Corrigendum to “Attenuation Correction of Weather Radar Reflectivity with Arbitrary Oriented Microwave Link”

Rain attenuation can hinder the implementation of quantitative precipitation estimations using X-band weather radar. Numerous studies have been conducted on correcting the attenuation of radar reflectivity by utilizing a dual-polarimetric radar and an arbitrary-oriented microwave link; however, there is a need to optimize the required number of microwave links and their locations. In this study, we tested four attenuation correction methods and proposed a novel algorithm based on the sole use of adjacent multiple microwave links. The attenuation of the X-band radar reflectivity was corrected by performing forward iterations at each link, and the correction coefficients were statistically analyzed to reduce the instability problem. The algorithms of each method were evaluated by studying the cases of convective and stratiform rainfall, and then validated by comparing the corrected reflectivity of the X-band radar with the qualitatively controlled reflectivity of the S-band radar. The new method was as efficient as the conventional method based on the specific differential phase of dual-polarimetric radar. Furthermore, the correction coefficient was more effectively optimized and stabilized using seven microwave links rather than a single link, and no further independent reference data were required. In addition, the attenuation correction also accounted for spatiotemporal differentiation depending on the rainfall type, and could recover the physical structure of the rainfall. The method developed herein can facilitate estimations of quantitative rainfall in developing countries where dual-polarization weather radars are not common. The exploitation of microwave link data is a promising method for rainfall remote sensing.

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Regional Attenuation Correction of Weather Radar Using a Distributed Microwave-Links Network

Advances in Meteorology ◽

10.1155/2017/8621239 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Yang Xue ◽

Xi-chuan Liu ◽

Tai-chang Gao ◽

Chang-ye Yang ◽

Kun Song

Keyword(s):

Attenuation Correction ◽

Tomographic Reconstruction ◽

Weather Radar ◽

Radar Data ◽

Radar Reflectivity ◽

Correction Coefficient ◽

Average Deviation ◽

Initial Field ◽

Attenuation Coefficients ◽

Reflectivity Factor

The complex temporal-spatial variation of raindrop size distribution will affect the precision of precipitation quantitative estimates (QPE) produced from radar data, making it difficult to correct echo attenuation. Given the fact that microwave links can obtain the total path attenuation accurately, we introduce the concept of regional attenuation correction using a multiple-microwave-links network based on the tomographic reconstruction of attenuation coefficients. Derived from the radar-based equation, the effect of rainfall distribution on the propagation of radar and microwave link signals was analyzed. This article focuses on modeling of the tomographic reconstruction of attenuation coefficients and regional attenuation correction algorithms. Finally, a numerical simulation of regional attenuation correction was performed to verify the algorithms employed here. The results demonstrate that the correction coefficient (0.9175) falls between the corrected and initial field of radar reflectivity factor (root mean square error, 2.3476 dBz; average deviation, 0.0113 dBz). Compared with uncorrected data, the accuracy of the corrected radar reflectivity factor was improved by 26.12%, and the corrected rainfall intensity distribution was improved by 51.85% validating the region attenuation correction algorithm. This method can correct the regional attenuation of weather radar echo effectively and efficiently; it can be widely used for the radar attenuation correction and the promotion of quantitative precipitation estimation by weather radar.

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PENGARUH KOREKSI ATENUASI RADAR CUACA TERHADAP PERHITUNGAN ESTIMASI CURAH HUJAN DI JAWA TIMUR

Jurnal Teknosains ◽

10.22146/teknosains.53452 ◽

2021 ◽

Vol 10 (2) ◽

pp. 111

Author(s):

Ahmad Kosasih ◽

Hartono Hartono ◽

Retnadi Heru Jatmiko

Keyword(s):

Attenuation Correction ◽

Weather Radar ◽

Radar Reflectivity ◽

Estimation Accuracy ◽

Correction Coefficient ◽

Rainfall Estimation ◽

Raw Data ◽

Before And After ◽

R Relation ◽

Rainfall Estimates

Rainfall estimation using band C weather radar creates uncertainty in the results of its estimation accuracy. The cause is meteorological and non-meteorological disturbances that affect the reflectivity raw data (dBz), one of which is attenuation due to rain, especially with heavy and very heavy intensity. This study aims to evaluate the attenuation correction ability of the reflectivity raw data generated by the weather radar against the calculation of rainfall estimates at the Juanda Sidoarjo Meteorological Station, as well as the best attenuation correction coefficient to be applied in the processing of rainfall estimates by weather radar. The method used to perform attenuation correction is Z-based attenuation correction (ZATC). The calculation of attenuation correction using the ZATC method uses several α and β coefficients while the Z-R relation (Z = 200R1.6) is used to calculate the estimated rainfall before and after attenuation correction. The results showed that the attenuation correction of the C band weather radar reflectivity raw data was able to provide an increase in the accuracy of rainfall estimation where in the estimation of rainfall from a weather radar without the attenuation correction stage of the raw data, an accuracy value of 70.8% was obtained, while applying the attenuation correction using several The α and β coefficients obtained an increase in the accuracy of rainfall estimation between 72.5% to 86.9%. The best α and β coefficients for attenuation correction of weather radar reflectivity (dBz) can be applied in obtaining a more accurate rainfall estimate, namely the α and β coefficients according to Krämer and Verworn which are able to provide an increase in the accuracy of rainfall estimation by 16.1%.

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Attenuation Correction of Weather Radar Reflectivity with Arbitrary Oriented Microwave Link

Advances in Meteorology ◽

10.1155/2017/6124149 ◽

2017 ◽

Vol 2017 ◽

pp. 1-17 ◽

Cited By ~ 9

Author(s):

Peng Zhang ◽

Xichuan Liu ◽

Zhaoming Li ◽

Zeming Zhou ◽

Kun Song ◽

...

Keyword(s):

Attenuation Correction ◽

Real Data ◽

Radar Reflectivity ◽

Attenuation Effect ◽

Rain Gauges ◽

X Band ◽

Variation Range ◽

Microwave Link ◽

Rain Rates ◽

Key Parameter

To compensate radar reflectivity for attenuation effect, a new method for attenuation correction of the radar reflectivity using arbitrary oriented microwave link (referred henceforth to as ACML) is developed and evaluated. Referring to the measurement of arbitrary oriented microwave link, the ACML method optimizes the ratio of specific attenuation to specific differential phase which is a key parameter in attenuation correction schemes. The proposed method was evaluated using real data of a dual-polarization X-band radar and measurements of two microwave links during two rainstorm events. The results showed that the variation range of the optimized ratio was overall consistent with the results of the previous studies. After attenuation correction with the optimal ratios, the radar reflectivity was significantly compensated, especially at long distances. The corrected reflectivity was more intense than the reflectivity corrected by the “self-consistent” (SC) method and closer to the reflectivity of a nearby S-band radar. The effectiveness of the method was also verified by comparing the rain rates estimated by the X-band radar with those derived by rain gauges. It is demonstrated that arbitrary oriented microwave link can be adopted to optimize the attenuation correction of radar reflectivity.

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Precipitation cloud identification based on faster-RCNN for Doppler weather radar

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01896-5 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Yuanbo Ran ◽

Haijiang Wang ◽

Li Tian ◽

Jiang Wu ◽

Xiaohong Li

Keyword(s):

Radial Velocity ◽

Vertical Direction ◽

Weather Radar ◽

Radar Reflectivity ◽

Convective Precipitation ◽

Horizontal Wind ◽

Doppler Weather Radar ◽

Velocity Data ◽

Precipitation Processes ◽

Radial Velocity Data

AbstractPrecipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

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Radar Quality Index for a Mosaic of Radar Reflectivity over Chao Phraya River Basin, Thailand

Applied Environmental Research ◽

10.35762/aer.2020.42.3.8 ◽

2020 ◽

pp. 92-104

Author(s):

Nattapon Mahavik ◽

Sarintip Tantanee

Keyword(s):

River Basin ◽

Quality Index ◽

Beam Propagation ◽

Weather Radar ◽

Radar Reflectivity ◽

Temporal Information ◽

Radar Beam ◽

Spatio Temporal ◽

Chao Phraya River ◽

Application Fields

The weather radar is one of the tools that can provide spatio-temporal information for nowcast which is useful for hydro-meteorological disasters warning and mitigation system. The ground-based weather radar can provide spatial and temporal information to monitor severe storm over the risky area. However, the usage of multiple radars can provide more effective information over large study area where single radar beam may be blocked by surrounding terrain Even though, the investigation of the sever storm physical characteristics needs the information from multiple radars, the mosaicked radar product has not been available for Thai researcher yet. In this study, algorithm of mosaicked radar reflectivity has been developed by using data from ground-based radar of Thai Meteorological Department over the Chao Phraya river basin in the middle of Thailand. The Python script associated with OpenCV and Wradlib libraries were used in our investigations of the mosaicking processes. The radar quality index (RQI) field has been developed by implementing an equation of a quality radar index to identify the reliability of each mosaicked radar reflectivity pixels. First, the percentage of beam blockage is computed to understand the radar beam propagation obstructed by surrounding topography in order to clarify the limitations of the observed beam on producing radar reflectivity maps. Second, the elevation of beam propagation associated with distance field has been computed. Then, these three parameters and the obtained percentage of beam blockage are utilized as the parameters in the equation of RQI. Finally, the detected radar flare, non-precipitating radar area, has been included to the RQI field. Then, the RQI field has been applied to the extracted radar reflectivity to evaluate the quality of mosaicked radar reflectivity to inform end user in any application fields over the Chao Phraya river basin.

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Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012054 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012054

Author(s):

M F Handoyo ◽

M P Hadi ◽

S Suprayogi

Keyword(s):

Attenuation Correction ◽

Estimation Error ◽

Weather Radar ◽

Radar Data ◽

Signal Interference ◽

Active System ◽

Estimation Errors ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation

Abstract A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

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