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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: 30 September 2024 | Viewed by 2851

Special Issue Editor

Prof. Dr. Dong-In Lee

E-Mail Website
Guest Editor
Department of Environmental Atmospheric Sciences, Pukyong National University, Busan 48513, Republic of Korea
Interests: radar meteorology; cloud and precipitation; high-impact weather; radar nowcasting; radar wind field retrieval and analyses; field observation
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 (2 papers)

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Research

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 1 | Viewed by 1247
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Structure diagram of ST-LSTM.</p> Full article ">Figure 2
<p>The overall architecture of the METEO-DLNet.</p> Full article ">Figure 3
<p>Structure diagram of encode-decode architecture.</p> Full article ">Figure 4
<p>Structure diagram of Meteo-LSTM.</p> Full article ">Figure 5
<p>Structure of the Spatial Attention Module (SA).</p> Full article ">Figure 6
<p>Structure of the Differential Attention Module (DA).</p> Full article ">Figure 7
<p>Structure diagram of the fusion module based on self-attention and gating mechanism.</p> Full article ">Figure 8
<p>(<b>a</b>) Radar reflectivity example chart for the northwestern region of France; (<b>b</b>) Precipitation example chart for the northwestern region of France.</p> Full article ">Figure 9
<p>(<b>a</b>) Mean value of SSIM over time on the test set; (<b>b</b>) Mean value of MSE over time on the test set.</p> Full article ">Figure 10
<p>Prediction example of radar echo test set in the northwest region of France.</p> Full article ">
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 921
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The topography of Taiwan and the surrounding regions (m, color) and the model domains used for the three events (A, B, and C) in this study (see text for details). Dashed and dotted areas depict 2.5 and 1 km domains for events A and B (red; R2.5AB and R1.0A/R1.0B) and event C (blue; R2.5C and R1.0C), respectively.</p> Full article ">Figure 2
<p>The CWB surface weather maps at 0000 UTC of (<b>a</b>) 13, (<b>b</b>) 14, (<b>c</b>) 15, and (<b>d</b>) 16 June 2008, respectively. Parameters shown include mean sea-level pressure (hPa, isobars every 4 hPa), surface front, and high-/low-pressure systems with station plots. (source: CWB.).</p> Full article ">Figure 3
<p>NCEP (1° × 1°) 850 hPa final analyses of geopotential height (gpm, black contours every 10 gpm) and horizontal wind (kt, barbs, one full/half barb = 10/5 kt with isotachs (m s<sup>−1</sup>, color, scale at bottom) at 0000 UTC on (<b>a</b>) 14 and (<b>b</b>) 16 Jun 2008, respectively. An additional isotach at 12 m s<sup>−1</sup> is also plotted in red.</p> Full article ">Figure 4
<p>As in <a href="#remotesensing-15-04651-f003" class="html-fig">Figure 3</a>, except for geopotential height (gpm) and horizontal winds (kt, barbs, one full/half barb = 10/5 kt) without isotachs, plus relative vorticity (10<sup>−4</sup> s<sup>−1</sup>, color, scale at bottom) at 0000 UTC on (<b>a</b>) 14 and (<b>b</b>) 16 Jun 2008, respectively. Troughs are depicted by thick dashed lines and the 5880 gpm isopleths are plotted in green.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>f</b>) The CWB radar reflectivity mosaic (dBZ, color, scale at bottom) and NCEP 800 hPa wind analysis (m s<sup>−1</sup>, vectors) at (<b>a</b>) 2000 UTC 13 June, and (<b>b</b>) 0100, (<b>c</b>) 0900, (<b>d</b>) 1100, (<b>e</b>) 1300, and (<b>f</b>) 1600 UTC 14 Jun 2008, respectively. (<b>g</b>–<b>i</b>) As in (<b>a</b>–<b>f</b>) except from model-simulated reflectivity (scale on the right) and wind vectors at 2 km height at (<b>g</b>) 0930, (<b>h</b>) 1030, and (<b>i</b>) 1130 UTC 14 Jun in R1.0AB. Reference vectors are shown at the bottom.</p> Full article ">Figure 6
<p>Daily accumulated rainfall observation (mm, color) in Taiwan (in LST) on (<b>a</b>) 14 and (<b>b</b>) 16 Jun during IOP-8, and on (<b>c</b>) 30 and (<b>d</b>) 31 May during IOP-3 in 2008, respectively. In Taiwan, 0000 LST corresponds to 1600 UTC.</p> Full article ">Figure 7
<p>(<b>a</b>) The reflectivity mosaic (dBZ) from S-Pol and winds at 2.5 km from radar syntheses (m s<sup>−1</sup>, blue vectors), and interpolated reflectivity (dBZ) and dual-Doppler-analyzed system-relative winds (m s<sup>−1</sup>, vectors) at (<b>b</b>) 2 and (<b>c</b>) 5 km at 1000 UTC 14 Jun 2008, respectively. In (<b>b</b>,<b>c</b>), red (blue) contours depict upward (downward) motion every 2 m s<sup>−1</sup>. Adapted from [<a href="#B46-remotesensing-15-04651" class="html-bibr">46</a>] and used with permission. In (<b>a</b>), the topography of 0.5 km is also plotted and the box depicts the regions shown in (<b>b</b>,<b>c</b>), where the dashed line AB shows the cross-section used in <a href="#remotesensing-15-04651-f008" class="html-fig">Figure 8</a>a,b. (<b>d</b>–<b>f</b>) As in (<b>a</b>–<b>c</b>), except from model simulation and retrieved reflectivity in R1.0A at 1100 UTC 14 Jun 2008 at the same scale. In (<b>d</b>), the topography of 2.5 km is plotted (blue contour) instead. In (<b>e</b>,<b>f</b>), only upward motion is plotted (also every 2 m s<sup>−1</sup>) and the dashed line A′B′ shows the cross-section used in <a href="#remotesensing-15-04651-f008" class="html-fig">Figure 8</a>c,d.</p> Full article ">Figure 8
<p>East–west vertical cross-section of system-relative wind vectors and upward motion (contours, both in m s<sup>−1</sup>), plus (<b>a</b>) reflectivity (dBZ) and (<b>b</b>) PID results from S-Pol along the line AB (<a href="#remotesensing-15-04651-f007" class="html-fig">Figure 7</a>b) at 1000 UTC 14 Jun 2008. Adapted from [<a href="#B46-remotesensing-15-04651" class="html-bibr">46</a>] and used with permission. The abbreviations for particle types are CL (cloud), DZ (drizzle), LR (light rain), MR (moderate rain), HR (heavy rain), HA (hail), RH (rain–hail mixture), GSH (graupel–small hail mixture), GR (graupel–rain mixture), DS (dry snow), WS (wet snow), IC (ice crystals), IIC (irregular ice crystals), SLD (supercooled liquid droplets), BGS (biological signals), ST (second trip), and GC (ground clutter), respectively. (<b>c</b>) As in (<b>a</b>), except from model simulation in R1.0A along the line A′B′ (<a href="#remotesensing-15-04651-f007" class="html-fig">Figure 7</a>e) at 1100 UTC 14 Jun 2008. (<b>d</b>) As in (<b>c</b>), but showing the mixing ratios (g kg<sup>−1</sup>) of hydrometeors (color for rain, and green, blue, and pink contours for graupel, snow, and ice, respectively). All panels have the same scale.</p> Full article ">Figure 9
<p>As in <a href="#remotesensing-15-04651-f005" class="html-fig">Figure 5</a>, except for (<b>a</b>–<b>f</b>) radar reflectivity mosaic (dBZ) and NCEP 800 hPa winds (m s<sup>−1</sup>) every 4 h from (<b>a</b>) 2000 UTC 15 June to (<b>f</b>) 1600 UTC 16 Jun 2008, respectively, and model-simulated reflectivity and winds at 2 km height at (<b>g</b>) 0400, (<b>h</b>) 0800, and (<b>i</b>) 1200 UTC 16 Jun in R1.0B. The areas of interest are circled.</p> Full article ">Figure 10
<p>(<b>a</b>–<b>c</b>) As in <a href="#remotesensing-15-04651-f007" class="html-fig">Figure 7</a>a–c, except for reflectivity from S-Pol, radar-synthesized winds, and dual-Doppler-analyzed system-relative winds at 0507 UTC 16 Jun 2008. Adapted from [<a href="#B46-remotesensing-15-04651" class="html-bibr">46</a>] and used with permission. In (<b>a</b>), the box depicts the regions shown in (<b>b</b>,<b>c</b>), where the dashed line CD shows the cross-section used in <a href="#remotesensing-15-04651-f011" class="html-fig">Figure 11</a>a,b. (<b>d</b>–<b>f</b>) As in <a href="#remotesensing-15-04651-f007" class="html-fig">Figure 7</a>d–f, except from model results in R1.0B at 0930 UTC 16 Jun 2008. The dashed line C′D′ in (<b>e</b>,<b>f</b>) shows the cross-section used in <a href="#remotesensing-15-04651-f011" class="html-fig">Figure 11</a>c,d.</p> Full article ">Figure 11
<p>As in <a href="#remotesensing-15-04651-f008" class="html-fig">Figure 8</a>, except for east–west vertical cross-sections (<b>a</b>,<b>b</b>) from S-Pol along the line CD (<a href="#remotesensing-15-04651-f010" class="html-fig">Figure 10</a>b) at 0507 UTC 16 Jun, and (<b>c</b>,<b>d</b>) from the model simulation in R1.0B along the line C′D′ (<a href="#remotesensing-15-04651-f010" class="html-fig">Figure 10</a>e) at 0930 UTC 16 Jun 2008, respectively. (<b>a</b>,<b>b</b>) Adapted from [<a href="#B46-remotesensing-15-04651" class="html-bibr">46</a>] and used with permission.</p> Full article ">Figure 12
<p>(<b>a</b>) The CWB surface weather map (isobars every 4 hPa, surface front and high/low systems with station plots) and (<b>b</b>–<b>d</b>) NCEP (1° × 1°) final analyses of geopotential height (gpm, black contours every 10 gpm), horizontal winds (kt, barbs, one full/half barb = 10/5 kt), plus (<b>b</b>) temperature (°C, color) at 850 hPa, (<b>c</b>) relative humidity (%, color) at 700 hPa, and (<b>d</b>) relative vorticity (10<sup>−4</sup> s<sup>−1</sup>, color) at 500 hPa, respectively, all at 0000 UTC 31 May 2008.</p> Full article ">Figure 13
<p>Enhanced infrared cloud images (brightness temperature, °C, scale at bottom) surrounding Taiwan by the geostationary Multifunctional Transport Satellite (MTSAT) every 2 h from 2200 UTC 30 to 1200 UTC 31 May 2008 (<b>a</b>–<b>h</b>).</p> Full article ">Figure 14
<p>(<b>a</b>–<b>e</b>) As in <a href="#remotesensing-15-04651-f007" class="html-fig">Figure 7</a>a, except for reflectivity (dBZ) from S-Pol at the height of 3 km at (<b>a</b>) 0752, (<b>b</b>) 0800, (<b>c</b>) 0807, (<b>d</b>) 0815, and (<b>e</b>) 0845 UTC 31 May 2008, respectively. Adapted from [<a href="#B47-remotesensing-15-04651" class="html-bibr">47</a>] and used with permission. The location of S-Pol (red triangle) and convection of interest (circles) are marked. In (<b>e</b>), the dashed line EF shows the cross-section used in <a href="#remotesensing-15-04651-f015" class="html-fig">Figure 15</a>a,b. (<b>f</b>–<b>j</b>) As in <a href="#remotesensing-15-04651-f007" class="html-fig">Figure 7</a>d, except for reflectivity and wind vectors (m s<sup>−1</sup>) at a height of 3 km from model results in R1.0C every 15 min from (<b>f</b>) 0430 to (<b>j</b>) 0530 UTC 31 May 2008, respectively, at the same scale. The dashed line E′F′ in (<b>j</b>) shows the cross-section used in <a href="#remotesensing-15-04651-f015" class="html-fig">Figure 15</a>c,d.</p> Full article ">Figure 15
<p>Similar to <a href="#remotesensing-15-04651-f008" class="html-fig">Figure 8</a>, except for the vertical cross-sections (<b>a</b>,<b>b</b>) from S-Pol along the line EF (<a href="#remotesensing-15-04651-f014" class="html-fig">Figure 14</a>e) at 0844 UTC 31 May (without kinematic field), and (<b>c</b>,<b>d</b>) from the model simulation in R1.0C along the line E′F′ (<a href="#remotesensing-15-04651-f014" class="html-fig">Figure 14</a>j) at 0530 UTC 31 May 2008, respectively. The dashed line depicts the 0 °C isotherm. (<b>a</b>,<b>b</b>) Adapted from [<a href="#B47-remotesensing-15-04651" class="html-bibr">47</a>] and used with permission. All panels have the same scale.</p> Full article ">
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