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  5. A quality control procedure for climatological studies using Argo data in the North Pacific Western Boundary Current region
 
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A quality control procedure for climatological studies using Argo data in the North Pacific Western Boundary Current region

Author(s)
Wenjing, Jia  
Dong, Wang  
Pinardi, Nadia  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia  
Simoncelli, Simona  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia  
Storto, Andrea  
Masina, Simona  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia  
Language
English
Obiettivo Specifico
4A. Oceanografia e clima
Status
Published
JCR Journal
N/A or not JCR
Peer review journal
Yes
Journal
Journal of Atmospheric and Oceanic Technology  
Issue/vol(year)
/33 (2016)
Pages (printed)
2717–2733
Date Issued
December 2016
DOI
10.1175/JTECH-D-15-0140.1
URI
https://www.earth-prints.org/handle/2122/11615
Subjects
Data Analysis
Data Quality
climatology
Abstract
A quality control (QC) procedure is developed to estimate monthly mean climatologies from the large Argo dataset (2005–12) over the North Pacific western boundary current region. In addition to the individual QC procedure, which checks for instrumental, transmission, and gross errors, the paper describes and shows the impact of climatological checks (collective QC) on the quality of both processed profiles and resultant climatological distributions. Objective analysis (OA) is applied progressively to produce the gridded climatological fields. The method uses horizontal regional climatological averages defined in five regime-oriented subregions in the Kuroshio area and the Japan Sea. Performing the QC procedure on specific coherent subregions produces improved profiling data and climatological fields because more details about the local hydrodynamics are taken into consideration. Nonrepresentative data and random noises are more effectively rejected by this method, which has value both in defining a climatological mean and identifying outlier data. Assessing with both profiling and coordinated datasets, the agreement is reasonably good (particularly for those areas with abundant observations), but the results (although already smoothed) can capture more detailed or mesoscale features for further regional studies. The method described has the potential to meet future challenges in processing accumulating Argo observations in the coming decades.
Type
article
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