Weather Data Quality Control Procedures in the Alberta Agriculture Drought Monitoring Network (AGDMN)

2006 ◽  
Author(s):  
Daniel Itenfisu ◽  
Ralph Wright
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Monitoring Network ◽  
Drought Monitoring ◽  
Weather Data ◽  
Data Quality Control ◽  
Control Procedures

Pycheron: A Python-Based Seismic Waveform Data Quality Control Software Package

2021 ◽  
Author(s):  
Katherine Anderson Aur ◽  
Jessica Bobeck ◽  
Anthony Alberti ◽  
Phillip Kay
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Data Quality ◽  
Monitoring Network ◽  
Downstream Processing ◽  
Data Quality Control ◽  
Data Set ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Wide Range

Abstract Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology’s Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.

Fault detection in a real-time monitoring network for water quality in the lagoon of Venice (Italy)

2004 ◽  
Vol 50 (11) ◽  
pp. 51-58 ◽  
Author(s):  
S. Ciavatta ◽  
R. Pastres ◽  
Z. Lin ◽  
M.B. Beck ◽  
C. Badetti ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Monitoring Network ◽  
Recursive Estimation ◽  
Data Quality Control ◽  
Real Time Monitoring ◽  
Lagoon Of Venice ◽  
On Line

In the context of monitoring water quality in natural ecosystems in real time, on-line data quality control is a very important issue for effective system surveillance and for optimizing maintenance of the monitoring network. This paper presents some applications of recursive state-parameter estimation algorithms to real-time detection of signal drift in high-frequency observations. Two continuous-discrete recursive estimation schemes, namely the Extended Kalman Filter and the Recursive Prediction Error algorithm, were applied to assuring the quality of the dissolved oxygen (DO) time series, as obtained from the Lagoon of Venice (Italy) during August 2002, through the real-time monitoring network of the Magistrato alle Acque (the Venice Water Authority). Results demonstrate the effectiveness of the methodology in early detection of a probable drift in the DO signal. Comparison of these results with those obtained from the application of a related recursive scheme (a Dynamic Linear Regression procedure) suggests the strong benefits of approaching the problem of on-line data quality control with several (not merely a single) independent such estimation methods.

scholarly journals Cloud–edge cooperation for meteorological radar big data: a review of data quality control

2021 ◽  
Author(s):  
Zhichen Hu ◽  
Xiaolong Xu ◽  
Yulan Zhang ◽  
Hongsheng Tang ◽  
Yong Cheng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Rapid Development ◽  
Production Factor ◽  
Weather Data ◽  
Data Quality Control ◽  
Cloud Computing Service ◽  
Convective Weather

AbstractWith the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

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