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.

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet
Sign in / Sign up

Export Citation Format

Share Document