Parameter identification for Kalman filtering of noisy and censored data: Is this a data quality control method?

1998 ◽  
Vol 68 (3-4) ◽  
pp. 221-233
Author(s):  
H. -D. Schilling ◽  
H. Steinhorst
Keyword(s):  
Quality Control ◽  
Parameter Identification ◽  
Data Quality ◽  
Censored Data ◽  
Kalman Filtering ◽  
Control Method ◽  
Data Quality Control ◽  
Quality Control Method

Data quality control method for VAD wind field retrieval based on coherent wind lidar

2018 ◽  
Vol 47 (2) ◽  
pp. 230002
Author(s):  
王贵宁 Wang Guining ◽  
刘秉义 Liu Bingyi ◽  
冯长中 Feng Changzhong ◽  
吴松华 Wu Songhua ◽  
刘金涛 Liu Jintao ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Wind Field ◽  
Control Method ◽  
Data Quality Control ◽  
Quality Control Method ◽  
Wind Lidar

scholarly journals Effects of undetected data quality issues on climatological analyses

2017 ◽  
Author(s):  
Stefan Hunziker ◽  
Stefan Brönnimann ◽  
Juan Marcos Calle ◽  
Isabel Moreno ◽  
Marcos Andrade ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Control Method ◽  
Correlation Coefficients ◽  
Maximum Temperature ◽  
Generation Process ◽  
Data Generation ◽  
Data Quality Control ◽  
Standard Data ◽  
Quality Issues

Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature, and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control method strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets, and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.

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
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