scholarly journals Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method

Atmosphere ◽  
2016 ◽  
Vol 7 (6) ◽  
pp. 83 ◽  
Author(s):  
Mohammad-Hossein Golbon-Haghighi ◽  
Guifu Zhang ◽  
Yinguang Li ◽  
Richard Doviak
Keyword(s):  
Weather Radar ◽  
Dual Polarization ◽  
Ground Clutter

scholarly journals Real-Time Calibration and Monitoring of Radar Reflectivity on Nationwide Dual-Polarization Weather Radar Network

2021 ◽  
Vol 13 (15) ◽  
pp. 2936
Author(s):  
Jeong-Eun Lee ◽  
Soohyun Kwon ◽  
Sung-Hwa Jung
Keyword(s):  
Real Time ◽  
Weather Forecasting ◽  
Weather Radar ◽  
Weather Conditions ◽  
Absolute Calibration ◽  
Dual Polarization ◽  
Ground Clutter ◽  
Radar Systems ◽  
Radar Network ◽  
Time Calibration

Monitoring calibration bias in reflectivity (ZH) in an operational S-band dual-polarization weather radar is the primary requisite for monitoring and prediction (nowcasting) of severe weather and routine weather forecasting using a weather radar network. For this purpose, we combined methods based on self-consistency (SC), ground clutter (GC) monitoring, and intercomparison to monitor the ZH in real time by complementing the limitations of each method. The absolute calibration bias can be calculated based on the SC between dual-polarimetric observations. Unfortunately, because SC is valid for rain echoes, it is impossible to monitor reflectivity during the non-precipitation period. GC monitoring is an alternative method for monitoring changes in calibration bias regardless of weather conditions. The statistics of GC ZH near radar depend on the changes in radar system status, such as antenna pointing and calibration bias. The change in GC ZH relative to the baseline was defined as the relative calibration adjustment (RCA). The calibration bias was estimated from the change in RCA, which was similar to that estimated from the SC. The ZH in the overlapping volume of adjacent radars was compared to verify the homogeneity of ZH over the radar network after applying the calibration bias estimated from the SC. The mean bias between two radars was approximately 0.0 dB after correcting calibration bias. We can conclude that the combined method makes it possible to use radar measurements, which are immune to calibration bias, and to diagnose malfunctioning radar systems as soon as possible.

scholarly journals An integrated approach to monitor the calibration stability of operational dual-polarization radars

2016 ◽  
Author(s):  
Mattia Vaccarono ◽  
Renzo Bechini ◽  
Venkatachalam Chandrasekar ◽  
Roberto Cremonini ◽  
Claudio Cassardo
Keyword(s):  
Weather Prediction ◽  
Weather Radar ◽  
Integrated Approach ◽  
The Sun ◽  
Dual Polarization ◽  
Ground Clutter ◽  
Hardware Failures ◽  
Radar Calibration ◽  
The Stability ◽  
Self Consistency

Abstract. The stability of the weather radar calibration is a mandatory aspect for quantitative applications, such as rainfall estimation, short-term weather prediction and initialization of numerical atmospheric and hydrological models. Over the years, calibration monitoring techniques based on external sources have been developed, specifically the calibration using the Sun, and the calibration based on ground clutter returns. In this paper, these two techniques are integrated and complemented with a self-consistency procedure and an intercalibration technique. The aim of the integrated approach is to implement a robust method for online monitoring, able to detect significant changes in the radar calibration. The physical consistency of polarimetric radar observables is exploited using the self-consistency approach, based on the expected correspondence between the dual-polarization power and phase measurements in rain. This technique allows to provide a reference absolute value for the radar calibration, from which eventual deviations may be detected using the other procedures. In particular, the ground clutter calibration is implemented on both polarization channels (horizontal and vertical) and for each radar scan, allowing to monitor the polarimetric variables and promptly recognize hardware failures. The Sun calibration allows to monitor the calibration and sensitivity of the radar receiver, in addition to the antenna pointing accuracy. It is also applied using observations collected during the standard operational scans, but requires longer integration times (several days) in order to accumulate a sufficient amount of data. Finally, an intercalibration technique is developed and performed to compare co-located measurements collected in rain by two radars on overlapping regions. The integrated approach is performed on the C-band weather radar network in northwestern Italy, during July–October 2014. The set of methods considered is shown to provide a robust online tool to monitor the stability of the radar calibration. The attainable accuracy for the calibration of the radar reflectivity is about 1 dB, which is considered adequate for most quantitative applications.

scholarly journals An integrated approach to monitoring the calibration stability of operational dual-polarization radars

2016 ◽  
Vol 9 (11) ◽  
pp. 5367-5383 ◽  
Author(s):  
Mattia Vaccarono ◽  
Renzo Bechini ◽  
Chandra V. Chandrasekar ◽  
Roberto Cremonini ◽  
Claudio Cassardo
Keyword(s):  
Weather Prediction ◽  
Weather Radar ◽  
Integrated Approach ◽  
The Sun ◽  
Dual Polarization ◽  
Ground Clutter ◽  
Hardware Failures ◽  
Radar Calibration ◽  
The Stability ◽  
Self Consistency

Abstract. The stability of weather radar calibration is a mandatory aspect for quantitative applications, such as rainfall estimation, short-term weather prediction and initialization of numerical atmospheric and hydrological models. Over the years, calibration monitoring techniques based on external sources have been developed, specifically calibration using the Sun and calibration based on ground clutter returns. In this paper, these two techniques are integrated and complemented with a self-consistency procedure and an intercalibration technique. The aim of the integrated approach is to implement a robust method for online monitoring, able to detect significant changes in the radar calibration. The physical consistency of polarimetric radar observables is exploited using the self-consistency approach, based on the expected correspondence between dual-polarization power and phase measurements in rain. This technique allows a reference absolute value to be provided for the radar calibration, from which eventual deviations may be detected using the other procedures. In particular, the ground clutter calibration is implemented on both polarization channels (horizontal and vertical) for each radar scan, allowing the polarimetric variables to be monitored and hardware failures to promptly be recognized. The Sun calibration allows monitoring the calibration and sensitivity of the radar receiver, in addition to the antenna pointing accuracy. It is applied using observations collected during the standard operational scans but requires long integration times (several days) in order to accumulate a sufficient amount of useful data. Finally, an intercalibration technique is developed and performed to compare colocated measurements collected in rain by two radars in overlapping regions. The integrated approach is performed on the C-band weather radar network in northwestern Italy, during July–October 2014. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. This is considered adequate to automatically detect any unexpected change in the radar system requiring further data analysis or on-site measurements.

scholarly journals Bayesian Echo Classification for Australian Single-Polarization Weather Radar with Application to Assimilation of Radial Velocity Observations

2015 ◽  
Vol 32 (7) ◽  
pp. 1341-1355 ◽  
Author(s):  
S. J. Rennie ◽  
M. Curtis ◽  
J. Peter ◽  
A. W. Seed ◽  
P. J. Steinle ◽  
...  
Keyword(s):  
Weather Radar ◽  
Training Data ◽  
Training Dataset ◽  
Bayes Classifier ◽  
Threshold Method ◽  
Ground Clutter ◽  
Radar Network ◽  
Routine Monitoring ◽  
Single Polarization ◽  
Anomalous Propagation

AbstractThe Australian Bureau of Meteorology’s operational weather radar network comprises a heterogeneous radar collection covering diverse geography and climate. A naïve Bayes classifier has been developed to identify a range of common echo types observed with these radars. The success of the classifier has been evaluated against its training dataset and by routine monitoring. The training data indicate that more than 90% of precipitation may be identified correctly. The echo types most difficult to distinguish from rainfall are smoke, chaff, and anomalous propagation ground and sea clutter. Their impact depends on their climatological frequency. Small quantities of frequently misclassified persistent echo (like permanent ground clutter or insects) can also cause quality control issues. The Bayes classifier is demonstrated to perform better than a simple threshold method, particularly for reducing misclassification of clutter as precipitation. However, the result depends on finding a balance between excluding precipitation and including erroneous echo. Unlike many single-polarization classifiers that are only intended to extract precipitation echo, the Bayes classifier also discriminates types of nonprecipitation echo. Therefore, the classifier provides the means to utilize clear air echo for applications like data assimilation, and the class information will permit separate data handling of different echo types.

scholarly journals Development of dual-polarization weather radar products display platform

2021 ◽  
Vol 1812 (1) ◽  
pp. 012016
Author(s):  
Qian Zhang ◽  
Peng Zhang ◽  
Yi Zhang ◽  
Yi Zheng ◽  
Haiwen Wei ◽  
...  
Keyword(s):  
Weather Radar ◽  
Dual Polarization

Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar

2021 ◽  
pp. 105852
Author(s):  
M. Montopoli ◽  
E. Picciotti ◽  
L. Baldini ◽  
S. Di Fabio ◽  
F.S. Marzano ◽  
...  
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar ◽  
Dual Polarization
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