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Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3074

Special Issue Editors


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Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: radar-based quantitative precipitation estimation; short-term quantitative precipitation forecast
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Shenzhen National Climate Observatory, Shenzhen, China
Interests: radar QPE methods; raindrop size distribution (DSD) characteristics; high-impact weather
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Guest Editor
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: hydrology; hydrological modeling; inverse modeling; catchment; baseflow
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Guest Editor
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: radar hydrology; hydrometeorological disasters

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Guest Editor
Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, 229 Butler-Carlton Hall,1401 N. Pine St., Rolla, MO 65409, USA
Interests: radar hydrology; rainfall uncertainties
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Special Issue Information

Dear Colleagues,

In 2019, the World Meteorological Organization (WMO) highlighted that, according to statistics from 2007 to 2019, 90% of the losses caused by natural disasters are related to meteorology, of which heavy storms and floods account for more than 70%. Heavy precipitation plays a very important role in the early warning of meteorological, hydrological and geological disasters. The heavy rainstorms induced by strong convection often cause serious natural disasters such as floods, landslides, and mudslides. Accurate monitoring, early warning, and forecasting of heavy rainfall induced by strong convection are central to improving the ability to prevent these disasters.

Recently, remote sensing techniques such as radar and satellite have become powerful tools for monitoring natural hazards such as flash floods and waterlogging induced by heavy rainstorms. Advanced remote sensing-based products such as QPE and QPF are extremely helpful for short-term weather and hydrological forecasting. Also, dual-polarization or dual-frequency radar data and satellite data are used to assess water mixing ratios and winds, and to improve the capability of convection-permitting numerical weather prediction (NWP) models to forecast severe storms at scales varying from a few hundred meters to kilometers. Associated surface in situ observation equipment, such as rain gauges, runoff gauges, and distrometers, is also required for calibrating the observational variables and products of radars and satellites.

Although such remote sensing equipment has been widely used in weather and hydrological monitoring and forecasting, several valid challenges remain:

  • Developing radar and satellite signal processing methods;
  • Assessing observational quality for newly developed radars and satellites;
  • Characterizing errors/uncertainties in remote sensing precipitation products and retrieval algorithm functions of different conditions, e.g., elevations, storms, and climatic regimes, and communicating the uncertainties for hydrogeological applications;
  • Developing more accurate ground radar- and/or satellite-based quantitative precipitation estimation (QPE) algorithms;
  • New sensing, attenuation correction, and calibration techniques;
  • The application of radar and satellite data in data assimilation to improve the performance of NWP models;
  • Developing new analysis methods, including machine learning and data assimilation, to maximize the benefits of using extensive datasets, multiscale remote sensing data, and in-situ data fusion;
  • Artificial intelligence and machine (deep) learning applications;
  • The application and analysis of radar and satellite data in disastrous weather conditions (e.g. heavy rain, flash floods, and waterlogging);
  • Radar and satellite observations of hydrometeorological extremes;
  • Improving quantitative precipitation forecasting (QPF) skills;
  • Improving the monitoring and forecasting of heavy rainfall for hydrometeorological hazards warnings triggered by remote sensing products;
  • Improving the ability of convection-induced flood forecasting and early warning in small mountain basins and urban areas with remote sensing products;
  • Improving flood simulation and forecasting capabilities for hydrological modelling using remote sensing products combining other types of precipitation data;
  • Improving the forecasting of and early warning capabilities for geological disasters, such as landslides and mudslides caused by convective precipitation, with radar and satellite products.

Dr. Youcun Qi
Dr. Zhe Zhang
Dr. Zhanfeng Zhao
Dr. Donghuan Li
Dr. Bong-Chul Seo
Guest Editors

Manuscript Submission Information

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Keywords

  • weather radars
  • satellites
  • flash floods and waterlogging
  • quantitative precipitation estimation (QPE)
  • remote sensing data assimilation
  • extreme weather and hydrological events
  • artificial intelligence and machine (deep) learning
  • application of remote sensing equipment in geological disaster, disastrous weather analysis, and observation of hydrometeorological extremes
  • weather and hydrological forecast

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Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 36489 KiB  
Article
Comparison of the Morrison and WDM6 Microphysics Schemes in the WRF Model for a Convective Precipitation Event in Guangdong, China, Through the Analysis of Polarimetric Radar Data
by Xiaolong Chen and Xiaoli Liu
Remote Sens. 2024, 16(19), 3749; https://doi.org/10.3390/rs16193749 - 9 Oct 2024
Viewed by 516
Abstract
Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. [...] Read more.
Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N0) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N0 of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N0 might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes. Full article
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22 pages, 9082 KiB  
Article
An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
by Yubao Chen, Lu Li, Fei Ye, Boshi Kang, Xiaopeng Wang, Zhichao Bu, Moyan Zhu, Qian Yang, Nan Shao and Jianyun Zhang
Remote Sens. 2024, 16(7), 1153; https://doi.org/10.3390/rs16071153 - 26 Mar 2024
Cited by 1 | Viewed by 824
Abstract
The quality of weather radar affects the reliability and effectiveness of monitoring severe convective weather. Therefore, rigorous calibration and validation are the foundation for the quantitative application of weather radar. Among the available methods, radial velocity validation is of great significance for reducing [...] Read more.
The quality of weather radar affects the reliability and effectiveness of monitoring severe convective weather. Therefore, rigorous calibration and validation are the foundation for the quantitative application of weather radar. Among the available methods, radial velocity validation is of great significance for reducing the false alarm rate in the identification of tornadoes and thunderstorms. Based on the traditional method that utilizes internal and external instrument radar velocity measurements, we propose a weather radar radial velocity validation method that uses RTK UAV to simulate external targets. In addition, according to the characteristics of the UAV application scenarios, we introduce the evaluation parameter of optimal absolute accuracy to supplement the original parametric system. The experimental results show that the evaluation parameter of optimal absolute accuracy can effectively reduce the interference caused by the systematic deviation of the UAV due to the internal and external environment, which can affect the validation results. When the UAV velocity is not greater than 10 m/s, the optimal absolute accuracy of the radial velocity validation is less than 0.05 m/s, which is essentially consistent with the external instruments’ measurement results. This method can be effectively applied to the procedural handling of weather radar radial velocity validation. It is significant for ensuring the accuracy and quality of weather radar radial velocity measurements and improving the effectiveness of radar velocity data applications. Full article
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24 pages, 15061 KiB  
Article
A Case Study on Two Differential Reflectivity Columns in a Convective Cell: Phased-Array Radar Observation and Cloud Model Simulation
by Gang Ren, Yue Sun, Hongping Sun, Yaning Dong, Yonglong Yang and Hui Xiao
Remote Sens. 2024, 16(3), 460; https://doi.org/10.3390/rs16030460 - 25 Jan 2024
Viewed by 978
Abstract
A convective cell storm containing two differential reflectivity (ZDR) columns was observed with a dual-polarization phased-array radar (X-PAR) in Xixian County. Since a ZDR column is believed to correspond to a strong updraft and a single convective cell is considered [...] Read more.
A convective cell storm containing two differential reflectivity (ZDR) columns was observed with a dual-polarization phased-array radar (X-PAR) in Xixian County. Since a ZDR column is believed to correspond to a strong updraft and a single convective cell is considered to have a simple dynamic structure with one updraft core, how these two ZDR columns form and coexist is the focus of this study. The dynamic and microphysical structures around the two ZDR columns are studied under the mutual confirmation of the X-PAR observations and a cloud model simulation. The main ZDR column forms and maintains in an updraft whose bottom corresponds to a convergence of low-level and mid-level flow; it lasts from the early stages to the later stages. The secondary ZDR column emerges at the rear of the horizontal reflectivity (ZH) core relative to the moving direction of the cell; it forms in the middle stages and lasts for a shorter period, and its formation is under an air lifting forced by the divergent outflow of precipitation. Therefore, the secondary ZDR column is only a by-product in the middle stages of the convection rather than an indicator of a new or enhanced convection. Full article
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