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Remote Sensing
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Recent Advances in Precipitation Radar
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Recent Advances in Precipitation Radar

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

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 6372

Special Issue Editor

Prof. Dr. Dong-In Lee

E-Mail Website
Guest Editor
Atmospheric Environmental Research Institute, Pukyong National University, Busan 48513, Republic of Korea
Interests: radar meteorology; dual-polarization radar and wind profiler observations; typhoon structure analysis, precipitation physics and dynamics; orographic rainfall process; artificial intelligence for radar data interpretation; machine learning and deep learning for quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF), radar nowcasting and hydrological prediction; hydrometeor classification using radar and AI technique, disdrometer and ground validation studies, global and regional hydrology of remote sensing-based precipitation estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There have been many natural disasters caused by high-impact weather such as torrential rainfall, hail, flood, tornado, snow storm and tropical cyclone as climate change progresses around the world. Weather radars (Doppler, polarimetric, phased array etc.) have been crucial instruments to monitor chaff diffusion, precipitation and winds, and to forecast high-impact weather systems with higher spatial and temporal resolution than other remote sensing equipment. Polarimetric capabilities help to understand the microphysical characteristics of precipitation systems and improve radar quantitative precipitation estimation/forecasting. The newly developed and advanced analyses of radar precipitation included with hydrometeo classification and wind field retrieval for QPE/QPF are of special interest for this Special Issue.

The goal of this Special Issue is share the recent achievements in various applications using operational or research radar data (e.g., field observation campaign, rainfall estimation, chaff diffusion in clear sky, nowcasting of precipitation, microphysical features of precipitation systems, hydrological modeling and forecasting in severe weather using Doppler radar and polarimetric radar.  We encourage contributions on the current state-of-the-art in the field, including challenges and discussions toward the better utilization of radar data.

We invite manuscripts on the following topics:

  • Field observation campaign;
  • Radar data quality control;
  • Quantitative precipitation estimation;
  • Wind field retrieval and analyses;
  • Short-term forecast of precipitation;
  • Assimilation of radar data into NWP;
  • Orographic/topographic precipitation;
  • Hydrological applications using weather radar;
  • High-impact weather such as hail, tornado, typhoon and lightning;
  • Atmospheric diffusion by chaff experiments;
  • Torrential rainfall and nowcasting;
  • Hydrometeo classification in clouds and precipitation.

Prof. Dr. Dong-In Lee
Guest Editor

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

  • weather radar quality control algorithms
  • quantitative precipitation estimation and forecasting
  • microphysical characteristics of precipitation
  • polarimetric and phased array radar applications
  • field observational campaign of high-impact weather
  • development mechanism of frontal systems and tropical cyclones
  • radar wind field retrieval and analyses
  • chaff diffusion analyses by radars
  • radar nowcasting

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Published Papers (3 papers)

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Research

21 pages, 6600 KiB  
Article
Strategic Deployment of a Single Mobile Weather Radar for the Enhancement of Meteorological Observation: A Coverage-Based Location Problem
by Bikram Parajuli and Xin Feng
Remote Sens. 2025, 17(5), 870; https://doi.org/10.3390/rs17050870 - 28 Feb 2025
Viewed by 1488
Abstract
Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration [...] Read more.
Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration of operational safety, data quality, and environmental constraints. In this study, we introduce a framework using a coverage-based location problem to solve the strategic deployment of a single mobile weather radar. This approach aims to enhance weather observation while accounting for the deployment space’s safety constraints and geospatial characteristics. The proposed location problem is solved optimally using the geometric branch-and-bound algorithm and heuristically using swarm-based optimization algorithms. The implementation relies entirely on open-source Python packages, allowing the work to be verified, replicated, and expanded upon by the broader scientific community. Results demonstrate that exact solution methods are ideal when ample time is available for decision-making and optimal deployment locations are desired. In contrast, heuristic algorithms can efficiently identify multiple near-optimal deployment locations, making them highly suitable for rapid decision-making and evaluating alternative deployment options. Moreover, the findings highlight the potential of quantitative decision-making techniques in improving the effectiveness of mobile radar positioning, thereby contributing to efficient weather observation, forecasting, and better-informed emergency response strategies. Full article
(This article belongs to the Special Issue Recent Advances in Precipitation Radar)
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20 pages, 5369 KiB  
Article
METEO-DLNet: Quantitative Precipitation Nowcasting Net Based on Meteorological Features and Deep Learning
by Jianping Hu, Bo Yin and Chaoqun Guo
Remote Sens. 2024, 16(6), 1063; https://doi.org/10.3390/rs16061063 - 17 Mar 2024
Cited by 3 | Viewed by 2454
Abstract
Precipitation prediction plays a crucial role in people’s daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms [...] Read more.
Precipitation prediction plays a crucial role in people’s daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms based on radar echo extrapolation for short-term precipitation forecasting. However, there are inadequately addressed issues with radar echo extrapolation methods based on deep learning, particularly when considering the inherent meteorological characteristics of precipitation on spatial and temporal scales. Additionally, traditional forecasting methods face challenges in handling local images that deviate from the overall trend. To address these problems, we propose the METEO-DLNet short-term precipitation prediction network based on meteorological features and deep learning. Experimental results demonstrate that the Meteo-LSTM of METEO-DLNet, utilizing spatial attention and differential attention, adequately learns the influence of meteorological features on spatial and temporal scales. The fusion mechanism, combining self-attention and gating mechanisms, resolves the divergence between local images and the overall trend. Quantitative and qualitative experiments show that METEO-DLNet outperforms current mainstream deep learning precipitation prediction models in natural spatiotemporal sequence problems. Full article
(This article belongs to the Special Issue Recent Advances in Precipitation Radar)
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25 pages, 21681 KiB  
Article
An Evaluation of Simulated Cloud Microphysical Characteristics of Three Mei-Yu Rainfall Systems in Taiwan by a Cloud-Resolving Model Using Dual-Polarimetric Radar Observations
by Chung-Chieh Wang, Yu-Han Chen, Yu-Yao Lan and Wei-Yu Chang
Remote Sens. 2023, 15(19), 4651; https://doi.org/10.3390/rs15194651 - 22 Sep 2023
Viewed by 1363
Abstract
This study selected three heavy-rainfall events of different types in Taiwan’s Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model’s capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a [...] Read more.
This study selected three heavy-rainfall events of different types in Taiwan’s Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model’s capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a mesoscale convective system (MCS) embedded in southwesterly flow, and a local convection near the front in southern Taiwan during the South-West Monsoon Experiment (SoWMEX) in 2008, chosen mainly because of the availability of the S-band polarimetric (S-Pol) radar observations, and especially the particle identification results. The simulations using the Cloud-Resolving Storm Simulator (CReSS) could reproduce all three corresponding rainfall systems at roughly the correct time and location, including their kinematic structures such as system-relative flows with minor differences, although the cells appeared to be coarser and wider than the S-Pol observations. The double-moment cold-rain microphysics scheme of the model could also capture the general distributions of hydrometeors, such as heavy rainfall below the updraft core with lighter rainfall farther away below the melting level, and graupel and mixed-phase particles in the upper part of the updraft with snow and ice crystals in stratiform areas between updrafts above the melting level. Near the melting level, the coexistence of rain and snow corresponds to wet snow in the observations. Differences in cloud characteristics in the events are also reflected in the model results to some extent. Overall, the model’s performance in the simulation of hydrometeors exhibits good agreement with the observation and appears reasonable. Full article
(This article belongs to the Special Issue Recent Advances in Precipitation Radar)
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