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Remote Sensing
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State-of-the-Art Remote Sensing in Precipitation and Thunderstorm
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State-of-the-Art Remote Sensing in Precipitation and Thunderstorm

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1524

Special Issue Editors

Dr. Zhiqun Hu

E-Mail Website
Guest Editor
State Key Laboratory of Severe Weather Meteorological Science and Technology, CAMS, Beijing 100081, China
Interests: radar observation of severe convective storms and precipitation microphysics; the application of artificial intelligence in radar meteorology and nowcasting
Special Issues, Collections and Topics in MDPI journals
Dr. Filomena Romano

E-Mail Website
Guest Editor
Institute of Methodologies for Environmental Analysis, National Research Council (IMAA/CNR), 85050 Potenza, Italy
Interests: cloud remote sensing; cloud radiative forcing; cloud detection and classification; cloud microphysical properties; surface solar irradiance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to their small spatiotemporal scale, short-term heavy precipitation and thunderstorms and other disaster weather mainly rely on various remote sensing devices for their now-forecasting. This Special Issue will introduce the latest research results of various remote sensing devices in the near forecasting of heavy precipitation, thunderstorms, and other disaster weather.

Based on satellite, dual polarization phased array weather radar, and atmospheric vertical detection system data, the thermal dynamics, cloud microphysical mechanisms, and micro- and meso-scale structural characteristics of convective storm development in extreme wind and hail weather are studied. Using machine learning methods, an intelligent recognition model for extreme wind and hail weather is studied, and a 0–2 hour short-term forecast technology is developed.

Suggested themes and article types for submission:

  • Weather radar detection technology;
  • Application of artificial intelligence in short-term disaster weather forecasting.

Dr. Zhiqun Hu
Dr. Filomena Romano
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

  • severe convective weather
  • thunderstorm gale
  • hail
  • short term heavy precipitation
  • nowcasting
  • artificial intelligence

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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

21 pages, 5652 KB  
Article
Analysis of Generalization Performance of Tornado Detection Models: A Cross-Domain Evaluation from U.S. to Chinese Weather Radar Observations
by Biao Jiang, Shuai Zhang, Yubao Chen, Xuehua Li and Yancheng Wang
Remote Sens. 2026, 18(6), 948; https://doi.org/10.3390/rs18060948 - 20 Mar 2026
Viewed by 253
Abstract
Tornadoes pose severe threats, yet their low frequency in China creates a labeled data scarcity that hinders training robust detection models. Leveraging abundant U.S. data offers a solution, though cross-domain generalization remains challenging due to distinct climatic environments and heterogeneous radar systems. This [...] Read more.
Tornadoes pose severe threats, yet their low frequency in China creates a labeled data scarcity that hinders training robust detection models. Leveraging abundant U.S. data offers a solution, though cross-domain generalization remains challenging due to distinct climatic environments and heterogeneous radar systems. This study systematically evaluates the generalization capability of three representative models—TORP, TORP-XGB, and TDA-CNN—trained on the U.S. TorNet dataset and applied to Chinese CINRAD observations (2020–2024) via a zero-shot transfer strategy. The results indicate that while all models demonstrated robust performance in the source domain (with POD values of 0.75, 0.72, and 0.71 for TORP, TORP-XGB, and TDA-CNN, respectively), they experienced varying degrees of performance attenuation in the target domain (with POD values dropping to 0.56, 0.48, and 0.41, respectively). Notably, the TORP model exhibited superior robustness with minimal performance degradation. Further analysis primarily attributes this cross-domain degradation to three factors: disparities in radar systems, magnitude differences in tornado rotational features, and data quality issues. Crucially, sensitivity experiments confirm that linear feature enhancement substantially improves the detection rate and effectively mitigates the cross-domain performance gap, albeit at the cost of increased false alarms. These findings provide a reference for the cross-domain deployment of tornado identification models and future improvements in transfer learning strategies. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in Precipitation and Thunderstorm)
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23 pages, 7133 KB  
Article
An AI Training Dataset for Thunderstorm Monitoring and Forecasting over China
by Na Liu, Wenming Xiao, Anyuan Xiong, Qiang Zhang, Hong Ma, Hansheng Xie, Shuo Zhao, Yingrui Sun, Yujia Liu and Zhongyan Hu
Remote Sens. 2026, 18(5), 724; https://doi.org/10.3390/rs18050724 - 28 Feb 2026
Viewed by 458
Abstract
A thunderstorm is a weather system that can trigger severe natural disasters, characterized by sudden onset, short duration, and significant damage. Accurate forecasting of thunderstorms has long been a challenging task. Data-driven artificial intelligence (AI) technologies have provided new solutions, yet AI-driven thunderstorm [...] Read more.
A thunderstorm is a weather system that can trigger severe natural disasters, characterized by sudden onset, short duration, and significant damage. Accurate forecasting of thunderstorms has long been a challenging task. Data-driven artificial intelligence (AI) technologies have provided new solutions, yet AI-driven thunderstorm forecasting still lacks high-quality thunderstorm training datasets. Leveraging lightning data from the China Meteorological Administration’s Advanced Direction and Time-of-Arrival Detecting (ADTD) network and the three-dimensional Very Low Frequency/Low Frequency (VLF/LF) lightning location data of the Institute of Electrical Engineering, Chinese Academy of Sciences, we have constructed an AI training dataset for thunderstorms over China (AITDTS) through four sequential procedures: rigorous data quality control, multi-source integration, thunderstorm-prone area labeling, and feature extraction. The AITDTS encompasses 85,071 thunderstorm events and 3,973,171 corresponding gridded samples at 10 min temporal resolution and 1 km × 1 km spatial resolution across China during 2016–2023. Each sample includes location labels, 38 radar-derived physical parameters with a 10-min temporal resolution and 62 environmental parameters with an hourly temporal resolution. We further quantified predictor information gain for thunderstorm forecasting: radar echo top/base heights, composite reflectivity, vertical integrated liquid water content and reflectivity at 10 km showed high information gain. Atmospheric instability, dynamic uplifting, moisture conditions and vertical wind shear at 1 km exhibited moderate information gain. The AITDTS can be directly applied to training and evaluation of AI-driven forecasting models, offering critical data for thunderstorm nowcasting. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in Precipitation and Thunderstorm)
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24 pages, 7352 KB  
Article
Vertical Structures and Macro-Microphysical Characteristics of Southwest Vortex Precipitation over Sichuan, China
by Yanxia Liu, Jun Wen, Jiafeng Zheng and Hao Wang
Remote Sens. 2026, 18(3), 533; https://doi.org/10.3390/rs18030533 - 6 Feb 2026
Viewed by 325
Abstract
The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency [...] Read more.
The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in Precipitation and Thunderstorm)
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