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Radar-Based Studies of Precipitation Systems and Their Microphysics

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 38373

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Special Issue Editors

Prof. Dr. Gyuwon Lee

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Guest Editor

Department of Astronomy and Atmospheric Sciences, School of Earth System Sciences, The Kyungpook National University (KNU), Daegu 41566, Republic of Korea
Interests: radar meteorology; precipitation microphysics; nowcasting; severe weather; instrumentation

Prof. Dr. Alexander Ryzhkov

E-Mail Website
Guest Editor

Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma and National Severe Storms Laboratory, Norman, OK 73072, USA
Interests: radar meteorology; precipitation microphysics; dual-polarimetry

Special Issue Information

Dear Colleagues,

Radar-based studies on understanding precipitation systems and their microphysical processes have dramatically advanced for the last few decades due to technological development of weather radars, in particular, dual-polarimetric radars and multiwavelength radars and advanced theory of the microphysical processes. This advancement continuously expands the fields of application of weather radar remote sensing.

With this Special Issue, we systematically document state-of-the-art research that specifically addresses various aspects of weather radar applications into (1) precipitation (rain and snow) estimation, (2) description and understanding of microphysical processes, (3) comprehensive studies of microphysical aspects of precipitation systems, (4) ground and/or space-borne observation of precipitation, (5) precipitation process study with advanced measurements, and (6) scale aspects of precipitation systems. Review contributions are most welcome and papers describing new techniques, concepts, and comprehensive understanding of precipitation are desired.

Prof. Dr. Gyuwon Lee
Prof. Dr. Alexander Ryzhkov
Guest Editors

Manuscript Submission Information

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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

Dual-polarimetric radar
Multiwavelength (multi-frequency) radar
Ground-based and/or Space-borne radar
Precipitation (rain and snow) estimation
Precipitation microphysics
Precipitation systems
Precipitation study
Mesoscale convective systems
Drop size distributions
Retrieval of drop size distributions
Microphysical processes
Melting, (re-)freezing, riming, aggregation
Secondary ice generation

Published Papers (14 papers)

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Research

Jump to: Review

18 pages, 3560 KiB

Open AccessArticle

Precipitation Microphysics during the Extreme Meiyu Period in 2020

by Aoqi Zhang, Yilun Chen, Shengnan Zhou, Shumin Chen and Weibiao Li

Remote Sens. 2022, 14(7), 1651; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071651 - 30 Mar 2022

Cited by 2 | Viewed by 1485

Abstract

Previous studies have reported the large-scale meteorological conditions and dynamic causes of the extreme period of meiyu rainfall in 2020. However, the microphysical properties of meiyu precipitation during this period remain unclear. We used the Global Precipitation Measurement 2ADPR orbital precipitation dataset, the IMERG gridded precipitation dataset and the ERA5 reanalysis dataset to study the characteristics of meiyu precipitation over the Yangtze Plain during the extreme meiyu period in 2020 and historical meiyu periods from 2014 to 2019. The results showed that the average daily rainfall during the 2020 meiyu period was 1.5 times higher than the historical average as a result of the super-strong water vapor flux in the low- to mid-level layers of the atmosphere. The amplitude of nocturnal low-level water vapor transport during the 2020 meiyu period was twice the historical average and, therefore, the diurnal peak of meiyu rainfall at 0630 LST in 2020 was significantly earlier than the historical average. The moisture transport was the dominant moisture supply for precipitation during the 2020 meiyu period, whereas the moisture convection contributed less than in the meiyu periods of 2014–2019. This led to the precipitation in the 2020 meiyu period having a higher concentration of smaller droplets than the historical average. There were lower proportions of size-sorting evaporation and break-up processes in the liquid-phase precipitation processes in the 2020 meiyu than the historical average, but a higher proportion of coalescence processes. These results provide a factual basis for the simulation and forecast of precipitation during extreme meiyu periods. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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17 pages, 5155 KiB

Open AccessArticle

How Much Attenuation Extinguishes mm-Wave Vertically Pointing Radar Return Signals?

by Christopher R. Williams

Remote Sens. 2022, 14(6), 1305; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061305 - 08 Mar 2022

Cited by 4 | Viewed by 1637

Abstract

Vertically pointing radars (VPRs) operating at millimeter wavelengths measure the power return from raindrops enabling precipitation retrievals as a function of height. However, as the rain rate increases, there are combinations of rain rate and rain path length that produce sufficient attenuation to prevent the radar from detecting raindrops all the way through rain shafts. This study explores the question: Which rain rate and path length combinations completely extinguish radar return signals for VPRs operating between 3 and 200 GHz? An important step in these simulations is converting attenuated radar reflectivity factor into radar received signal-to-noise ratio (SNR) in order to determine the range where the SNR drops below the receiver detection threshold. Configuring the simulations to mimic a U.S. Department of Energy Atmospheric Radiation Mission (ARM) W-band (95 GHz) radar deployed in Brazil, the simulation results indicate that a W-band radar could observe raindrops above 3.5 km only when the rain rate was less than approximately 4 mm h⁻¹. The deployed W-band radar measurements confirm the simulation results with maximum observed heights ranging between 3 and 4.5 km when a surface disdrometer measured 4 mm h⁻¹ rain rate (based on 25-to-75 percentiles from over 25,000 W-band radar profiles). In summary, this study contributes to our understanding of how rain and atmospheric gas attenuation impacts the performance of millimeter-wave VPRs and will help with the design and configuration of multi-frequency VPRs deployed in future field campaigns. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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30 pages, 17108 KiB

Open AccessArticle

Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems

by Jeong-Ho Bae and Ki-Hong Min

Remote Sens. 2022, 14(3), 605; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030605 - 27 Jan 2022

Cited by 4 | Viewed by 1979

Abstract

Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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20 pages, 9927 KiB

Open AccessArticle

Vertical Structure of Ice Clouds and Vertical Air Motion from Vertically Pointing Cloud Radar Measurements

by Bo-Young Ye and GyuWon Lee

Remote Sens. 2021, 13(21), 4349; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214349 - 29 Oct 2021

Cited by 3 | Viewed by 2088

Abstract

The vertical structure of ice clouds and vertical air motion (V_air) were investigated using vertically pointing Ka-band cloud radar. The distributions of reflectivity (Z), Doppler velocity (V_D), and spectrum width (SW) were analyzed for three ice cloud types, namely, cirrus, anvil, and stratiform clouds. The radar parameters of the cirrus clouds showed narrower distributions than those of the stratiform and anvil clouds. In the vertical structures, the rapid growth of Z and V_D occurred in the layer between 8 and 12 km (roughly a layer of −40 °C to −20 °C) for all ice clouds. The prominent feature in the stratiform clouds was an elongated “S” shape in the V_D near 7–7.5 km (at approximately −16 °C to −13 °C) due to a significant decrease in an absolute value of V_D. The mean terminal fall velocity (V_t) and V_air in the ice clouds were estimated using pre-determined V_t–Z relationships (V_t = aZ^b) and the observed V_D. Although the cirrus clouds demonstrated wide distributions in coefficients a and exponents b depending on cloud heights, they showed a smaller change in Z and V_t values compared to that of the other cloud types. The anvil clouds had a larger exponent than that of the stratiform clouds, indicating that the ice particle density of anvil clouds increases at a faster rate compared with the density of stratiform clouds for the same Z increment. The significant positive V_air appeared at the top of all ice clouds in range up to 0.5 m s⁻¹, and the anvil clouds showed the deepest layer of upward motion. The stratiform and anvil clouds showed a dramatic increase in vertical air motion in the layer of 6–8 km as shown by the rapid decrease of V_D. This likely caused increase of supersaturation above. A periodic positive V_air linked with a significant reduction in V_D appeared at the height of 7–8 km (approximately −15 °C) dominantly in the stratiform clouds. This layer exhibited a bi-modal power spectrum produced by pre-existing larger ice particles and newly formed numerous smaller ice particles. This result raised a question on the origins of smaller ice particles such as new nucleation due to increased supersaturation by upward motion below or the seeder-feeder effect. In addition, the retrieved V_air with high-resolution data well represented a Kelvin-Helmholtz wave development. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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20 pages, 7419 KiB

Open AccessArticle

A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations

by Albert Garcia-Benadí, Joan Bech, Sergi Gonzalez, Mireia Udina and Bernat Codina

Remote Sens. 2021, 13(21), 4323; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214323 - 27 Oct 2021

Cited by 7 | Viewed by 2775

Abstract

The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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21 pages, 14236 KiB

Open AccessArticle

Revision of WDM7 Microphysics Scheme and Evaluation for Precipitating Convection over the Korean Peninsula

by Sungbin Jang, Kyo-Sun Sunny Lim, Jeongsu Ko, Kwonil Kim, GyuWon Lee, Su-Jeong Cho, Kwang-Deuk Ahn and Yong-Hee Lee

Remote Sens. 2021, 13(19), 3860; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193860 - 27 Sep 2021

Cited by 7 | Viewed by 2771

Abstract

The Weather Research and Forecasting (WRF) Double-Moment 7-Class (WDM7) cloud microphysics scheme was developed to parameterize cloud and precipitation processes explicitly for mesoscale phenomena in the Korean Integrated Model system. However, the WDM7 scheme has not been evaluated for any precipitating convection system over the Korean peninsula. This study modified WDM7 and evaluated simulated convection during summer and winter. The suggested modifications included the integration of the new fall velocity–diameter relationship of raindrops and mass-weighted terminal velocity of solid-phase precipitable hydrometeors (the latter is for representing mixed-phase particles). The mass-weighted terminal velocity for snow and graupel has been suggested by Dudhia et al. (2008) to allow for a more realistic representation of partially rimed particles. The WDM7 scheme having an additional hail category does not apply this terminal velocity only for hail. Additionally, the impact of enhanced collision-coalescence (C-C) efficiency was investigated. An experiment with enhanced C-C efficiency overall improved the precipitation skill scores, such as probability of detection, equitable threat score, and spatial pattern correlation, compared with those of the control experiment for the summer and winter cases. With application of the new mass-weighted terminal velocity of solid-phase hydrometeors, the hail mixing ratio at the surface was considerably reduced, and rain shafts slowed down low-level winds for the winter convective system. Consequently, the simulated hydrometeors were consistent with observations retrieved via remote sensing. The fall velocity–diameter relationship of raindrops further reduced the cloud ice amount. The proposed modifications in our study improved the simulated precipitation and hydrometeor profiles, especially for the selected winter convection case. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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15 pages, 2849 KiB

Open AccessArticle

Statistical Characteristics of Raindrop Size Distribution in Monsoon Season over South China Sea

by Chaoying Huang, Sheng Chen, Asi Zhang and Ying Pang

Remote Sens. 2021, 13(15), 2878; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152878 - 22 Jul 2021

Cited by 11 | Viewed by 2078

Abstract

The South China Sea (SCS) is the largest and southernmost sea in China. Water vapor from the SCS is the primary source of precipitation over coastal areas during the summer monsoon season and may cause the uneven distribution of rainfall in southern China. Deep insight into the spatial variability of raindrop size distribution (DSD) is essential for understanding precipitation microphysics, since DSD contains abundant information about rainfall microphysics processes. However, compared to the studies of DSDs over mainland China, very little is known about DSDs over Chinese ocean areas, especially over the South China Sea (SCS). This study investigated the statistical characteristics of the DSD in summer monsoon seasons using the second-generation Particle Size and Velocity (Parsivel²) installed on the scientific research vessel that measured the size and velocity of raindrops over the SCS. In this study, the characteristics of precipitation over the SCS for daytime and nighttime rains were analyzed for different precipitation systems and upon different rain rates. It was found that: (1) rain events were more frequent during the late evening to early morning; (2) more than 78.2% of the raindrops’ diameters were less than 2 mm, and the average value of mass-weighted mean diameter

D_{m}

(1.46 mm) of the SCS is similar to that over land in the southern China; (3) the stratiform precipitation features a relatively high concentration of medium to large-sized rain drops compared to other regions; (4) the DSD in the SCS agreed with a three-parameter gamma distribution for the small raindrop diameter. Furthermore, a possible factor for significant DSD variability in the ocean compared with the coast and large islands is also discussed. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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20 pages, 20478 KiB

Open AccessArticle

Estimation of Precipitation Area Using S-Band Dual-Polarization Radar Measurements

by Joon Jin Song, Melissa Innerst, Kyuhee Shin, Bo-Young Ye, Minho Kim, Daejin Yeom and GyuWon Lee

Remote Sens. 2021, 13(11), 2039; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112039 - 21 May 2021

Cited by 5 | Viewed by 1865

Abstract

Estimating precipitation area is important for weather forecasting as well as real-time application. This paper aims to develop an analytical framework for efficient precipitation area estimation using S-band dual-polarization radar measurements. Several types of factors, such as types of sensors, thresholds, and models, are considered and compared to form a data set. After building the appropriate data set, this paper yields a rigorous comparison of classification methods in statistical (logistic regression and linear discriminant analysis) and machine learning (decision tree, support vector machine, and random forest). To achieve better performance, spatial classification is considered by incorporating latitude and longitude of observation location into classification, compared with non-spatial classification. The data used in this study were collected by rain detector and present weather sensor in a network of automated weather systems (AWS), and an S-band dual-polarimetric weather radar during ten different rainfall events of varying lengths. The mean squared prediction error (MSPE) from leave-one-out cross validation (LOOCV) is computed to assess the performance of the methods. Of the methods, the decision tree and random forest methods result in the lowest MSPE, and spatial classification outperforms non-spatial classification. Particularly, machine-learning-based spatial classification methods accurately estimate the precipitation area in the northern areas of the study region. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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18 pages, 7229 KiB

Open AccessArticle

Characteristics of the Bright Band Based on Quasi-Vertical Profiles of Polarimetric Observations from an S-Band Weather Radar Network

by Jeong-Eun Lee, Sung-Hwa Jung and Soohyun Kwon

Remote Sens. 2020, 12(24), 4061; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244061 - 11 Dec 2020

Cited by 8 | Viewed by 2339

Abstract

Bright band (BB) characteristics obtained via dual-polarization weather radars elucidate thermodynamic and microphysical processes within precipitation systems. This study identified BB using morphological features from quasi-vertical profiles (QVPs) of polarimetric observations, and their geometric, thermodynamic, and polarimetric characteristics were statistically examined using nine operational S-band weather radars in South Korea. For comparable analysis among weather radars in the network, the calibration biases in reflectivity (Z_H) and differential reflectivity (Z_DR) were corrected based on self-consistency. The cross-correlation coefficient (ρ_HV) bias in the weak echo regions was corrected using the signal-to-noise ratio (SNR). First, we analyzed the heights of BB_PEAK derived from the Z_H as a function of season and compared the heights of BB_PEAK derived from the Z_H, Z_DR, and ρ_HV. The heights of BB_PEAK were highest in the summer season when the surface temperature was high. However, they showed distinct differences depending on the location (e.g., latitude) within the radar network, even in the same season. The height where the size of melting particles was at a maximum (BB_PEAK from the Z_H) was above that where the oblateness of these particles maximized (BB_PEAK from Z_DR). The height at which the inhomogeneity of hydometeors was at maximum (BB_PEAK from the ρ_HV) was also below that of BB_PEAK from the Z_H. Second, BB thickness and relative position of BB_PEAK were investigated to characterize the geometric structure of the BBs. The BB thickness increased as the Z_H at BB_BOTTOM increased, which indicated that large snowflakes melt more slowly than small snowflakes. The geometrical structure of the BBs was asymmetric, since the melting particles spent more time forming the thin shell of meltwater around them, and they rapidly collapsed to form a raindrop at the final stage of melting. Third, the heights of BB_TOP, BB_PEAK, and BB_BOTTOM were compared with the zero-isotherm heights. The dry-temperature zero-isotherm heights were between BB_TOP and BB_BOTTOM, while the wet-bulb temperature zero-isotherm heights were close to the height of BB_PEAK. Finally, we examined the polarimetric observations to understand the involved microphysical processes. The correlation among Z_H at BB_TOP, BB_PEAK, and BB_BOTTOM was high (>0.94), and the Z_DR at BB_BOTTOM was high when the BB’s intensity was strong. This proved that the size and concentration of snowflakes above the BB influence the size and concentration of raindrops below the BB. There was no depression in the ρ_HV for a weak BB. Finally, the mean profile of the Z_H and Z_DR depended on the Z_H at BB_BOTTOM. In conclusion, the growth process of snowflakes above the BB controls polarimetric observations of BB. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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Review

Jump to: Research

26 pages, 3976 KiB

Open AccessReview

The Retrieval of Drop Size Distribution Parameters Using a Dual-Polarimetric Radar

by GyuWon Lee, Viswanathan Bringi and Merhala Thurai

Remote Sens. 2023, 15(4), 1063; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041063 - 15 Feb 2023

Cited by 2 | Viewed by 1935

Abstract

The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD representation and its linkage to polarimetric radar observables from functional forms (exponential, gamma, and generalized gamma models) and its normalization (un-normalized, single/double-moment scaling normalized). The four-parameter generalized gamma model is a good candidate for the optimal representation of the DSD variability. A radar-based disdrometer was found to describe the five archetypical shapes (from Montreal, Canada) consisting of drizzle, the larger precipitation drops and the ‘S’-shaped curvature that occurs frequently in between the drizzle and the larger-sized precipitation. Similar ‘S’-shaped DSDs were reproduced by combining the disdrometric measurements of small-sized drops from an optical array probe and large-sized drops from 2DVD. A unified theory based on the double-moment scaling normalization is described. The theory assumes the multiple power law among moments and DSDs are scaling normalized by the two characteristic parameters which are expressed as a combination of any two moments. The normalized DSDs are remarkably stable. Thus, the mean underlying shape is fitted to the generalized gamma model from which the ‘optimized’ two shape parameters are obtained. The other moments of the distribution are obtained as the product of power laws of the reference moments M₃ and M₆ along with the two shape parameters. These reference moments can be from dual-polarimetric measurements: M₆ from the attenuation-corrected reflectivity and M₃ from attenuation-corrected differential reflectivity and the specific differential propagation phase. Thus, all the moments of the distribution can be calculated, and the microphysical evolution of the DSD can be inferred. This is one of the major findings of this article. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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25 pages, 3500 KiB

Open AccessReview

Dual-Polarization Radar Fingerprints of Precipitation Physics: A Review

by Matthew R. Kumjian, Olivier P. Prat, Karly J. Reimel, Marcus van Lier-Walqui and Hughbert C. Morrison

Remote Sens. 2022, 14(15), 3706; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153706 - 02 Aug 2022

Cited by 11 | Viewed by 3427

Abstract

This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called “fingerprints” of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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33 pages, 15555 KiB

Open AccessReview

Polarimetric Radar Quantitative Precipitation Estimation

by Alexander Ryzhkov, Pengfei Zhang, Petar Bukovčić, Jian Zhang and Stephen Cocks

Remote Sens. 2022, 14(7), 1695; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071695 - 31 Mar 2022

Cited by 17 | Viewed by 3724

Abstract

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase K_DP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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23 pages, 5752 KiB

Open AccessReview

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

by Liang Liao and Robert Meneghini

Remote Sens. 2022, 14(4), 843; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040843 - 11 Feb 2022

Cited by 20 | Viewed by 3263

Abstract

The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (D_m) or an R-D_m relationship in solving for R and D_m simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and D_m in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and D_m estimates to some extent as the range-independent assumption of the adjustable parameter (ε) of the R-D_m relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (δPIA) used in the DPR algorithm to find the best ε and range-independent ε assumption. A range-variable ε model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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40 pages, 7924 KiB

Open AccessReview

Weather Radar in Complex Orography

by Urs Germann, Marco Boscacci, Lorenzo Clementi, Marco Gabella, Alessandro Hering, Maurizio Sartori, Ioannis V. Sideris and Bertrand Calpini

Remote Sens. 2022, 14(3), 503; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030503 - 21 Jan 2022

Cited by 18 | Viewed by 4853

Abstract

Applications of weather radar data to complex orography are manifold, as are the problems. The difficulties start with the choice of suitable locations for the radar sites and their construction, which often involves long transport routes and harsh weather conditions. The next challenge is the 24/7 operation and maintenance of the remote, unmanned mountain stations, with high demands on the availability and stability of the hardware. The data processing and product generation also require solutions that have been specifically designed and optimised in a mountainous region. The reflection and shielding of the beam by the mountains, in particular, pose great challenges. This review article discusses the main problems and sources of error and presents solutions for the application of weather radar technology in complex orography. The review is focused on operational radars and practical applications, such as nowcasting and the automatic warning of thunderstorms, heavy rainfall, hail, flash floods and debris flows. The presented material is based, to a great extent, on experience collected by the authors in the Swiss Alps. The results show that, in spite of the major difficulties that emerge in mountainous regions, weather radar data have an important value for many practical quantitative applications. Full article

(This article belongs to the Special Issue Radar-Based Studies of Precipitation Systems and Their Microphysics)

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