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Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method

Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Qualit...

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Detalles Bibliográficos
Autores principales: Urraca, Ruben, Antonanzas, Javier, Sanz-Garcia, Andres, Martinez-de-Pison, Francisco Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603785/
https://www.ncbi.nlm.nih.gov/pubmed/31151288
http://dx.doi.org/10.3390/s19112483
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author Urraca, Ruben
Antonanzas, Javier
Sanz-Garcia, Andres
Martinez-de-Pison, Francisco Javier
author_facet Urraca, Ruben
Antonanzas, Javier
Sanz-Garcia, Andres
Martinez-de-Pison, Francisco Javier
author_sort Urraca, Ruben
collection PubMed
description Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations.
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spelling pubmed-66037852019-07-17 Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method Urraca, Ruben Antonanzas, Javier Sanz-Garcia, Andres Martinez-de-Pison, Francisco Javier Sensors (Basel) Article Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations. MDPI 2019-05-30 /pmc/articles/PMC6603785/ /pubmed/31151288 http://dx.doi.org/10.3390/s19112483 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Urraca, Ruben
Antonanzas, Javier
Sanz-Garcia, Andres
Martinez-de-Pison, Francisco Javier
Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
title Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
title_full Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
title_fullStr Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
title_full_unstemmed Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
title_short Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
title_sort analysis of spanish radiometric networks with the novel bias-based quality control (bqc) method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603785/
https://www.ncbi.nlm.nih.gov/pubmed/31151288
http://dx.doi.org/10.3390/s19112483
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