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Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation
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Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 37944

Special Issue Editors

Dr. Yingzhao Ma

E-Mail Website
Guest Editor
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80521, USA
Interests: extreme precipitation; radar hydrometeorology; remote sensing of precipitation
Special Issues, Collections and Topics in MDPI journals
Dr. Robert Cifelli

E-Mail Website
Guest Editor
NOAA Physical Sciences Laboratory, 325 Broadway, Boulder, CO 80305, USA
Interests: radar meteorology; radar hydrology; precipitation dynamics and processes; hydrometeorology modeling and applications
Dr. Seppo Pulkkinen

E-Mail Website
Guest Editor
Finnish Meteorological Institute, PL 503, FIN-00101 Helsinki, Finland
Interests: radar-based quantitative precipitation estimation and nowcasting; statistical modeling of radar and rain gauge data; data mining; image processing; computer vision; optimization; numerical algorithms
Prof. Dr. V. Chandrasekar

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523, USA
Interests: radar meteorology; radar system and networking; polarimetric analysis and signal processing; wave propagation and remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar remote sensing deals with the full vector nature of electromagnetic waves scattered from targets of interest, from which it is possible to perform hydrometeor identification, rainfall microphysical retrievals, and quantitative precipitation estimation and nowcasting in the remote sensing of atmosphere. The vector nature comes from the fact that multiple polarization or frequencies are used in the observation process. In the 1990s, the implementation of polarimetric measurement options for operational WSR-88D radars resulted in a widespread application of polarimetric techniques by a broad segment of the meteorological community. Since the 1990s, many polarimetric radars have been deployed not just at S-band frequency used by the US weather service but also at higher frequencies including C, X, Ku, Ka, and W. Similarly, multiple frequency radars and vertically pointing radars have also been deployed. This rich base of information also attracts advanced processing techniques, in particular, the use of machine learning or Bayesian retrievals.

This Special Issue focuses on recent advances in radar remote sensing of precipitation, including rain microphysics, hydrometeor classification, quantitative precipitation estimation, and nowcasting. Papers focused on the radar remote sensing of non-precipitation phenomena, such as clouds, winds, and lightning, are also welcome.

Dr. Yingzhao Ma
Dr. Robert Cifelli
Dr. Seppo Pulkkinen
Prof. V. Chandrasekar
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.

Published Papers (18 papers)

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Research

18 pages, 20784 KiB  
Article
Prediction of Radar Echo Space-Time Sequence Based on Improving TrajGRU Deep-Learning Model
by Qiangyu Zeng, Haoran Li, Tao Zhang, Jianxin He, Fugui Zhang, Hao Wang, Zhipeng Qing, Qiu Yu and Bangyue Shen
Remote Sens. 2022, 14(19), 5042; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14195042 - 09 Oct 2022
Cited by 5 | Viewed by 2338
Abstract
Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear [...] Read more.
Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear motion of small- to medium-sized convective systems in radar echoes. To solve this, we propose a deep-learning model combining CNN and RNN. The model T-UNet proposed in this paper uses an efficient convolutional neural network of UNet architecture with a residual network, where the encoder and decoder networks are connected by nested dense skip paths, while a TrajGRU recurrent neural network is added at each layer, to achieve the perceptual capability of time series. The quantitative statistical evaluation showed that the use of T-UNet could improve the nowcasting skill (CSI score, HSS score) by a maximum of 10.57% and 7.80% over a 60 min prediction cycle. Further evaluation showed that T-UNet also improved the prediction accuracy and prediction performance in the strong echo region. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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23 pages, 6053 KiB  
Article
Multiple Characteristics of Precipitation Inferred from Wind Profiler Radar Doppler Spectra
by Albert Garcia-Benadi, Joan Bech, Mireia Udina, Bernard Campistron and Alexandre Paci
Remote Sens. 2022, 14(19), 5023; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14195023 - 09 Oct 2022
Cited by 3 | Viewed by 1528
Abstract
A methodology to process radar wind profiler Doppler spectra is presented and implemented for an UHF Degreane PCL1300 system. First, double peak signal detection is conducted at each height level and, then, vertical continuity checks for each radar beam ensure physically consistent measurements. [...] Read more.
A methodology to process radar wind profiler Doppler spectra is presented and implemented for an UHF Degreane PCL1300 system. First, double peak signal detection is conducted at each height level and, then, vertical continuity checks for each radar beam ensure physically consistent measurements. Second, horizontal and vertical wind, kinetic energy flux components, Doppler moments, and different precipitation-related variables are computed. The latter include a new precipitation type estimate, which considers rain, snow, and mixed types, and, finally, specific variables for liquid precipitation, including drop size distribution parameters, liquid water content and rainfall rate. The methodology is illustrated with a 48 h precipitation event, recorded during the Cerdanya-2017 field campaign, carried out in the Eastern Pyrenees. Verification is performed with a previously existing process for wind profiler data regarding wind components, plus precipitation estimates derived from Micro Rain Radar and disdrometer observations. The results indicated that the new methodology produced comparable estimates of wind components to the previous methodology (Bias < 0.1 m/s, RMSE ≈ 1.1 m/s), and was skilled in determining precipitation type when comparing the lowest estimate of disdrometer data for snow and rain, but did not correctly identify mixed precipitation cases. The proposed methodology, called UBWPP, is available at the GitHub repository. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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12 pages, 2831 KiB  
Communication
Calibration of D3R Weather Radar Using UAV-Hosted Target
by Shashank S. Joshil and Chandra V. Chandrasekar
Remote Sens. 2022, 14(15), 3534; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153534 - 23 Jul 2022
Cited by 3 | Viewed by 1736
Abstract
The accuracy of weather radar-measured products, such as reflectivity, plays a crucial factor in obtaining good quality, derived remote sensing products, such as hydrometeor classification. A slight mismatch of a few decibels in reflectivity may cause the hydrometeor classification to deviate from the [...] Read more.
The accuracy of weather radar-measured products, such as reflectivity, plays a crucial factor in obtaining good quality, derived remote sensing products, such as hydrometeor classification. A slight mismatch of a few decibels in reflectivity may cause the hydrometeor classification to deviate from the actual truth. In order to obtain accurate remote-sensing measurements, calibration of weather radars should be carried out at regular intervals of time. The dual-frequency, dual-polarization, Doppler radar (D3R) is a well-established tool for measuring light rain and snow. There are various methods which are used for the calibration of weather radars, such as suspending a metallic sphere from a weather balloon or using corner reflectors on top of a tower or structure. In this work, we have shown the potential of using a UAV to suspend a calibration sphere, which is then used for calibrating the D3R radar. In this work, the advantages along with the practical aspects to be considered for calibration using UAV are discussed in detail. From the calibration results, it was observed that an offset of 2.2 dB was present in the Ku H-Pol reflectivity, Ku V-Pol was well calibrated and offsets within 2 dB were observed in the Ka H-Pol and V-Pol reflectivities. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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22 pages, 8649 KiB  
Article
Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events
by Yang Zhang, Liping Liu, Hao Wen, Benchao Yu, Huiying Wang and Yong Zhang
Remote Sens. 2022, 14(13), 3154; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133154 - 30 Jun 2022
Cited by 1 | Viewed by 1561
Abstract
For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used [...] Read more.
For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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15 pages, 4570 KiB  
Article
Dual-Frequency Radar Retrievals of Snowfall Using Random Forest
by Tiantian Yu, V. Chandrasekar, Hui Xiao, Ling Yang, Li Luo and Xiang Li
Remote Sens. 2022, 14(11), 2685; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112685 - 03 Jun 2022
Cited by 1 | Viewed by 1526
Abstract
The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex [...] Read more.
The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex terrain. The NASA Dual-frequency Dual-polarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer observations were used to develop retrievals. The conventional look-up table method (LUT) and random forest method (RF) were applied to the disdrometer data to develop retrievals for the volume-weighted mean diameter (Dm), the shape factor (mu), the normalized intercept parameter (Nw), the ice water content (IWC), and the snowfall rate (S). Evaluations were performed between the D3R radar and disdrometer observations using these two methods. The mean errors of the retrievals based on the RF method were small compared with those of the LUT method. The results indicate that the RF method is a promising way of retrieving microphysical parameters, because this method does not require any assumptions about the PSD of snowfall. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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22 pages, 5359 KiB  
Article
Analysis of Two Convective Storms Using Polarimetric X-Band Radar and Satellite Data
by Gabriela Bobotová, Zbyněk Sokol, Jana Popová, Ondřej Fišer and Petr Zacharov
Remote Sens. 2022, 14(10), 2294; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102294 - 10 May 2022
Cited by 3 | Viewed by 1785
Abstract
We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was [...] Read more.
We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was based mainly on Doppler polarimetric radar data measured in vertical cross-sections (RHI-Range-Height Indicator). Radar data was also used for classifying hydrometeors by a newly developed XCLASS (X-band radar CLASSification) algorithm. We also used rapid scan data measured by the geostationary satellite Meteosat Second Generation to validate radar measurements at the upper parts of storms. Although an attenuation correction was applied to the reflectivity and differential reflectivity measurements, the attenuation typical of X-band radars was noticeable. It was mainly manifested in the differential reflectivity, co-polar correlation coefficient and specific differential phase. Nevertheless, radar measurements can be used to analyze the internal cloud structure of severe convective storms. The XCLASS classification was developed by major innovation of a previously published algorithm. The XCLASS algorithm identifies seven types of hydrometeors: light rain, rain, wet snow, dry snow, ice, graupel, and hail. It uses measured horizontal and vertical radar reflectivity, specific differential phase, co-polar correlation coefficient, and temperature, and applies fuzzy logic to determine the type of hydrometeor. The new algorithm practically eliminates unrealistic results around and below the melting layer provided by the original algorithm. It identifies wet snow in more cases, and areas with individual hydrometeors have more realistic shapes compared to the original algorithm. The XCLASS algorithm shows reasonable results for the classification of hydrometeors and can be used to study the structure of convective storms. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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17 pages, 13159 KiB  
Article
Consistency of Vertical Reflectivity Profiles and Echo-Top Heights between Spaceborne Radars Onboard TRMM and GPM
by Lei Ji, Weixin Xu, Haonan Chen and Nana Liu
Remote Sens. 2022, 14(9), 1987; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091987 - 21 Apr 2022
Cited by 1 | Viewed by 1764
Abstract
Globally consistent long-term radar measurements are imperative for understanding the global climatology and potential trends of convection. This study investigates the consistency of vertical profiles of reflectivity (VPR) and 20-dBZ echo-top height (Topht20) between the two precipitation radars onboard the Tropical Rainfall Measuring [...] Read more.
Globally consistent long-term radar measurements are imperative for understanding the global climatology and potential trends of convection. This study investigates the consistency of vertical profiles of reflectivity (VPR) and 20-dBZ echo-top height (Topht20) between the two precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. Results show that VPR coincidently observed by the TRMM’s and GPM’s Ku-band radar agree well for both convective and stratiform precipitation, although certain discrepancies exist in the VPR of weak convection. Topht20s of the TRMM and GPM are consistent either for coincident events, or latitudinal mean during the 7-month common period, all with biases within the radar range resolution (0.1–0.2 km). The largest difference in the Topht20 between the TRMM’s and GPM’s Ku-band radar occurs in shallow precipitation. Possible reasons for this discrepancy are discussed, including sidelobe clutter, beam-mismatch, non-uniform beam filling, and insufficient sampling. Finally, a 23-year (1998–2020) climatology of Topht20 has been constructed from the two spaceborne radars, and the global mean Topht20 time series shows no significant trend in convective depth during the last two decades. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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16 pages, 6058 KiB  
Article
Analyses of DSD Vertical Evolution and Rain Variation Mechanism in Stratiform Cloud Cases Using Micro Rain Radar
by Ningkun Ma, Yichen Chen, Zuo Jia, Liping Liu, Xincheng Ma and Yu Huang
Remote Sens. 2022, 14(7), 1655; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071655 - 30 Mar 2022
Cited by 1 | Viewed by 1567
Abstract
(1) Raindrop size distribution (DSD) is a vital microphysical characteristic of clouds and precipitations. The vertical evolution of DSD also provides a reference for the microphysical mechanisms and dynamic processes involved in clouds and precipitations. (2) Here we analyzed the characteristics and vertical [...] Read more.
(1) Raindrop size distribution (DSD) is a vital microphysical characteristic of clouds and precipitations. The vertical evolution of DSD also provides a reference for the microphysical mechanisms and dynamic processes involved in clouds and precipitations. (2) Here we analyzed the characteristics and vertical evolution of DSDs, which were obtained from Micro Rain Radar (MRR) data of two typical stratiform rain cases. (3) First, we compared MRR-observed reflectivity (Z) and DSD at 400 m with data from a distrometer on the ground. This ensured the reliability of the MRR data of the two cases. Then it was found that the DSD was wider just below the 0 °C level than at lower levels. The larger DSDs width formed a bulge shape in the vertical direction, and large particles in the ‘bulge’ then constantly collided as they were falling down. The DSD was broadened and the echo of the warm layer was strengthened. We referred to this as the bulge phenomenon (BP), which appeared occasionally, and broader DSD propagated from high to low intermittently during the stratiform rain. Next, by combining the detailed cloud structures detected by cloud radar with BP, we found that a BP was always accompanied by higher developing cloud tops, stronger Z and larger falling velocity. It was inferred that ice particles formed near cloud top intermittently and fell through the underlying cloud, causing the gustiness and instability of particle aggregation, which was reflected by the BP below the 0 °C layer. BP triggered quick collision and falling down along the warm layer, enhancing the Z and falling velocity transiently. Thus, BP was considered as one of the mechanisms of rain variation in stratocumulus and stratiform rain in North China. Finally, we defined the cycle time of a BP (BPT), which was composed of broadening stage (BS) and stable stage (SS). We found that changes of DSD parameters for both MRR and distrometer responded to each BP occurring, showing the same intermittency. From each BP occurring time to the corresponding BS ending time, Dm basically grew from small to large. After this, Dm decreased immediately or maintained for a while and then decreased. Nw had the opposite trend to Dm. Also, it was found that larger R accelerated the fluency of BP occurring (BPT). Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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24 pages, 54810 KiB  
Article
An Inverse Method for Drop Size Distribution Retrieval from Polarimetric Radar at Attenuating Frequency
by Matias Alcoba, Hervé Andrieu and Marielle Gosset
Remote Sens. 2022, 14(5), 1116; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051116 - 24 Feb 2022
Cited by 1 | Viewed by 1502
Abstract
A method that formulates the retrieval of drop size distribution (DSD) parameters from polarimetric radar variables at attenuating frequency as the solution of an inverse problem is presented. The DSD in each radar bin is represented by a normalized Gamma distribution defined by [...] Read more.
A method that formulates the retrieval of drop size distribution (DSD) parameters from polarimetric radar variables at attenuating frequency as the solution of an inverse problem is presented. The DSD in each radar bin is represented by a normalized Gamma distribution defined by three parameters (Dm,N0*,μ). The direct problem that describes polarimetric radar observables—scattering and propagation terms—and their dependency on DSD parameters is analyzed based on T-matrix scattering simulations. The inverse algorithm and its application to the DSD retrieval are then presented. The inverse method is applied to an African Monsoon Multidisciplinary Analysis (AMMA) field campaign that deployed an X-band dual-polarization Doppler radar and optical disdrometers in Benin, West Africa, in 2006 and 2007. The dataset is composed of X-band polarimetric radar PPIs and disdrometer data for 15 organized convective systems observed in 2006. A priori information on DSD parameters (benchmark method) is derived from the polarimetric radar observables by applying power law relationships. The proposed retrieval method of DSD parameters leads to the following results as compared to the benchmark: (i) we found a better spatial consistency of the retrieved parameters, (ii) the reconstructed polarimetric radar observables are closer to the observations, (iii) The validation with disdrometer data confirms an improved estimation of the DSD parameters. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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22 pages, 6266 KiB  
Article
Raindrop Size Distribution Characteristics of the Western Pacific Tropical Cyclones Measured in the Palau Islands
by Balaji Kumar Seela, Jayalakshmi Janapati, Pay-Liam Lin, Chen-Hau Lan, Ryuichi Shirooka, Hiroyuki Hashiguchi and K. Krishna Reddy
Remote Sens. 2022, 14(3), 470; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030470 - 19 Jan 2022
Cited by 8 | Viewed by 2070
Abstract
Due to the severe threat of tropical cyclones to human life, recent years have witnessed an increase in the investigations on raindrop size distributions of tropical cyclones to improve their quantitative precipitation estimation algorithms and modeling simulations. So far, the raindrop size distributions [...] Read more.
Due to the severe threat of tropical cyclones to human life, recent years have witnessed an increase in the investigations on raindrop size distributions of tropical cyclones to improve their quantitative precipitation estimation algorithms and modeling simulations. So far, the raindrop size distributions of tropical cyclones using disdrometer measurements have been conducted at coastal and inland stations, but such studies are still missing for oceanic locations. To the authors’ knowledge, the current study examines—for the first time—the raindrop size distributions of fourteen tropical cyclones observed (during 2003–2007) at an oceanic station, Aimeliik, located in the Palau islands in the Western Pacific. The raindrop size distributions of Western Pacific tropical cyclones measured in the Palau islands showed unlike characteristics between stratiform and convective clusters, with a larger mass-weighted mean diameter and smaller normalized intercept parameter in the convective type. The contribution of the drop diameters to the total number concentration showed a gradual decrease with the increase in drop diameter size. Raindrop size distributions of Western Pacific tropical cyclones measured in the Palau islands differed slightly from Taiwan and Japan. The helpfulness of empirical relations in raindrop size distribution in rainfall estimation algorithms of ground-based (Z–R, μ–Λ, Dm–R, and Nw–R) and remote-sensing (σm–Dm, μo–Dm, Dm–Zku, and Dm–Zka) radars are evaluated. Furthermore, the present study also related the rainfall kinetic energy of fourteen tropical cyclones with rainfall rate and mass-weighted mean diameter (KEtime–R, KEmm–R, and KEmm–Dm). The raindrop size distribution empirical relations appraised in this study offer a chance to: (1) enhance the rain retrieval algorithms of ground-based, remote sensing radars; and (2) improve rainfall kinetic energy estimations using disdrometers and GPM DPR in rainfall erosivity studies. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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20 pages, 8123 KiB  
Article
Hydrometeor Classification of Winter Precipitation in Northern China Based on Multi-Platform Radar Observation System
by Yichen Chen, Xiang’e Liu, Kai Bi and Delong Zhao
Remote Sens. 2021, 13(24), 5070; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245070 - 14 Dec 2021
Cited by 3 | Viewed by 2161
Abstract
Hydrometeor classification remains a challenge in winter precipitation cloud systems. To address this issue, 42 snowfall events were investigated based on a multi-platform radar observation system (i.e., X-band dual-polarization radar, Ka-band millimeter wave cloud radar, microwave radiometer, airborne equipment, etc.) in the mountainous [...] Read more.
Hydrometeor classification remains a challenge in winter precipitation cloud systems. To address this issue, 42 snowfall events were investigated based on a multi-platform radar observation system (i.e., X-band dual-polarization radar, Ka-band millimeter wave cloud radar, microwave radiometer, airborne equipment, etc.) in the mountainous region of northern China from 2016 to 2020. A fuzzy logic classification method is proposed to identify the particle phases, and the retrieval result was further verified with ground-based radar observation. Moreover, the hydrometeor characteristics were compared with the numerical simulations to clarify the reliability of the proposed hydrometeor classification approach. The results demonstrate that the X-/Ka- band radars are capable of identifying hydrometeor phases in winter precipitation in accordance with both ground observations and numerical simulations. Three particle categories, including snow, graupel and the mixture of snow and graupel are also detected in the winter precipitation cloud system, and there are three vertical layers identified from top to bottom, including the ice crystal layer, snow-graupel mixed layer and snowflake layer. Overall, this study has the potential for improving the understanding of microphysical processes such as freezing, deposition and aggregation of ice crystal particles in the winter precipitation cloud system. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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21 pages, 25773 KiB  
Article
A Comparative Study on the Vertical Structures and Microphysical Properties of Stratiform Precipitation over South China and the Tibetan Plateau
by Jingshu He, Jiafeng Zheng, Zhengmao Zeng, Yuzhang Che, Min Zheng and Jianjie Li
Remote Sens. 2021, 13(15), 2897; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152897 - 23 Jul 2021
Cited by 8 | Viewed by 2085
Abstract
Under different water vapor and dynamic conditions, and the influence of topographies and atmospheric environments, stratiform precipitation over South China and the Tibetan Plateau can produce different features. In this study, stratiform precipitation vertical characteristics, bright-band (BB) microstructures, and the vertical variations of [...] Read more.
Under different water vapor and dynamic conditions, and the influence of topographies and atmospheric environments, stratiform precipitation over South China and the Tibetan Plateau can produce different features. In this study, stratiform precipitation vertical characteristics, bright-band (BB) microstructures, and the vertical variations of the raindrop size distribution (DSD) over a low-altitude site (Longmen site, 86 m) in South China and a high-altitude site (Nagqu site, 4507 m) on the Tibetan Plateau were comprehensively investigated and compared using measurements from a Ka-band millimeter-wave cloud radar (CR), a K-band microrain radar (MRR), and a Parsivel disdrometer (disdrometer). A reliable BB identification scheme was proposed on the basis of CR variables and used for stratiform precipitation sample selection and further statistics and analysis. Results indicate that melting layers over the Longmen are much higher and slightly thicker than those over the Nagqu due to significant differences in atmospheric conditions. For stratiform precipitation, vertical air motions and radar variables over the two sites show different variation trends from cloud top to the ground. Vertical air motions are very weak in the stratiform precipitation over the Longmen, whereas updrafts are more active over the Nagqu. Above the melting layer, radar equivalent reflectivity factor Ze (mean Doppler velocity VM) gradually increases (decreases) as height decreases over the two sites, but the aggregation rate for ice particles over the Longmen can be faster. In the melting layer, Ze (VM) at the BB bottom/center over the Longmen is larger (smaller) than those over the Nagqu for the reason that melted raindrops in the melting layers over the Longmen are larger than those over the Nagqu. Below the melting layer, profiles of radar variables and DSDs show completely different behaviors over the two sites, which reflects that the collision, coalescence, evaporation, and breakup processes of raindrops are different between the two sites. Over the Longmen, collision and coalescence dominate the precipitation properties; in particular, from 2.0–2.8 km, the breakup process competes with collision–coalescence processes but later is overpowered. In contrast, due to the lower BB heights over the Nagqu, collision and coalescence dominate raindrop properties. Comparisons of raindrop spectra suggest that the concentration of small (medium-to-large) raindrops over the Nagqu is much higher (slightly lower) than that over the Longmen. Therefore, the mass-weighted mean diameter Dm (the generalized intercept parameter Nw) over the Nagqu is smaller (larger) than that over the Longmen. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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20 pages, 3772 KiB  
Article
Application of Ground-Based Microwave Radiometer in Retrieving Meteorological Characteristics of Tibet Plateau
by Jiahua Wei, Yang Shi, Yan Ren, Qiong Li, Zhen Qiao, Jiongwei Cao, Olusola O. Ayantobo, Jianguo Yin and Guangqian Wang
Remote Sens. 2021, 13(13), 2527; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132527 - 28 Jun 2021
Cited by 14 | Viewed by 2443
Abstract
The characteristics of plateau precipitation and atmosphere, once accurately and comprehensively understood, can be used to inform sound air–water resource development practices. In this study, atmospheric exploration of the Tibet Plateau (TP) was conducted using ground-based microwave radiometer (MWR) data collected during the [...] Read more.
The characteristics of plateau precipitation and atmosphere, once accurately and comprehensively understood, can be used to inform sound air–water resource development practices. In this study, atmospheric exploration of the Tibet Plateau (TP) was conducted using ground-based microwave radiometer (MWR) data collected during the East Asian summer monsoon. Atmospheric temperature, pressure, humidity, and other variables were gathered under clear-sky, cloudy-sky, and rainy-sky conditions. Statistical characteristics of the air parcel height and stability/convection indices such as convective available potential energy (CAPE) and convective inhibition (CIN) were investigated, with a special focus on the rainy-sky condition. Two retrieval applications for characterizing precipitation, namely short-term precipitation forecast and quantitative precipitation estimation were presented. Results showed that CAPE values in the Darlag region reached extremes around 18:00–20:00 (UTC+8) for cloudy-sky and rainy-sky conditions with corresponding peaks of about 1046.56 J/kg and 703.02 J/kg, respectively. When stratiform or convective–mixed precipitation occurs, the precipitable water vapor (PWV) and CAPE values were generally greater than 1.7 cm and 1000 J/kg, respectively. CAPE values are likely to decrease before the occurrence of precipitation due to the release of the latent heat in the atmosphere. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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21 pages, 6666 KiB  
Article
Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter
by Wooyoung Na and Chulsang Yoo
Remote Sens. 2021, 13(12), 2365; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122365 - 17 Jun 2021
Cited by 1 | Viewed by 2035
Abstract
The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared [...] Read more.
The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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17 pages, 3192 KiB  
Article
Melting Layer Detection and Observation with the NCAR Airborne W-Band Radar
by Ulrike Romatschke
Remote Sens. 2021, 13(9), 1660; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091660 - 24 Apr 2021
Cited by 7 | Viewed by 2257
Abstract
A melting layer detection algorithm is developed for the NCAR 94 GHz airborne cloud radar (HIAPER CloudRadar, HCR). The detection method is based on maxima in the linear depolarization ratio and a discontinuity in the radial velocity field. A melting layer field is [...] Read more.
A melting layer detection algorithm is developed for the NCAR 94 GHz airborne cloud radar (HIAPER CloudRadar, HCR). The detection method is based on maxima in the linear depolarization ratio and a discontinuity in the radial velocity field. A melting layer field is added to the radar data, which provides detected, interpolated, and estimated altitudes of the melting layer and the altitude of the 0 °C isotherm detected in model temperature data. The icing level is defined as the lowest melting layer, and the cloud data are flagged as either above (cold) or below (warm) the icing level. Analysis of the detected melting layer shows that the offset between the 0 °C isotherm and the actual melting layer varies with cloud type: in heavy convection sampled in the tropics, the melting layer is found up to 500 m below the 0 °C isotherm, while in shallow clouds, the offset is much smaller or sometimes vanishes completely. A relationship between the offset and the particle fall speed both above and below the melting layer is established. Special phenomena, such as a lowering of the melting layer towards the center of storms or split melting layers, were observed. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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19 pages, 6768 KiB  
Article
Challenges of the Polarimetric Update on Operational Radars in China—Ground Clutter Contamination of Weather Radar Observations
by Chong Wu, Liping Liu, Chao Chen, Chian Zhang, Guangxin He and Juan Li
Remote Sens. 2021, 13(2), 217; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020217 - 10 Jan 2021
Cited by 3 | Viewed by 2776
Abstract
China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter’s response to the ground clutter. This polarimetric [...] Read more.
China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter’s response to the ground clutter. This polarimetric contamination was characterized by decreased differential reflectivity (ZDR) and cross-correlation coefficient (ρhv), as well as an increased standard deviation of the differential phase (ΦDP), generating a large-area and long-term observational anomaly for eight polarimetric radars in South China. Considering that outliers simultaneously appeared in the radar mainlobe and sidelobe, the variations in the reflectivity before and after clutter mitigation (ΔZH) and ρhv were used for quantitatively describing the random dispersion caused by mainlobe and sidelobe clutters. The performance of polarimetric algorithms was also reduced by clutter contamination. The deteriorated membership functions in the hydrometeor classification algorithm changed the proportion of classified echoes. The empirical relations of R(ZH, ZDR) and R(KDP) were broken in the quantitative precipitation estimation algorithm and the extra error considerably exceeded the uncertainty caused by the drop-size distribution (DSD) variability of R(ZH). The above results highlighted the negative impact of clutter contamination on polarimetric applications that need to be further investigated. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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27 pages, 6874 KiB  
Article
Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars
by Yang Zhang, Liping Liu and Hao Wen
Remote Sens. 2020, 12(21), 3557; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213557 - 30 Oct 2020
Cited by 5 | Viewed by 2368
Abstract
The quality of radar data is crucial for its application. In particular, before radar mosaic and quantitative precipitation estimation (QPE) can be conducted, it is necessary to know the quality of polarimetric parameters. The parameters include the horizontal reflectivity factor, ZH; [...] Read more.
The quality of radar data is crucial for its application. In particular, before radar mosaic and quantitative precipitation estimation (QPE) can be conducted, it is necessary to know the quality of polarimetric parameters. The parameters include the horizontal reflectivity factor, ZH; the differential reflectivity factor, ZDR; the specific differential phase, KDP; and the correlation coefficient, ρHV. A novel radar data quality index (RQI) is specifically developed for the Chinese polarimetric radars. Not only the influences of partial beam blockages and bright band upon radar data quality, but also those of bright band correction performance, signal-to-noise ratio, and non-precipitation echoes are considered in the index. RQI can quantitatively describe the quality of various polarimetric parameters. A new radar mosaic QPE algorithm based on RQI is presented in this study, which can be used in different regions with the default values adjusted according to the characteristics of local radar. RQI in this algorithm is widely used for high-quality polarimetric radar data screening and mosaic data merging. Bright band correction is also performed to errors of polarimetric parameters caused by melting ice particles for warm seasons in this algorithm. This algorithm is validated by using nine rainfall events in Guangdong province, China. Major conclusions are as follows. ZH, ZDR, and KDP in bright band become closer to those under bright band after correction than before. However, the influence of KDP correction upon QPE is not as good as that of ZH and ZDR correction in bright band. Only ZH and ZDR are used to estimate precipitation in the bright band affected area. The new mosaic QPE algorithm can improve QPE performances not only in the beam blocked areas and the bright band affected area, which are far from radars, but also in areas close to the two radars. The sensitivity tests show the new algorithm can perform well and stably for any type of precipitation occurred in warm seasons. This algorithm lays a foundation for regional polarimetric radar mosaic precipitation estimation in China. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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19 pages, 6674 KiB  
Article
Establishment and Preliminary Application of the Forward Modeling Method for Doppler Spectral Density of Ice Particles
by Han Ding and Liping Liu
Remote Sens. 2020, 12(20), 3378; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203378 - 15 Oct 2020
Cited by 1 | Viewed by 1600
Abstract
Owing to the various shapes of ice particles, the relationships between fall velocity, backscattering cross-section, mass, and particle size are complicated. This affects the application of cloud radar Doppler spectral density data in the retrieval of the microphysical properties of ice crystals. In [...] Read more.
Owing to the various shapes of ice particles, the relationships between fall velocity, backscattering cross-section, mass, and particle size are complicated. This affects the application of cloud radar Doppler spectral density data in the retrieval of the microphysical properties of ice crystals. In this study, under the assumption of six particle shape types, the relationships between particle mass, fall velocity, backscattering cross-section, and particle size were established based on existing research. Variations of Doppler spectral density with the same particle size distribution (PSD) of different ice particle types are discussed. The radar-retrieved liquid and ice PSDs, water content, and mean volume-weighted particle diameter were compared with airborne in situ observations in the Xingtai, Hebei Province, China, in 2018. The results showed the following. (1) For the particles with the same equivalent diameter (De), the fall velocity of the aggregates was the largest, followed by hexagonal columns, hexagonal plates, sector plates, and stellar crystals, with the ice spheres falling two to three times faster than ice crystals with the same De. Hexagonal columns had the largest backscattering cross-section, followed by stellar crystals and sector plates, and the backscattering cross-sections of hexagonal plates and the two types of aggregates were very close to those of ice spheres. (2) The width of the simulated radar Doppler spectral density generated by various ice crystal types with the same PSD was mainly affected by the particle’s falling velocity, which increased with the particle size. Turbulence had different degrees of influence on the Doppler spectrum of different ice crystals, and it also brought large errors to the PSD retrieval. (3) PSD comparisons showed that each ice crystal type retrieved from the cloud radar corresponded well to aircraft observations within a certain scale range, when assuming that only a certain type of ice crystals existed in the cloud, which could fully prove the feasibility of retrieving ice PSDs from the reflectivity spectral density. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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