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Andrea N. Grant, Stefan Brönnimann, Tracy Ewen, and Andrey Nagurny

back to 1948 but in a number of cases were forced to discard the pre-IGY data. Up to now, the pre-IGY data have never even been systematically compiled. The data are scattered among numerous archives, cataloged via multiple station identifier schemes, and have been subjected to different quality control and data culling procedures. It was our hypothesis that some of the discarded earlier data may be usable after quality assessment and correction; therefore, we attempted to compile a comprehensive

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J. Fidel González-Rouco, J. Luis Jiménez, Vicente Quesada, and Francisco Valero

were identified as those values trespassing a maximum threshold for each time series ( Trenberth and Paolino 1980 ; Peterson et al. 1998a ) defined by P out = q 0.75 + 3IQR, (1) where q 0.75 is the third quartile and IQR the interquartilic range. The IQR has been used in quality control of climate data ( Eischeid et al. 1995 ) because it is resistant to outliers. Values over P out were substituted by this limit. This way of proceeding reduces the bias caused by outliers and yet keeps the

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Chunlüe Zhou, Junhong Wang, Aiguo Dai, and Peter W. Thorne

) and duplicates (consecutive red dots) were removed. Some data points (green dots) were also excluded in our analysis due to insufficient monthly sampling (see the text for details). Black dots represent subdaily raw temperatures retained in our subsequent analysis. These quality-controlled data were then merged with preference given to IGRA2 to create a comprehensive, global 0000 and 1200 UTC radiosonde temperature dataset at the surface and 16 standard levels, namely 1000, 925, 850, 700, 500, 400

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Shoji Hirahara, Masayoshi Ishii, and Yoshikazu Fukuda

compared with the SST analysis of the recent years based on satellite and in situ data. The subsampled data and their analysis are referred to below as pseudo-observations and pseudoanalysis, respectively. Quality-controlled and merged data used in the pseudoanalysis are subsampled on a daily basis, which are nearest to the date (month and day only) and position of the past data. Duplicate sampling within 15 days is allowed. The number of subsampled observations is almost the same as that of the

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Edmund K. M. Chang

reanalyses is ship observations. Zorita et al. (1992) have examined variability of the standard deviation of MSLP taken from the monthly summary statistics of the International Comprehensive Ocean–Atmosphere Data Set (ICOADS; see Woodruff et al. 1987 ; Worley et al. 2005 ). Chang (2005 , hereafter C05 ) examined MSLP observations over the central North Pacific contained in the ICOADS and found that, because of changes in the frequency and quality of observations, there may be time-dependent biases

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Tianbao Zhao, Weidong Guo, and Congbin Fu

approximately 1.875° × 1.875° (192 × 94 Gaussian grid points) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Simmons and Gibson 2000 ; Uppala et al. 2005 ) on a 2.5° × 2.5° latitude–longitude grid. A daily temperature dataset of 597 stations in mainland China over the same period is utilized for comparison and calibration. A quality control procedure by the National Meteorological Center of the China Meteorological Administration (CMA) was applied to Chinese

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Kaicun Wang, Robert E. Dickinson, Qian Ma, John A. Augustine, and Martin Wild

black and white detector (Eppley model 8-48; see section 3 for detailed information). To homogenize the data, the diffuse measurements before 2001 have been corrected using a method similar to Dutton et al. (2001) . We only used data from the ARM and ISIS sites after 2002 (see Table 1 ), when all daytime diffuse measurements were made with an Eppley 8-48. The surface incident solar radiation data at 1- or 3-min temporal resolution were downloaded. For quality control (or to minimize biases

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Elizabeth J. Kendon, Stephen Blenkinsop, and Hayley J. Fowler

gave similar results ( Fowler et al. 2010 ). In this paper, we examine how detection time varies for U.K. precipitation accumulated across a range of time and space scales, including down to 10-min and kilometer scales, using output from a very high-resolution (convection permitting) climate model, and investigate the consistency of modeled detection times, with observed changes from gauge data in a new, quality-controlled dataset of hourly rainfall for the United Kingdom. Convection

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Wenhui Xu, Chenghu Sun, Jingqing Zuo, Zhuguo Ma, Weijing Li, and Song Yang

and surface air temperature (SAT) in northern China in winter since 2005. The observation dataset they used had been subjected to initial quality control by the China Meteorological Administration (CMA). However, no homogeneity check and correction was performed. Moreover, lack of reliable observational GST data makes assessing model simulations difficult. Considering the important role of GST in land–atmosphere interaction, the quality of reanalysis products of the GST in China was assessed in

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Fabienne Gaillard, Thierry Reynaud, Virginie Thierry, Nicolas Kolodziejczyk, and Karina von Schuckmann

time evolution of these essential ocean variables. ISAS space and time resolution have been adapted to the Argo array of profiling floats. The main goal is to perform climatic studies and extract meaningful indices; for that reason, the quality control of the data has been a strong concern since the beginning of the processing. To complement the Argo network, ISAS integrates any type of in situ observation from individual profiles to fixed-point time series. a. Optimal interpolation The temperature

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