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Precipitation, Flood and Earthquake Events Monitoring, Simulation, Analysis and Early Warning by Advanced Environmental Remote Sensing and AI

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 5764

Special Issue Editors

Prof. Dr. Congzheng Han

E-Mail Website
Guest Editor
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: microwave remote sensing; microwave links; microwave radar; wireless communication; signal processing; environmental monitoring; unmanned aerial vehicles; sensors network; energy harvesting; atmospheric science
Dr. Jiamou Liu

E-Mail Website
Guest Editor
School of Computer Science, University of Auckland, Auckland 1142, New Zealand
Interests: artificial intelligence in remote sensing
Prof. Dr. Hongbin Chen

E-Mail Website
Guest Editor
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: radar meteorology; satellite meteorology

Special Issue Information

Dear Colleagues,

Climate change has led to an increase in the frequency of natural disasters and extreme weather events, such as heavy precipitation, flooding, and earthquakes, along with their impacts, raising significant concern.

The accuracy of short-term heavy rainfall forecasting remains low, resulting in unreliable early warnings for extreme rainstorms. The accurate and high-resolution monitoring of the occurrence location, timing, intensity, and temporal–spatial variation trends in heavy rain is essential for realizing more detailed and precise short-term heavy rain forecasting. Scientific experimental observations, combined with systematic analysis, help to reveal the mechanisms behind disaster-causing short-term heavy rainfall.

The environment for underground space disasters is complex, with hidden inducing factors and difficulties in obtaining "black box" information during disasters. New methods for obtaining high-precision information are needed for studying the causes and mechanisms of urban underground space disasters (flooding, subsidence, and collapse) under changing environments.

Advanced technologies, such as microwave detection, remote sensing, radar, and optical fiber sensing, can be utilized to construct an integrated sky–ground and underground three-dimensional monitoring network.

This Special Issue aims to include papers which discuss, but are not limited to, the following topics:

  • Advances and new findings that enhance the accuracy of precipitation monitoring and short-term precipitation nowcasting;
  • Exploring high-resolution fiber-optic acoustic sensing equipment and imaging technologies for geophysical exploration;
  • Research on physical models and numerical methods for flood prediction, simulation, monitoring, analysis, and early warning;
  • Disaster monitoring and disaster mitigation and avoidance simulation;
  • The advancement of environmental remote sensing and AI technologies and their applications in disaster prevention and management.

Prof. Dr. Congzheng Han
Dr. Jiamou Liu
Prof. Dr. Hongbin Chen
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

  • radar
  • satellite
  • microwave links
  • AI and algorithms
  • flood and drought prediction, simulation, modeling, and analysis
  • short-term precipitation forecasting
  • precipitation monitoring
  • earthquake prediction and monitoring and geophysical exploration
  • technology for disaster information acquisition
  • disaster monitoring and disaster mitigation and avoidance simulation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

17 pages, 4721 KiB  
Article
Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms
by Zhan Zhang, Qingping Song, Minzheng Duan, Hailei Liu, Juan Huo and Congzheng Han
Remote Sens. 2025, 17(7), 1123; https://doi.org/10.3390/rs17071123 - 21 Mar 2025
Viewed by 469
Abstract
Nowcasting is a critical technology for disaster prevention and mitigation, and the accuracy of radar echo extrapolation directly impacts forecasting performance. In most deep learning-based models, accurately predicting heavy precipitation remains a challenging task. Focusing on the region of China, this study proposes [...] Read more.
Nowcasting is a critical technology for disaster prevention and mitigation, and the accuracy of radar echo extrapolation directly impacts forecasting performance. In most deep learning-based models, accurately predicting heavy precipitation remains a challenging task. Focusing on the region of China, this study proposes an improved model based on residual and attention mechanisms—RA-UNet—for precipitation nowcasting with a lead time of 3 h. The model introduces the residual neural network (ResNet) and the convolutional block attention module (CBAM) to integrate multi-scale features into the U-Net encoder–decoder architecture, enhancing its ability to capture the spatiotemporal evolution of precipitation systems. Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. To evaluate the model’s performance, experiments were conducted using 6 min resolution radar echo data from China in 2024, with comparisons made against the optical flow (OF) method and the U-Net model. The experimental results show that RA-UNet demonstrates significant advantages in 3 h forecasting: its mean absolute error (MAE) is reduced by approximately 7%, the false alarm rate (FAR) decreases by about 20%, and it outperforms the comparison models in metrics such as the critical success index (CSI) and structural similarity index (SSIM). Notably, RA-UNet effectively mitigates intensity degradation in long-term forecasts, successfully predicting the trend of >40 dBZ strong echo cores in two typical cases and significantly improving the premature dissipation problem of precipitation fields. This study provides a new approach to refined forecasting of complex precipitation systems, and future work will combine multi-source data fusion with physical constraint mechanisms to further enhance precipitation event prediction capabilities. Full article
(This article belongs to the Special Issue Precipitation, Flood and Earthquake Events Monitoring, Simulation, Analysis and Early Warning by Advanced Environmental Remote Sensing and AI)
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31 pages, 14554 KiB  
Article
The Spatiotemporal Fluctuations of Extreme Rainfall and Their Potential Influencing Factors in Sichuan Province, China, from 1970 to 2022
by Lin Bai, Tao Liu, Agamo Sha and Dinghong Li
Remote Sens. 2025, 17(5), 883; https://doi.org/10.3390/rs17050883 - 1 Mar 2025
Viewed by 872
Abstract
Utilizing daily data gathered from 63 meteorological stations across Sichuan Province between 1970 and 2022, this study investigates the spatial and temporal shifts in extreme precipitation patterns, alongside the connections between changes in extreme precipitation indices (EPIs) and the underlying drivers, such as [...] Read more.
Utilizing daily data gathered from 63 meteorological stations across Sichuan Province between 1970 and 2022, this study investigates the spatial and temporal shifts in extreme precipitation patterns, alongside the connections between changes in extreme precipitation indices (EPIs) and the underlying drivers, such as geographic characteristics and atmospheric circulation influences, within the region. The response of precipitation to these factors was examined through various methods, including linear trend analysis, the Mann–Kendall test, cumulative anomaly analysis, the Pettitt test, R/S analysis, Pearson correlation analysis, and wavelet transformation. The findings revealed that (1) Sichuan Province’s EPIs generally show an upward trend, with the simple daily intensity index (SDII) demonstrating the most pronounced increase. Notably, the escalation in precipitation indices was more substantial during the summer months compared to other seasons. (2) The magnitude of extreme precipitation variations showed a rising pattern in the plateau regions of western and northern Sichuan, whereas a decline was observed in the central and southeastern basin areas. (3) The number of days with precipitation exceeding 5 mm (R5mm), 10 mm (R10mm), and 20 mm (R20mm) all exhibited a significant change point in 2012, surpassing the 95% significance threshold. The future projections for EPIs, excluding consecutive dry days (CDDs), align with historical trends and suggest a continuing possibility of an upward shift. (4) Most precipitation indices, with the exception of CDDs, demonstrated a robust positive correlation with longitude and a negative correlation with both latitude and elevation. Except for the duration indicators (CDDs, CWDs), EPIs generally showed a gradual decrease with increasing altitude. (5) Atmospheric circulation patterns were found to have a substantial impact on extreme precipitation events in Sichuan Province, with the precipitation indices showing the strongest associations with the Atlantic Multidecadal Oscillation (AMO), the Sea Surface Temperature of the East Central Tropical Pacific (Niño 3.4), and the South China Sea Summer Monsoon Index (SCSSMI). Rising global temperatures and changes in subtropical high pressure in the western Pacific may be deeper factors contributing to changes in extreme precipitation. These insights enhance the understanding and forecasting of extreme precipitation events in the region. Full article
(This article belongs to the Special Issue Precipitation, Flood and Earthquake Events Monitoring, Simulation, Analysis and Early Warning by Advanced Environmental Remote Sensing and AI)
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24 pages, 924 KiB  
Article
DRAF-Net: Dual-Branch Residual-Guided Multi-View Attention Fusion Network for Station-Level Numerical Weather Prediction Correction
by Kaixin Chen, Jiaxin Chen, Mengqiu Xu, Ming Wu and Chuang Zhang
Remote Sens. 2025, 17(2), 206; https://doi.org/10.3390/rs17020206 - 8 Jan 2025
Cited by 1 | Viewed by 659
Abstract
Accurate station-level numerical weather predictions are critical for disaster prevention and mitigation, with error correction playing an essential role. However, existing correction models struggle to effectively handle the high-dimensional features and complex dependencies inherent in meteorological data. To address these challenges, this paper [...] Read more.
Accurate station-level numerical weather predictions are critical for disaster prevention and mitigation, with error correction playing an essential role. However, existing correction models struggle to effectively handle the high-dimensional features and complex dependencies inherent in meteorological data. To address these challenges, this paper proposes the dual-branch residual-guided multi-view attention fusion network (DRAF-Net), a novel deep learning-based correction model. DRAF-Net introduces two key innovations: (1) a dual-branch residual structure that enhances the spatial sensitivity of deep high-dimensional features and improves output stability by connecting raw data and shallow features to deep features, respectively; and (2) a multi-view attention fusion mechanism that models spatiotemporal influences, temporal dynamics, and spatial associations, significantly improving the representation of complex dependencies. The effectiveness of DRAF-Net was validated on two real-world datasets comprising observations and predictions from Chinese meteorological stations. It achieved an average RMSE reduction of 83.44% and an average MAE reduction of 84.21% across all eight variables, significantly outperforming other methods. Moreover, extensive studies confirmed the critical contributions of each key component, while visualization results highlighted the model’s ability to eliminate anomalous values and improve prediction consistency. The code will be made publicly available to support future research and development. Full article
(This article belongs to the Special Issue Precipitation, Flood and Earthquake Events Monitoring, Simulation, Analysis and Early Warning by Advanced Environmental Remote Sensing and AI)
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20 pages, 3073 KiB  
Article
Successful Precipitation Downscaling Through an Innovative Transformer-Based Model
by Fan Yang, Qiaolin Ye, Kai Wang and Le Sun
Remote Sens. 2024, 16(22), 4292; https://doi.org/10.3390/rs16224292 - 18 Nov 2024
Cited by 1 | Viewed by 1377
Abstract
In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained [...] Read more.
In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. Full article
(This article belongs to the Special Issue Precipitation, Flood and Earthquake Events Monitoring, Simulation, Analysis and Early Warning by Advanced Environmental Remote Sensing and AI)
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18 pages, 9930 KiB  
Article
A Comparative Study of Cloud Microphysics Schemes in Simulating a Quasi-Linear Convective Thunderstorm Case
by Juan Huo, Yongheng Bi, Hui Wang, Zhan Zhang, Qingping Song, Minzheng Duan and Congzheng Han
Remote Sens. 2024, 16(17), 3259; https://doi.org/10.3390/rs16173259 - 2 Sep 2024
Cited by 1 | Viewed by 1431
Abstract
An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant [...] Read more.
An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain’s topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes. Full article
(This article belongs to the Special Issue Precipitation, Flood and Earthquake Events Monitoring, Simulation, Analysis and Early Warning by Advanced Environmental Remote Sensing and AI)
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