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Special Issue "Radar Polarimetry—Applications in Remote Sensing of the Atmosphere"

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

Deadline for manuscript submissions: 31 August 2019

Special Issue Editors

Guest Editor
Dr. Haonan Chen

NOAA Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305, USA
E-Mail
Interests: radar remote sensing; radar polarimetry; radar and satellite data fusion; precipitation microphysics; precipitation classification and quantification using multiparameter weather radar
Guest Editor
Prof. V. Chandrasekar

Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523, USA
Website | E-Mail
Interests: radar meteorology; radar system and networking; polarimetric analysis and signal processing; wave propagation and remote sensing
Guest Editor
Dr. Sanghun Lim

Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10223, Republic of Korea
E-Mail
Interests: quantitative precipitation estimation and hydrometeor classification using dual-polarization radar measurements and weather observation using automotive sensors
Guest Editor
Prof. Tomoo Ushio

Department of Aeronautics and Astronautics, Tokyo Metropolitan University, 1 Chome-1 Minamiosawa, Hachioji, Tokyo 192-0364, Japan
Website | E-Mail
Interests: radar-based remote sensing, passive and active remote sensing of atmosphere from spaceborne platforms, and atmospheric electricity

Special Issue Information

Dear Colleagues,

Radar has been widely used for remote sensing of weather, climate, hydrology, and the environment. Over the past 30 years, numerous radar techniques and algorithms have been developed for measuring, modeling, simulating and forecasting the Earth’s atmosphere state. In particular, polarization diversity has great potential to characterize precipitation microphysics and different atmospheric properties. The ground-based polarimetric radar can also be used for validation of satellite (i.e., passive or active space-borne sensors) observations and products. This Special Issue focuses on recent advances in polarimetric radar applications in geoscience and remote sensing. Contributions are welcome from all areas of active remote sensing of the atmosphere. Submissions are solicited covering, but not limited to, the following topics:

  • Concept of wave propagation and polarization
  • Radar remote sensing of environment
  • Advances in polarimetric radar hardware, signal processing, and data quality control
  • Scanning and vertically pointing cloud and precipitation radars
  • Identification of hydrometeor phase using polarimetric and Doppler spectra radar measurements
  • Remote sensing precipitation measurement, validation, and applications
  • The International Workshop on Small Weather Radars (ISWR 2018)

Dr. Haonan Chen  
Prof. V. Chandrasekar
Dr. Sanghun Lim
Prof. Tomoo Ushio
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 papers will be 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 1800 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

  • Quantitative remote sensing
  • Polarization theory/application
  • Atmospheric sensing
  • Polarimetric Radar
  • Satellite
  • Precipitation retrieval, validation and application

Published Papers (8 papers)

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Research

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Open AccessArticle
Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization
Remote Sens. 2019, 11(12), 1436; https://doi.org/10.3390/rs11121436
Received: 15 May 2019 / Revised: 12 June 2019 / Accepted: 12 June 2019 / Published: 17 June 2019
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Abstract
The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) [...] Read more.
The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm). Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Open AccessArticle
Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting
Remote Sens. 2019, 11(6), 642; https://doi.org/10.3390/rs11060642
Received: 30 January 2019 / Revised: 4 March 2019 / Accepted: 13 March 2019 / Published: 16 March 2019
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Abstract
Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term [...] Read more.
Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Open AccessArticle
Statistical Characteristics of Raindrop Size Distribution in the Monsoon Season Observed in Southern China
Remote Sens. 2019, 11(4), 432; https://doi.org/10.3390/rs11040432
Received: 3 January 2019 / Revised: 14 February 2019 / Accepted: 15 February 2019 / Published: 19 February 2019
Cited by 1 | PDF Full-text (3033 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates the statistical characteristics of raindrop size distributions (DSDs) in monsoon season with observations collected by the second-generation Particle Size and Velocity (Parsivel2) disdrometer located in Zhuhai, southern China. The characteristics are quantified based on convective and stratiform precipitation [...] Read more.
This study investigates the statistical characteristics of raindrop size distributions (DSDs) in monsoon season with observations collected by the second-generation Particle Size and Velocity (Parsivel2) disdrometer located in Zhuhai, southern China. The characteristics are quantified based on convective and stratiform precipitation classified using the rainfall intensity and total number of drops. On average of the whole dataset, the DSD characteristic in southern China consists of a higher number concentration of relatively small-sized drops when compare with eastern China and northern China, respectively. In the meanwhile, the Dm and log10Nw scatter plots prove that the convective rain in monsoon season can be identified as maritime-like cluster. The DSD is in good agreement with a three-parameter gamma distribution, especially for the medium to large raindrop size. Using filtered data observed by Parsivel2 disdrometer, a new Z–R relationship, Z = 498R1.3, is derived for convective rain in monsoon season in southern China. These results offer insights of the microphysical nature of precipitation in Zhuhai during monsoon season, and provide essential information that may be useful for precipitation retrievals based on weather radar observations. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Open AccessArticle
Phased-Array Radar System Simulator (PASIM): Development and Simulation Result Assessment
Remote Sens. 2019, 11(4), 422; https://doi.org/10.3390/rs11040422
Received: 17 January 2019 / Revised: 8 February 2019 / Accepted: 14 February 2019 / Published: 19 February 2019
Cited by 1 | PDF Full-text (6752 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete [...] Read more.
In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Open AccessArticle
Utilization of a C-band Polarimetric Radar for Severe Rainfall Event Analysis in Complex Terrain over Eastern China
Remote Sens. 2019, 11(1), 22; https://doi.org/10.3390/rs11010022
Received: 29 October 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 23 December 2018
Cited by 2 | PDF Full-text (10703 KB) | HTML Full-text | XML Full-text
Abstract
Polarimetric radar measurements and products perform as the cornerstones of modern severe weather warning and nowcast systems. Two radar quantitative precipitation estimation (QPE) frameworks, one based on a radar-gauge feedback mechanism and the other based on standard rain drop size distribution (DSD)-derived rainfall [...] Read more.
Polarimetric radar measurements and products perform as the cornerstones of modern severe weather warning and nowcast systems. Two radar quantitative precipitation estimation (QPE) frameworks, one based on a radar-gauge feedback mechanism and the other based on standard rain drop size distribution (DSD)-derived rainfall retrieval relationships, are both evaluated and investigated through an extreme severe convective rainfall event that occurred on 23 June 2015 in the mountainous region over eastern China, using the first routinely operational C-band polarimetric radar in China. Complex rainstorm characteristics, as indicated by polarimetric radar observables, are also presented to account for the severe rainfall field center located in the gap between gauge stations. Our results show that (i) the improvements of the gauge-feedback-derived radar QPE estimator can be attributed to the attenuation correction technique and dynamically adjusted Z–R relationships, but it greatly relies on the gauge measurement accuracy. (ii) A DSD-derived radar QPE estimator based on the specific differential phase (KDP) performs best among all rainfall estimators, and the interaction between the mesocyclone and the windward slope of the mountainous terrain can account for its apparent overestimation. (iii) The rainstorm is mainly dominated by small-sized and moderate-sized raindrops, with the mean volume diameter being less than 2 mm, but its KDP column (KDP > 3°·km−1) has a liquid water content that is higher than 2.4815 g·m−3, and a high raindrop concentration (Nw) with log10(Nw) exceeding 5.1 mm−1m−3. In addition, small hailstones falling and melting are also found in this event, which further aggregates Nw upon the severe rainfall center in the gap between gauge stations. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Open AccessArticle
An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables
Remote Sens. 2018, 10(8), 1179; https://doi.org/10.3390/rs10081179
Received: 7 May 2018 / Revised: 15 July 2018 / Accepted: 17 July 2018 / Published: 26 July 2018
Cited by 3 | PDF Full-text (8287 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function [...] Read more.
This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Other

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Open AccessTechnical Note
Pointing Accuracy of an Operational Polarimetric Weather Radar
Remote Sens. 2019, 11(9), 1115; https://doi.org/10.3390/rs11091115
Received: 14 March 2019 / Revised: 29 April 2019 / Accepted: 5 May 2019 / Published: 10 May 2019
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Abstract
Exact navigation of detected radar signals is crucial for usage of radar data in meteorological applications. The antenna pointing accuracy in azimuth and elevation of a polarimetric weather research radar depending on position of the sun is assessed using dedicated solar boxscans in [...] Read more.
Exact navigation of detected radar signals is crucial for usage of radar data in meteorological applications. The antenna pointing accuracy in azimuth and elevation of a polarimetric weather research radar depending on position of the sun is assessed using dedicated solar boxscans in a sequence of 10 min. The research radar of the German Meteorological Service (Deutscher Wetterdienst, DWD) is located at the meteorological observatory Hohenpeissenberg. It is identical to the 17 weather radars of the German weather radar network. A non-linear azimuthal variation of azimuthal pointing bias of up to 0.1 is found, which is significant as this is commonly viewed as the target pointing accuracy. This azimuthal variation can be attributed to the mechanical design of the drive train with the angle encoder. This includes the inherent backlash of the gear-drive assembly. The pointing bias estimates based on over 1000 boxscans from 26 days show a small case by case variability, which indicates that dedicated solar boxscans from one day are sufficient to characterize the pointing performance of a particular system. An azimuth and elevation range that is covered with this approach is limited and dependent on the time of the year. At Hohenpeißenberg, an azimuth range up to 50–300 was covered around summer solstice and about 90 boxscans were acquired. It is shown that the pointing bias based on solar boxscan data are consistent with results from the operational assessment of pointing bias using solar hits from operational scanning if we take into account the fact that the DWD operational scan definition has only a maximum elevation of 25 . The analysis of a full diurnal cycle of boxscans from four operational radar system shows that the azimuthal dependence of azimuth bias needs to be evaluated individually for each system. For one of the systems, the azimuthal variation of the pointing bias of about 0.2 seems related to the bull gear. A difference of the pointing bias for the horizontal and vertical polarization is an indication of beam squint and, eventually, that of a feed misalignment. Beam squint and, as such, the quality of the antenna assembly can easily be monitored with this method during the life-time of a weather radar. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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Open AccessTechnical Note
On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
Remote Sens. 2018, 10(7), 1007; https://doi.org/10.3390/rs10071007
Received: 1 May 2018 / Revised: 14 June 2018 / Accepted: 21 June 2018 / Published: 25 June 2018
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Abstract
“Bright” scatterers are useful for monitoring modern weather radars. In order to be “bright”, a point target with deterministic backscattering properties should be present at a near range and be hit by the antenna beam axis. In this note, a statistical characterization of [...] Read more.
“Bright” scatterers are useful for monitoring modern weather radars. In order to be “bright”, a point target with deterministic backscattering properties should be present at a near range and be hit by the antenna beam axis. In this note, a statistical characterization of the echoes from a metallic tower located on Cimetta, at an 18 km range and at the same altitude as the Monte Lema radar, is presented. The analysis is based on five clear sky days (1440 samples with a spatial resolution of 1° × 1° × 83.33 m). The spectral and polarimetric signatures are striking: The spectrum width is perfectly stable; the mean radial velocity is very stable; the radar reflectivity is also quite stable, with the vertical (V) polarization being more variable than the horizontal (H) one. As far as the polarimetric information is concerned, the daily average of the differential reflectivity is approximately 1 ± 0.9 dB. The copolar correlation coefficient between H and V is remarkably large (0.9962, on average) and stable. It is believed that these unique and stable ground clutter signals could be used to monitor operational dual-polarization weather radars. Full article
(This article belongs to the Special Issue Radar Polarimetry—Applications in Remote Sensing of the Atmosphere)
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