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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) belonging to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 19973

Editors

Dr. Youcun Qi

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Zhe Zhang

E-Mail Website
Guest Editor
Shenzhen National Climate Observatory, Shenzhen, China
Interests: radar QPE methods; raindrop size distribution (DSD) characteristics; high-impact weather
Special Issues, Collections and Topics in MDPI journals
Dr. Zhanfeng Zhao

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Donghuan Li

E-Mail Website
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
Dr. Bong-Chul Seo

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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-anonymized 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

  • 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 (9 papers)

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Research

Jump to: Review

31 pages, 21987 KB  
Article
An Enhanced Nonlinear Grid Transformation Method for Weather Radar Echo Extrapolation
by Tao Yang, Huiling Yang, Yue Sun, Shengchao Li and Zhaowu Liu
Remote Sens. 2026, 18(17), 2865; https://doi.org/10.3390/rs18172865 - 24 Aug 2026
Viewed by 216
Abstract
In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced [...] Read more.
In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced NGT (ENGT) method. By extending the nonlinear transformation matrix to include radar reflectivity as the third dimension in addition to the grid coordinates X and Y, a 3 × 9 transformation matrix is used to describe the continuous spatial variation in the radar reflectivity field. The transformation matrix is solved using historical near-term data, enabling the extrapolation of subsequent time steps. In a set of ideal extrapolation experiments combining translation, temporal increments, and path variations, the ENGT method demonstrated better qualitative and conceptual performance than the NGT and traditional optical flow (OF) methods. In a real squall line case, the ENGT method could predict the overall movement direction of the cloud system synthesized by moving and emerging cells. In a real enhanced convective cloud cluster case, the ENGT method achieved higher scores because it generated stronger reflectivity. Although there are still mathematically unsolved and statistically insignificant problems, the ENGT method shows potential in predicting strong reflectivity, and the computational efficiency for a single weather radar is considerable. Full article
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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27 pages, 11400 KB  
Article
Characterizing Short-Duration Summer Rainstorms in Nanjing, China, Using Multi-Source Remote Sensing and Explainable AI
by Yiding Wang, Ningxin Yong, Siyu Zhu and Yang Hong
Remote Sens. 2026, 18(13), 2212; https://doi.org/10.3390/rs18132212 - 5 Jul 2026
Viewed by 461
Abstract
With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s [...] Read more.
With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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29 pages, 4416 KB  
Article
Flood Susceptibility and Potential Flood Risk Assessment in Afghanistan Using Morphometric and Socioeconomic Indicators
by Qutbudin Ishanch, Kanchan Mishra, Christiane Zarfl and Kathryn E. Fitzsimmons
Remote Sens. 2026, 18(9), 1411; https://doi.org/10.3390/rs18091411 - 2 May 2026
Viewed by 1364
Abstract
Afghanistan is highly vulnerable to climate-driven extremes because of its combination of rugged geography and socio-political instability. Frequent events of extreme precipitation, floods, and droughts pose severe socio-economic and environmental challenges. Floods are particularly destructive, yet national-scale potential flood risk in Afghanistan has [...] Read more.
Afghanistan is highly vulnerable to climate-driven extremes because of its combination of rugged geography and socio-political instability. Frequent events of extreme precipitation, floods, and droughts pose severe socio-economic and environmental challenges. Floods are particularly destructive, yet national-scale potential flood risk in Afghanistan has not been systematically assessed, largely due to limited data and field access. This study addresses this gap by mapping flood susceptibility, vulnerability, and risk using remote sensing (RS) and geographic information systems (GIS) at both subbasin and provincial scales. We apply a hybrid approach that combines Principal Component Analysis (PCA) to identify key environmental, climatic, and socio-economic indicators with the Analytic Hierarchy Process (AHP) to derive consistent weights and reduce subjectivity in decision-making. The results show that the eastern and northeastern ssubbasins especially within the Panj-Amu and Kabul River basins, have the highest flood susceptibility due to intense precipitation, steep terrain, and efficient drainage. Vulnerability increases in the densely populated northern and northeastern provinces, where land-use change and socio-economic constraints elevate flood-related impacts. Overall, 31% and 20% of study areas are classified as Very High and High vulnerability zones, respectively. The composite potential flood-risk index identifies that approximately 24% and 22% of Afghanistan fall within Very High and High flood risk zones, concentrated in the northern and eastern provinces. Model performance, evaluated using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC), indicates strong agreement between mapped Very High/High risk zones and frequently flooded provinces, with the upper-threshold scenario yielding an AUC of 0.913. These findings support targeted resource allocation, mitigation planning, and disaster-risk reduction in data-scarce and conflict-affected mountain regions. Full article
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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29 pages, 7104 KB  
Article
The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects
by Xinlei Tang, Qian Chen, Jianping Guo, Jing Yang, Zeyong Zou, Jinghua Chen and Yue Sun
Remote Sens. 2025, 17(5), 778; https://doi.org/10.3390/rs17050778 - 23 Feb 2025
Cited by 3 | Viewed by 1581
Abstract
Thermally driven local-scale precipitation (LSP) is an important type of summer precipitation over China, but the prestorm environmental conditions remain unclear. In order to investigate the major factors controlling the LSP intensity, the meteorological parameters preceding the occurrence of light and heavy afternoon [...] Read more.
Thermally driven local-scale precipitation (LSP) is an important type of summer precipitation over China, but the prestorm environmental conditions remain unclear. In order to investigate the major factors controlling the LSP intensity, the meteorological parameters preceding the occurrence of light and heavy afternoon LSP over Eastern China during 2018–2022 are examined using rain gauge, radiosonde sounding, and satellite observations. The temperature differences between heavy and light LSP events are relatively small, but heavy LSP events exhibit larger water vapor mixing ratios (Qv) below a 5 km altitude than light LSP. With an almost identical vertical temperature distribution, an increment in Qv increases the relative humidity (RH) in the lower troposphere. Furthermore, large eddy simulations with spectral bin microphysics are performed to investigate the impacts of humidity and aerosols on the LSP intensity. Increased low-level RH leads to larger mass concentrations of rain and graupel at the expense of cloud droplets due to enhanced drop collisions and the riming of ice particles, respectively, thereby reinforcing the LSP. However, an increased aerosol concentration leads to more cloud water but reduced rain water content, resulting mainly from suppressed drop collisions. The graupel mixing ratio exhibits a non-monotonic trend with aerosols, mostly contributed by riming. As a result, the LSP intensity first increases and then decreases with an increment in the aerosol concentration in both dry and humid air. Moreover, more aerosols lead to the humidification of the surrounding air due to the enhanced evaporation of cloud droplets, particularly under lower-RH conditions. These findings provide an enhanced understanding of the effects of covariations in humidity and aerosol concentrations on the afternoon LSP intensity over Eastern China. Full article
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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19 pages, 12556 KB  
Article
The Direct Assimilation of Radar Reflectivity Data with a Two-Moment Microphysics Scheme for a Landfalling Typhoon in an OSSE Framework
by Ziyue Wang, Jingyao Luo, Hong Li, Yijie Zhu and Rui He
Remote Sens. 2024, 16(22), 4286; https://doi.org/10.3390/rs16224286 - 17 Nov 2024
Cited by 2 | Viewed by 2214
Abstract
Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with [...] Read more.
Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with an ensemble Kalman filter (EnKF), using simulated observations in an Observing System Simulation Experiment (OSSE) framework for the landfalling typhoon In-Fa. With an OSSE, it is convenient to analyze the impact of assimilation of radar reflectivity on analysis and forecast for various state variables, especially for hydrometeors. Our results show that the direct assimilation of radar reflectivity with EnKF does not introduce non-physical hydrometeors and is able to adjust well, not only to hydrometers, but also to some large-scale variables which are not directly related to reflectivity, especially in terms of temperature and vertical velocity. Though the most notable reduction in the Root Mean Square Errors (RMSEs) is observed through mixing the ratio of rainwater and snow, the analysis of other variables is also significantly improved with the accumulation of assimilation cycles. The correlation analysis reveals the strongest correlation between radar reflectivity data and hydrometeor-related variables as well as the correlation with certain large-scale variables, indicating that these cross-variables are updated well through the reliable multivariate ensemble covariance in the EnKF. As a result, an obvious improvement in typhoon intensity and precipitation forecast is obtained in the data assimilation experiment. The impact of assimilation on radar reflectivity can last for up to 15–16 h. Full article
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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23 pages, 36489 KB  
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
Cited by 5 | Viewed by 3590
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
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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22 pages, 9082 KB  
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 7 | Viewed by 2678
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
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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24 pages, 15061 KB  
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
Cited by 8 | Viewed by 2388
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
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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Review

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28 pages, 1268 KB  
Review
Dual-Polarization Radar Quantitative Precipitation Estimation (QPE): Principles, Operations, and Challenges
by Zhe Zhang, Zhanfeng Zhao, Youcun Qi and Muqi Xiong
Remote Sens. 2025, 17(21), 3619; https://doi.org/10.3390/rs17213619 - 31 Oct 2025
Cited by 5 | Viewed by 3127
Abstract
Quantitative precipitation estimation (QPE) is one of the primary applications of weather radar. Over the last several decades, dual-polarization radars have significantly improved QPE accuracy by providing additional observational variables that offer more microphysical information about precipitation particles. In this work, we review [...] Read more.
Quantitative precipitation estimation (QPE) is one of the primary applications of weather radar. Over the last several decades, dual-polarization radars have significantly improved QPE accuracy by providing additional observational variables that offer more microphysical information about precipitation particles. In this work, we review QPE methods for dual-polarization radars and summarize their advantages and disadvantages from both theoretical and practical perspectives. The development paths and current status of operational QPE systems in the United States, China, and France are examined. We demonstrate how dual-polarization radars have improved QPE accuracy in these systems not only directly through the application of polarimetric QPE methods, but also indirectly through the more accurate identification of non-meteorological echoes, the mitigation of the partial blockage effect, and the detection of melting layers. The challenges are discussed for dual-polarization radar QPE, including the quality of polarimetric variables, QPE quality in complex terrain, estimation of surface precipitation with observations within or above the melting layer, and polarimetric QPE methods for snow. Full article
(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)
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