitations persist in identifying mixed-phase precipitation due to its transitional nature and vertical variability. Feature importance analysis indicates that temperature is the most influential variable in the classification scheme, followed by reflectivity factor measured in the horizontal plane (Ze) and differential reflectivity (Zdr). This methodology demonstrates the potential of combining machine learning techniques with multi-instrument observations to improve hydrometeor classification in complex terrain. The approach offers valuable insights for operational forecasting, water resource management, and climate impact assessments in mountainous regions. Full article
(This article belongs to the Special Issue Advanced Applications of Radar Remote Sensing and Artificial Intelligence in Meteorology and Hydrology)
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30 pages, 15490 KB  
Article
MRKAN: A Multi-Scale Network for Dual-Polarization Radar Multi-Parameter Extrapolation
by Junfei Wang, Yonghong Zhang, Linglong Zhu, Qi Liu, Haiyang Lin, Huaqing Peng and Lei Wu
Remote Sens. 2026, 18(2), 372; https://doi.org/10.3390/rs18020372 - 22 Jan 2026
Viewed by 199
Abstract
Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis [...] Read more.
Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis functions (RBFs), and the Kolmogorov–Arnold Network (KAN). The method predicts radar reflectivity, differential reflectivity, and the specific differential phase, enabling a refined depiction of the dynamic structure of severe convective systems. MRKAN incorporates four key innovations. First, a Cross-Scan Mamba module is designed to enhance global spatiotemporal dependencies through point-wise modeling across multiple complementary scans. Second, a Multi-Order KAN module is developed that employs multi-order β-spline functions to overcome the linear limitations of convolution kernels and to achieve high-order representations of nonlinear local features. Third, a Gaussian and Inverse Multiquadratic RBF module is constructed to extract mesoscale features using a combination of Gaussian radial basis functions and Inverse Multiquadratic radial basis functions. Finally, a Multi-Scale Feature Fusion module is designed to integrate global, local, and mesoscale information, thereby enhancing multi-scale adaptive modeling capability. Experimental results show that MRKAN significantly outperforms mainstream methods across multiple key metrics and yields a more accurate depiction of the spatiotemporal evolution of severe convective weather. Full article
(This article belongs to the Special Issue Advanced Applications of Radar Remote Sensing and Artificial Intelligence in Meteorology and Hydrology)
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19 pages, 5451 KB  
Article
Evaluation of the flagGraupelHail Product from Dual-Frequency Precipitation Radar Onboard the Global Precipitation Measurement Core Observatory Using Multi-Parameter Phased Array Weather Radar
by Nobuhiro Takahashi and Tomoki Kosaka
Remote Sens. 2025, 17(22), 3741; https://doi.org/10.3390/rs17223741 - 17 Nov 2025
Cited by 1 | Viewed by 609
Abstract
A major scientific challenge is understanding how precipitation systems will change under global warming. In particular, extreme precipitation events associated with hail and graupel are of significant concern. In this study, we evaluated the performance of the flagGraupelHail product from the Dual-Frequency Precipitation [...] Read more.
A major scientific challenge is understanding how precipitation systems will change under global warming. In particular, extreme precipitation events associated with hail and graupel are of significant concern. In this study, we evaluated the performance of the flagGraupelHail product from the Dual-Frequency Precipitation Radar (DPR) aboard the GPM Core Observatory using high-resolution dual-polarization observations from Multi-Parameter Phased Array Weather Radar (MP-PAWR). The analysis focused on a convective system that developed in a humid environment over the Tokyo region of Japan, providing a valuable assessment within a climatic regime that has been underrepresented in previous studies. A bias correction for MP-PAWR reflectivity, derived from XRAIN network comparisons, yielded good agreement with KuPR observations from the DPR. A new grid-matching method, suitable for comparing vertically varying hydrometeor particle types and available only for MP-PAWR, was also introduced. The comparison revealed that DPR flagGraupelHail detections generally corresponded to regions of graupel occurrence identified by the MP-PAWR GHratio, defined as the number of graupel/hail grids within a DPR observation volume, although DPR tended to detect fewer events. To improve detection performance, we introduced a new indicator, STH35-FH—the height difference between the 35 dBZ echo top and the 0 °C level—as a complementary parameter to the PTI value used to determine flagGraupelHail. Incorporating STH35-FH improved the consistency between DPR and MP-PAWR detections, reducing false positives and enhancing overall detection accuracy. These results demonstrate the value of combining ground-based and spaceborne radar observations to improve global precipitation retrievals, particularly in humid environments. This approach will contribute to more accurate global graupel/hail estimation by spaceborne precipitation radar and a better understanding of how global warming affects precipitation systems. Full article
(This article belongs to the Special Issue Advanced Applications of Radar Remote Sensing and Artificial Intelligence in Meteorology and Hydrology)
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