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Special Issue "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: 25 March 2024 | Viewed by 875

Special Issue Editors

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
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 (1 paper)

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17 pages, 70334 KiB  
Technical Note
Assessment of Deep Learning-Based Nowcasting Using Weather Radar in South Korea
Remote Sens. 2023, 15(21), 5197; https://doi.org/10.3390/rs15215197 - 31 Oct 2023
Viewed by 609
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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