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Remote Sensing
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Remote Sensing for High Impact Weather and Extremes (2nd Edition)
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Remote Sensing for High Impact Weather and Extremes (2nd Edition)

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

Deadline for manuscript submissions: 14 November 2025 | Viewed by 1430

Special Issue Editors

Prof. Dr. Zhaoxia Pu

E-Mail Website
Guest Editor
College of Mines and Earth Sciences, University of Utah, Salt Lake City, UT, USA
Interests: high-impact weather and extremes; numerical weather prediction; satellite and radar data assimilation; land-atmosphere interaction and coupled land-atmosphere data assimilation; hurricanes and tropical convection; big data; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhiqiu Gao

E-Mail Website
Guest Editor
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: surface processes: modeling and observation; near-surface layer parameterization for models; air–sea or land–sea interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme weather in the form of hurricanes, tornadoes, floods, droughts, heatwaves, and heavy precipitation can result in the loss and destruction of infrastructure, the disruption of essential services, and economic losses. Understanding and effectively monitoring these events is crucial for disaster preparedness, early-warning systems, and the development of strategies to mitigate their impacts. Remote sensing plays a vital role in the study of high-impact weather and involves the use of satellites, airborne platforms, and ground-based instruments to collect data about the Earth's atmosphere and surface. Remote sensing data provide valuable information on atmospheric conditions, precipitation patterns, cloud dynamics, and other parameters relevant to high-impact weather phenomena.

To this end, this Special Issue calls for original research, reviews, methodology papers, and case studies that demonstrate the application of remote sensing methods for monitoring and predicting high-impact weather. This Special Issue’s scope includes but is not limited to the following topics:

(1) advanced remote sensing techniques for detecting and tracking severe weather events, such as tropical cyclones, thunderstorms, and severe precipitation;
(2) integration of multi-source remote sensing data for improving weather forecasting and warning systems;
(3) Assimilation of remote sensing data for improved numerical prediction of extreme weather;
(4) Use of remote sensing for assessing the impacts of extreme weather on the environment and society;
(5) Development of new models and algorithms for processing large-scale, high-resolution remote sensing data relevant to severe weather;
(6) Evaluation of remote sensing data quality and uncertainty in impact weather studies.

This Special Issue is the second edition of “Remote Sensing for High Impact Weather and Extremes”, which can be viewed at https://www.mdpi.com/journal/remotesensing/special_issues/73632Z76ZU.

Prof. Dr. Zhaoxia Pu
Prof. Dr. Zhiqiu Gao
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
  • high-impact weather
  • extreme events
  • satellite
  • weather forecasting
  • natural hazards

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Related Special Issue

Published Papers (2 papers)

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Research

23 pages, 8102 KiB  
Article
Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data
by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He and Xingya Xi
Remote Sens. 2025, 17(13), 2155; https://doi.org/10.3390/rs17132155 - 23 Jun 2025
Viewed by 342
Abstract
Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developing precise real-time icing grids [...] Read more.
Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developing precise real-time icing grids is challenging due to the uneven distribution of monitoring stations, data confidentiality restrictions, and the limitations of existing interpolation methods. In this study, we propose a new approach for constructing real-time icing grid fields using 1339 online terminal monitoring datasets provided by the China Southern Power Grid Research Institute Co., Ltd. (CSPGRI) during the winter of 2023. Our method integrates static geographic information, dynamic meteorological factors, and ice_kriging values derived from parameter-optimized Empirical Bayesian Kriging Interpolation (EBKI) to create a spatiotemporally matched, multi-source fused icing thickness grid dataset. We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. The stacking model performed best, achieving an R-value of 0.634 (0.893), RMSE of 3.424 mm (2.834 mm), CSI of 0.514 (0.774), MAR of 0.309 (0.091), FAR of 0.332 (0.161), and fbias of 1.034 (1.084), respectively, when comparing predicted icing values with actual measurements on pylons. Additionally, we employed the SHAP model to provide a physical interpretation of the stacking model, confirming the independence of selected features. Meteorological factors such as relative humidity (RH), 10 m wind speed (WS10), 2 m temperature (T2), and precipitation (PRE) demonstrated a range of positive and negative contributions consistent with the observed growth of icing. Thus, our multi-source remote-sensing data-fusion approach, combined with the stacking model, offers a highly accurate and interpretable solution for generating real-time icing grid fields. Full article
(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes (2nd Edition))
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24 pages, 9947 KiB  
Article
Detection and Spatiotemporal Distribution Analysis of Vertically Developing Convective Clouds over the Tibetan Plateau and East Asia Using GEO-KOMPSAT-2A Observations
by Haokai Kang, Hongqing Wang, Qiong Wu and Yan Zhang
Remote Sens. 2025, 17(8), 1427; https://doi.org/10.3390/rs17081427 - 17 Apr 2025
Viewed by 550
Abstract
Vertically developing convective clouds (VDCCs), characterized by cloud-top ascent and cooling, are critical precursors to severe convective weather due to their association with intense updrafts. However, existing studies are constrained by limited spatiotemporal resolution of data and tracking methodologies, hindering real-time and pixel-level [...] Read more.
Vertically developing convective clouds (VDCCs), characterized by cloud-top ascent and cooling, are critical precursors to severe convective weather due to their association with intense updrafts. However, existing studies are constrained by limited spatiotemporal resolution of data and tracking methodologies, hindering real-time and pixel-level capture of VDCC evolution. Furthermore, large-scale statistical analyses of VDCC spatiotemporal distribution remain scarce compared with mature convective systems, particularly in topographically complex regions like the Tibetan Plateau (TP). To address these challenges, we integrated an optical flow algorithm (for dense atmospheric motion vector (AMV) retrieval) with cloud-top cooling rates (CTCRs, as indicators of vertical development), leveraging the high spatiotemporal resolution and multispectral capabilities of the GEO-KOMPSAT-2A (GK2A) satellite. This approach achieved pixel-level VDCC detection at 10 min intervals across diurnal cycles, enabling comprehensive statistical analysis. Based on this technical foundation, the most important finding in the study was the distinct convective spatiotemporal distribution over the TP and East Asia (EA) by analyzing VDCC detection data in three summers (2021–2023). Specifically, VDCC diurnal peaks preceded precipitation by 2–3 h, confirming their precursor roles in both study regions. Regional comparisons revealed that topographic and thermal forcing strongly influenced VDCC distribution patterns. The TP exhibited earlier and more frequent daytime convection at middle-to-low levels than EA, driven by intense thermal forcing, yet vertical development was limited by moisture scarcity. In contrast, EA’s monsoonal moisture sustained deeper convection, with more VDCCs penetrating the upper troposphere. The detection and statistical studies of VDCCs offer new insights into convective processes over the TP and surrounding regions, offering potential improvements in severe weather monitoring and early warning systems. Full article
(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes (2nd Edition))
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