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Remote Sensing
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Advance of Radar Meteorology and Hydrology II
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Advance of Radar Meteorology and Hydrology II

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4478

Special Issue Editors

Dr. Sanghun Lim

E-Mail Website
Guest Editor
Korea Institute of Civil Engineering and Building Technology, Goyang-si, Republic of Korea
Interests: radar meteorology/hydrology; precipitation microphysics; precipitation identification and quantitative precipitation estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Seongsim Yoon

E-Mail Website
Guest Editor
Korea Institute of Civil Engineering and Building Technology, Goyang-si, Republic of Korea
Interests: hydrometeorology; quantitative precipitation estimation; quantitative precipitation forecast
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tremendous advances have been made in the last 30 years in the science, technology, and engineering of radars. With the development of multiple polarization, multiple wavelength, and network sensing technologies, the radar has become a widely used tool in meteorological and hydrological applications. Radar can provide the information needed for weather systems, weather forecasting, flood warning, and climate surveys.
The goal of this Special Issue is to share the recent advances in radar meteorology and hydrology. Topics of interest include, but are not limited to, the following areas:

  • New radar system concept for precipitation observation;
  • Advances in radar signal processing and quality control;
  • Cloud and precipitation microphysics;
  • Remote sensing precipitation measurement;
  • Radar meteorological and hydrological applications;
  • Remote sensing applications in climatology.

You may choose our Joint Special Issue in Climate.

Dr. Sanghun Lim
Dr. Seongsim Yoon
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 system
  • radar signal processing
  • quality control
  • quantitative precipitation estimation
  • nowcasting
  • hydrological applications
  • remote sensing
  • precipitation

Related Special Issue

Published Papers (5 papers)

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Research

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20 pages, 18270 KiB  
Article
Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil
by Fernanda F. Verdelho, Cesar Beneti, Luis G. Pavam Jr., Leonardo Calvetti, Luiz E. S. Oliveira and Marco A. Zanata Alves
Remote Sens. 2024, 16(11), 1971; https://doi.org/10.3390/rs16111971 - 30 May 2024
Abstract
In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional [...] Read more.
In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method. Full article
(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)
21 pages, 10017 KiB  
Article
Seasonal Variation in Vertical Structure for Stratiform Rain at Mêdog Site in Southeastern Tibetan Plateau
by Jiaqi Wen, Gaili Wang, Renran Zhou, Ran Li, Suolang Zhaxi and Maqiao Bai
Remote Sens. 2024, 16(7), 1230; https://doi.org/10.3390/rs16071230 - 30 Mar 2024
Viewed by 695
Abstract
Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 [...] Read more.
Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 was investigated using micro rain radar (MRR) observations, as there is a lack of similar studies in this region. The average melting layer height is the lowest in February, after which it gradually increases, reaches its peak in August, and then gradually decreases. For lower rain categories, the vertical distribution of small drops remains uniform in winter below the melting layer. The medium-sized drops show slight increases, leading to negative gradients in the microphysical profiles. Slight or evident decreases in concentrations of small drops are observed with decreasing height in the premonsoon, monsoon, and postmonsoon seasons, likely due to significant evaporation. The radar reflectivity, rain rate, and liquid water content profiles decrease with decreasing height according to the decrease in concentrations of small drops. With increasing rain rate, the drop size distribution (DSD) displays significant variations in winter, and the fall velocity decreases rapidly with decreasing height. In the premonsoon, monsoon, and postmonsoon seasons, the concentrations of large drops significantly decrease below the melting layer because of the breakup mechanism, leading to the decreases in the fall velocity profiles with decreasing height during these seasons. Raindrops with sizes ranging from 0.3–0.5 mm are predominant in terms of the total drop number concentration in all seasons. Precipitation in winter and postmonsoon seasons is mainly characterized by small raindrops, while that in premonsoon and monsoon seasons mainly comprises medium-sized raindrops. Understanding the seasonal variation in the vertical structure of precipitation in Mêdog will improve the radar quantitative estimation and the use of microphysical parameterization schemes in numerical weather forecast models over the TP. Full article
(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)
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13 pages, 4493 KiB  
Communication
Radar Echo Recognition of Gust Front Based on Deep Learning
by Hanyuan Tian, Zhiqun Hu, Fuzeng Wang, Peilong Xie, Fen Xu and Liang Leng
Remote Sens. 2024, 16(3), 439; https://doi.org/10.3390/rs16030439 - 23 Jan 2024
Viewed by 953
Abstract
Gust fronts (GFs) belong to the boundary layer convergence system. A strong GF can cause serious wind disasters, so its automatic monitoring and identification are very helpful but difficult in daily meteorological operations. By collecting convective weather processes in Hubei, Jiangsu, and other [...] Read more.
Gust fronts (GFs) belong to the boundary layer convergence system. A strong GF can cause serious wind disasters, so its automatic monitoring and identification are very helpful but difficult in daily meteorological operations. By collecting convective weather processes in Hubei, Jiangsu, and other regions of China, 1422 GFs from 106 S-band new-generation weather radar (CINRAD/SA) volume scan data are labeled as positive samples by means of human–computer interaction, and the same number of negative samples are randomly tagged from no GF radar data. A deep learning dataset including 2844 labels with a positive and negative sample ratio of 1:1 is constructed, and 80%, 10%, and 10% of the dataset are separated as training, validation, and test sets, respectively. Then, the training dataset is expanded to 273,120 samples by data augmentation technology. Since the height of a GF is generally less than 1.5 km, three deep-learning-based models are trained for GF automatic recognition according to the distance from the radars. Three models (M1, M2, M3) are trained with the data at a 0.5° elevation angle from 65 to 180 km away from the radars, at 0.5° and 1.5° angles from 40 to 65 km, and at 0.5°, 1.5°, and 2.4° angles within 40 km, respectively. The precision, confusion matrix, and its derived indicators including receiver operating characteristic curve (ROC) and the area under ROC (AUC) are used to evaluate the three models by the test set. The results show that the identification precisions of the models are 97.66% (M1), 90% (M2), and 90.43% (M3), respectively. All the hit rates are over 89%, the false positive rates are less than 11%, and the critical success indexes (CSIs) surpass 82%. In addition, all the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.93. These results suggest that the three models can effectively achieve the automatic discrimination of GFs. Finally, the models are demonstrated by three GF events detected with Qingpu, Nantong, and Cangzhou radars. Full article
(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)
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12 pages, 29525 KiB  
Communication
Design and Implementation of K-Band Electromagnetic Wave Rain Gauge System
by Jeongho Choi and Sanghun Lim
Remote Sens. 2024, 16(1), 6; https://doi.org/10.3390/rs16010006 - 19 Dec 2023
Viewed by 793
Abstract
In order to prevent and manage damage caused by localized torrential downpours, the quantitative observation of rainfall is crucial. Considering the spatial complexity and vertical variability of rainfall, it is important to obtain low-altitude, high-resolution radar observations to reduce uncertainty in radar rainfall [...] Read more.
In order to prevent and manage damage caused by localized torrential downpours, the quantitative observation of rainfall is crucial. Considering the spatial complexity and vertical variability of rainfall, it is important to obtain low-altitude, high-resolution radar observations to reduce uncertainty in radar rainfall estimates. In this paper, we present an electromagnetic wave rainfall gauge system (EWRG) that detects rainfall within the observation area and estimates the areal rainfall using electromagnetic waves. The EWRG system was developed based on a subminiature size antenna, a K-band dual-polarization transceiver, and advanced high-resolution, high-speed signal processing technology. The system design and signal processing techniques are described in detail. The EWRG has the advantage of overcoming the limitations of conventional cylindrical ground rain gauges, such as the contamination and spatial inaccuracy of rain gauges, which cause uncertainty in quantitative precipitation measurement. Full article
(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)
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17 pages, 70334 KiB  
Technical Note
Assessment of Deep Learning-Based Nowcasting Using Weather Radar in South Korea
by Seong-Sim Yoon, Hongjoon Shin, Jae-Yeong Heo and Kwang-Bae Choi
Remote Sens. 2023, 15(21), 5197; https://doi.org/10.3390/rs15215197 - 31 Oct 2023
Cited by 1 | Viewed by 1420
Abstract
This study examines the effectiveness of various deep learning algorithms in nowcasting using weather radar data from South Korea. Herein, the algorithms examined include RainNet, ConvLSTM2D U-Net, a U-Net-based recursive model, and a generative adversarial network. Moreover, this study used S-band radar data [...] Read more.
This study examines the effectiveness of various deep learning algorithms in nowcasting using weather radar data from South Korea. Herein, the algorithms examined include RainNet, ConvLSTM2D U-Net, a U-Net-based recursive model, and a generative adversarial network. Moreover, this study used S-band radar data from the Ministry of Environment to assess the predictive performance of these models. Results show the efficacy of these algorithms in short-term rainfall prediction. Specifically, for a threshold of 0.1 mm/h, the recursive RainNet model achieved a critical success index (CSI) of 0.826, an F1 score of 0.781, and a mean absolute error (MAE) of 0.378. However, for a higher threshold of 5 mm/h, the model achieved an average CSI of 0.498, an F1 score of 0.577, and a MAE of 0.307. Furthermore, some models exhibited spatial smoothing issues with increasing rainfall-prediction times. The findings of this research hold promise for applications of societal importance, especially for preventing disasters due to extreme weather events. Full article
(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)
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