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AI Applications to Remote Sensing of Cloud and Precipitation: Monitoring, Modeling, and Prediction

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

Deadline for manuscript submissions: 30 December 2025 | Viewed by 816

Special Issue Editors

Dr. Junqiang Yao

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Guest Editor
Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
Interests: climate variability; climate change; extreme climate events; extreme precipitation; drought; moisture cycle; atmospheric water vapor; evapotranspiration
Special Issues, Collections and Topics in MDPI journals
Dr. Jinfang Yin

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Guest Editor
State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing, China
Interests: convective cloud; hydrometeors; cumulus
Dr. Haoran Li

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Guest Editor
State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing, China
Interests: radar meteorology; cloud and precipitation physics
Special Issues, Collections and Topics in MDPI journals
Dr. Chang Yi

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Guest Editor
Key Laboratory of Cloud-Precipitation Physics and Weather Modification, China Meteorological Administration, CMA Weather Modification Centre, Beijing 100081, China
Interests: cloud and precipitation; convection; cloud physics; Tibetan Plateau; cloud macro- and microphysical properties
Special Issues, Collections and Topics in MDPI journals
Dr. Ismail Gultepe

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Guest Editor
Engineering and Applied Science Department, Ontario Technical University, Oshawa, ON L1G 0C5, Canada.
Interests: clouds; cold weather systems; cloud microphysics; precipitation; arctic weather; aviation meteorology; aircraft and ground-based in situ and remote sensing observations of the atmosphere, including satellites, radars, lidars, as well as microwave radiometers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clouds and precipitation are integral components of Earth's hydrological and energy cycles, exerting profound impacts on climate dynamics, weather extremes, water resource management, and disaster mitigation. Over recent decades, remote sensing technologies—including satellite-based sensors, ground-based radars, and in situ airborne platforms—have revolutionized our ability to observe and quantify atmospheric processes at the mesoscales to global scales. However, the complexity and variability of cloud microphysics, precipitation systems, and their  interaction with land/ocean surfaces continue to pose significant challenges to accurate observing, monitoring, numerical modeling, and prediction. The advance of artificial intelligence (AI), particularly deep learning (DL) and linear models (LMs), has led to new frontiers in addressing these challenges. AI-driven approaches enhance the extraction of actionable insights from vast, heterogeneous remote sensing datasets, enabling improved spatiotemporal resolution, real-time processing, and predictive capabilities. Integrating AI techniques with remote sensing observations may offer unprecedented opportunities to advance our understanding of cloud-precipitation microphysics, dynamics, and thermodynamics, refining forecast and climate models, improving the effectiveness of cloud water utilization, and developing applications for sustainable resource management and disaster resilience.

In this Special Issue, we invite submissions of scientific research papers that use remote sensing analysis and those on AI methodologies to address cloud and precipitation monitoring and prediction challenges. The scope for this Special Issue includes the following research directions:

  • Remote sensing of clouds and precipitation;
  • AI applications and ML;
  • Multi-sensor data fusion and assimilation;
  • Precipitation nowcasting ;
  • Cloud macro- and microphysics
  • Aerosol–cloud–precipitation interactions;
  • RS applications to cloud seeding theories and techniques;
  • Hydrological modeling and water resource management;
  • Satellite analysis in extreme precipitation forecasting;
  • Extreme weather event detection (floods, droughts, cyclones, etc.);
  • RS applications in studying climate change impacts on precipitation regimes;
  • GPS/GNSS meteorology.

Dr. Junqiang Yao
Dr. Jinfang Yin
Dr. Haoran Li
Dr. Chang Yi
Dr. Ismail Gultepe
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 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

  • remote sensing techniques
  • clouds and precipitation
  • AI applications for RS
  • cloud microphysics
  • cloud seeding
  • extreme weather events
  • data fusion and assimilation
  • RS for hydrological analysis
  • atmospheric water vapor/moisture

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 6329 KiB  
Article
Mesoscale Analysis and Numerical Simulation of an Extreme Precipitation Event on the Northern Slope of the Middle Kunlun Mountains in Xinjiang, China
by Chenxiang Ju, Man Li, Xia Yang, Yisilamu Wulayin, Ailiyaer Aihaiti, Qian Li, Weilin Shao, Junqiang Yao and Zonghui Liu
Remote Sens. 2025, 17(14), 2519; https://doi.org/10.3390/rs17142519 - 19 Jul 2025
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Abstract
Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of [...] Read more.
Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article
(This article belongs to the Special Issue AI Applications to Remote Sensing of Cloud and Precipitation: Monitoring, Modeling, and Prediction)
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18 pages, 3393 KiB  
Article
An Investigation of the Characteristics of the Mei–Yu Raindrop Size Distribution and the Limitations of Numerical Microphysical Parameterization
by Zhaoping Kang, Zhimin Zhou, Yinglian Guo, Yuting Sun and Lin Liu
Remote Sens. 2025, 17(14), 2459; https://doi.org/10.3390/rs17142459 - 16 Jul 2025
Viewed by 221
Abstract
This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR [...] Read more.
This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR) are slightly underestimated relative to RG measurements. Both observations and simulations identify 1–3 mm raindrops as the dominant precipitation contributors, though the model overestimates small and large drop contributions. At low RR, decreased small-drop and increased large-drop concentrations cause corresponding leftward and rightward RSD shifts with decreasing altitude—a pattern well captured by simulations. However, at elevated rainfall rates, the simulated concentration of large raindrops shows no significant increase, resulting in negligible rightward shifting of RSD in the model outputs. Autoconversion from cloud droplets to raindrops (ATcr), collision and breakup between raindrops (AGrr), ice melting (MLir), and evaporation of raindrops (VDrv) contribute more to the number density of raindrops. At 0.1 < RR < 1 mm·h−1, ATcr dominates, while VDrv peaks in this intensity range before decreasing. At higher intensities (RR > 20 mm·h−1), AGrr contributes most, followed by MLir. When the RR is high enough, the breakup of raindrops plays a more important role than collision, leading to a decrease in the number density of raindrops. The overestimation of raindrop breakup from the numerical parameterization may be one of the reasons why the RSD does not shift significantly to the right toward the surface under the heavy RR grade. The RSD near the surface varies with the RR and characterizes surface precipitation well. Toward the surface, ATcr and VDrv, but not AGrr, become similar when precipitation approaches. Full article
(This article belongs to the Special Issue AI Applications to Remote Sensing of Cloud and Precipitation: Monitoring, Modeling, and Prediction)
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