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Jinsheng You, Kenneth G. Hubbard, Saralees Nadarajah, and Kenneth E. Kunkel

1. Introduction The objective of this study is to develop automated quality control (QC) tools for precipitation, based on the empirical statistical distributions underlying the observations. This paper explores threshold quantifying methods to identify a subset of data consisting of potential outliers in the precipitation observations with the aim of reducing the manual checking workload. Previous studies have documented various QC tools for use with weather data ( Wade 1987 ; Gandin 1988

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Christopher A. Fiebrich, Cynthia R. Morgan, Alexandria G. McCombs, Peter K. Hall Jr., and Renee A. McPherson

), some inaccuracies may be unavoidable (e.g., a rotating anemometer coated in ice or a pyranometer packed with snow; Tanner 2001 ). Gandin (1988) described the particularly complicated challenge of detecting errors in meteorological data, because of their variability in both space and time. Olson (2003) advised that in order to control data accuracy, it is important to control it at many different stages. Numerous network managers have recognized that an end-to-end quality assurance system (e

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Kenneth G. Hubbard, Nathaniel B. Guttman, Jinsheng You, and Zhirong Chen

data is scant. General testing approaches such as using threshold and step change criteria have been designed for the single station review of data to detect potential outliers ( Wade 1987 ; Reek et al. 1992 ; Meek and Hatfield 1994 ; Eischeid et al. 1995 ). Recently, the use of multiple stations in quality assurance procedures has proven to provide valuable information for quality control (QC) compared with the single-station checking. Spatial tests compare a station’s data against the data

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Sung Yong Kim

enhanced awareness of building and sustaining regional coastal ocean observing programs (e.g., Malone and Cole 2000 ; Ocean.US 2002 ; Stokstad 2006 ). In this paper, detailed and technical descriptions of HFR data analysis are presented in terms of the quality assurance and quality control (QAQC) of radial velocity data based on the expected geophysical signals and dynamic relationships between driving forces and responses. This work will be beneficial and instructive not only for HFR operators and

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T. W. Ocheltree and H. W. Loescher

that may contribute to the net exchange of scalars ( Loescher et al. 2006 ). To reduce and quantify these uncertainties, the AmeriFlux quality assurance and quality control (QAQC) laboratory was created to enhance data quality and ensure consistency in EC measurements within and among sites. The primary activities of the AmeriFlux QAQC laboratory involve the use of a portable eddy covariance system (PECS, discussed below). This system includes all necessary hardware and software to make EC

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Imke Durre, Matthew J. Menne, and Russell S. Vose

1. Introduction Users of meteorological data may legitimately ask, “To what extent have quality assurance (QA) procedures removed significant errors from the dataset, and at what cost?” In other words, users need to know what types of errors remain in a dataset and whether the QA procedures have inadvertently removed true climate extremes. Ideally, this information would be provided via a thorough evaluation of the type-I and type-II errors (i.e., the degree to which the QA process identified

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Cheng-Dong Xu, Jin-Feng Wang, Mao-Gui Hu, and Qing-Xiang Li

: 10.1007/s00376-008-0157-7 . You , J. S. , and K. G. Hubbard , 2006 : Quality control of weather data during extreme events . J. Atmos. Oceanic Technol. , 23 , 184 – 197 , doi: 10.1175/JTECH1851.1 . You , J. S. , S. Nadarajah , and K. E. Kunkel , 2007 : Performance of quality assurance procedures on daily precipitation . J. Atmos. Oceanic Technol. , 24 , 821 – 834 , doi: 10.1175/JTECH2002.1 . You , J. S. , K. G. Hubbard , and S. Goddard , 2008 : Comparison of methods

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Hui Wan, Xiaolan L. Wang, and Val R. Swail

significant decline in annual mean and winter mean SLP over the Arctic. However, the original records of surface atmospheric pressure are hourly measurements, from which the commonly used monthly or daily mean pressure values are derived. Unfortunately, the hourly pressure data archived in Environment Canada (EC) have not undergone a quality control (QC) or quality assurance (QA) procedure (except at times for which missing data are flagged). Slonosky and Graham (2005) corrected some problems in their

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Simone Cosoli, Giorgio Bolzon, and Andrea Mazzoldi

oceanographic applications, there is no agreement for quality assurance and quality control (QA–QC) procedures. Protocols for quality control and quality assurance of remotely sensed currents are neither well established nor standardized because of the different level of manipulation that radar data typically undergo (radial currents; surface current maps, merged from radial maps; wave data; particle tracks). The majority of near-real-time QA–QC tests on radial currents for direction-finding (DF) systems

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Etor E. Lucio-Eceiza, J. Fidel González-Rouco, Jorge Navarro, Hugo Beltrami, and Jorge Conte

1. Introduction Performing meteorological measurements, data storage, and management is a delicate process that is never exempt of errors, despite the efforts and care invested in the task. For any meaningful use of these meteorological data, it is important to ensure, as much as possible, the validity of observations. The procedures used for this purpose constitute the so-called quality control (QC; e.g., Wade 1987 ; Gandin 1988 ; DeGaetano 1997 ; Shafer et al. 2000 ; Fiebrich et al. 2010

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