scholarly journals Clutter Elimination Algorithm for Non-Precipitation Echo of Radar Data Considering Meteorological and Observational Properties in Polarimetric Measurements

2020 ◽  
Vol 12 (22) ◽  
pp. 3790
Author(s):  
Young-A Oh ◽  
Hae-Lim Kim ◽  
Mi-Kyung Suk
Keyword(s):  
Quality Control ◽  
Control Algorithm ◽  
Radar Data ◽  
Biological Targets ◽  
Elimination Algorithm ◽  
Radar Networks ◽  
Radar Echo ◽  
Improved Performance ◽  
Noise Correction ◽  
Anomalous Propagation

Non-precipitation echoes due to ground and sea clutter, chaff, anomalous propagation, biological targets, and interference in weather radar observations are major issues causing a decline in the accuracy of meteorological and hydrological applications based on radar data. Statistically based quality control techniques using polarimetric variables have improved the accuracy of radar echo classification, however their performance is affected by attenuation, nonuniform beam filling, and hydrometeor diversity as well as terrain blockage, beam broadening, and noise correction issues due to the quality degradation of polarimetric measurements. To address this, a new quality control algorithm, named clutter elimination algorithm for non-precipitation echo of radar data (CLEANER), was designed by employing independent feature parameters and variable classification conditions with spatial and temporal observation environments to adapt to these meteorological artifacts and observational limitations. CLEANER was applied to several precipitation cases with various non-precipitation echoes, showing improved performance compared with results from the fuzzy logic-based quality control algorithm in terms of non-precipitation echo removal as well as in precipitation echo conservation. In addition, CLEANER shows better computational efficiency and robustness, as well as an excellent expandability for different radar networks.

Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?

2015 ◽  
Vol 32 (6) ◽  
pp. 1209-1223 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Christopher Karstens ◽  
John Krause ◽  
Kim Elmore ◽  
Alexander Ryzhkov ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Control Algorithm ◽  
Radar Data ◽  
Errors In Variables ◽  
Data Quality Control ◽  
Reference Dataset ◽  
Ground Clutter ◽  
Statistical Framework ◽  
The Impact

AbstractRecently, a radar data quality control algorithm has been devised to discriminate between weather echoes and echoes due to nonmeteorological phenomena, such as bioscatter, instrument artifacts, and ground clutter (Lakshmanan et al.), using the values of polarimetric moments at and around a range gate. Because the algorithm was created by optimizing its weights over a large reference dataset, statistical methods can be employed to examine the importance of the different variables in the context of discriminating between weather and no-weather echoes. Among the variables studied for their impact on the ability to identify and censor nonmeteorological artifacts from weather radar data, the method of successive permutations ranks the variance of Zdr, the reflectivity structure of the virtual volume scan, and the range derivative of the differential phase on propagation [PhiDP (Kdp)] as the most important. The same statistical framework can be used to study the impact of calibration errors in variables such as Zdr. The effects of Zdr calibration errors were found to be negligible.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

scholarly journals Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Daniel Michelson ◽  
Bjarne Hansen ◽  
Dominik Jacques ◽  
François Lemay ◽  
Peter Rodriguez
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Weather Radar ◽  
Radar Data ◽  
Data Quality Control ◽  
Continental Scale

Automated quality control of High Frequency radar data

2012 Oceans ◽  
2012 ◽  
Author(s):  
H. Roarty ◽  
M. Smith ◽  
J. Kerfoot ◽  
J. Kohut ◽  
S. Glenn
Keyword(s):  
Quality Control ◽  
High Frequency ◽  
Radar Data ◽  
High Frequency Radar ◽  
Automated Quality Control

scholarly journals Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1166
Author(s):  
Hsin-Hung Lin ◽  
Chih-Chien Tsai ◽  
Jia-Chyi Liou ◽  
Yu-Chun Chen ◽  
Chung-Yi Lin ◽  
...  
Keyword(s):  
Numerical Model ◽  
Model Prediction ◽  
Radar Data ◽  
False Alarms ◽  
Rainfall Events ◽  
System A ◽  
Radar Echo ◽  
Grid Points ◽  
Mean Square Errors ◽  
Heavy Rainfall Events

This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accurate prediction of both strengthening and weakening processes. The statistical skill for all the sampled events shows that the nowcasts by ExAMP and the extrapolation system obtain the lowest and second lowest root mean square errors at all the lead time, respectively. In terms of threat scores and bias scores above certain reflectivity thresholds, the ExAMP nowcast may have more grid points of misses for high reflectivity in comparison to extrapolation, but serious overestimation among the points of hits and false alarms is the least likely to happen with the new scheme. Moreover, the event type does not change the performance ranking of the five methods, all of which have the highest predictability for a typhoon event and the lowest for local thunderstorm events.

scholarly journals Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

2020 ◽  
Vol 13 (2) ◽  
pp. 537-551
Author(s):  
Shuai Zhang ◽  
Xingyou Huang ◽  
Jinzhong Min ◽  
Zhigang Chu ◽  
Xiaoran Zhuang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Weak Signal ◽  
Post Processing ◽  
Algorithm Performance ◽  
Radar Echoes ◽  
Fuzzy Logic Method ◽  
Anomalous Propagation ◽  
Logic Method

Abstract. To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions.

associations between aircraft measurements of turbulence and weather radar measurements1

1965 ◽  
Vol 46 (8) ◽  
pp. 443-447 ◽  
Author(s):  
Edwin Kessler ◽  
Jean T. Lee ◽  
Kenneth E. Wilk
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Time Dependent ◽  
Aircraft Measurements ◽  
Radar Echoes ◽  
Radar Echo

Aircraft have been guided with the aid of radar data to measure turbulence in thunderstorm areas. Although turbulence is frequently encountered in areas containing highly reflective and sharp-edged echoes, no unique correspondence has been discovered between single-echo parameters and collocated within-storm turbulence. A theory embracing some of the time-dependent relationships between fields of wind and precipitation suggests that the correspondence between instantaneous distributions of radar echoes and turbulence is statistical rather than precise. Statistical bases for study of radar echo-turbulence relationships are outlined.

scholarly journals TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments

2020 ◽  
Vol 36 (16) ◽  
pp. 4406-4414 ◽  
Author(s):  
Lifan Chen ◽  
Xiaoqin Tan ◽  
Dingyan Wang ◽  
Feisheng Zhong ◽  
Xiaohong Liu ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Three Dimensional ◽  
Prediction Performance ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Biological Targets ◽  
Interaction Prediction ◽  
Protein Interaction Prediction ◽  
Improved Performance ◽  
Ligand Bias

Abstract Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance. Results To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization. Availability and implementation https://github.com/lifanchen-simm/transformerCPI.

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