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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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Sebastián M. Torres and Christopher D. Curtis

signal processing ( Ivić et al. 2003 ; Torres and Ivić 2005 ). More recently, a real-time implementation was employed on the National Weather Radar Testbed phased-array radar (NWRT PAR) that resulted in faster scan times (by a factor of 2) with no significant loss in data quality ( Curtis and Torres 2011 ). As range-oversampling processing is feasible on modern digital receivers and signal processing architectures, it becomes increasingly important to understand what calibration measurements are

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Edouard Goudenhoofdt and Laurent Delobbe

correlation in most cases. 1) Rain gauge measurements The hydrological service of the Walloon region (SPW) operates a dense (one gauge per 135 km 2 ) and integrated network of 90 telemetric rain gauges ( Fig. 2 ). Most of them are tipping-bucket systems providing hourly rainfall accumulation. The collected data are used for hydrological modeling and directly sent to RMIB. The rain gauges are controlled on site every 3 months and in a specialized workshop every year. Every day, a quality control of the

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Sebastián M. Torres and David A. Warde

CLEAN-AP is the autocorrelation spectral density (ASD) introduced by Warde and Torres (2014) : an extension of the classical power spectral density (PSD) that includes spectral phase information. CLEAN-AP combines the use of adaptive data windows and the argument of the lag-1 ASD to provide a suitable compromise between clutter suppression and the quality of the meteorological variables obtained after filtering. In this paper, simulations and real data recorded from two operational WSR-88D radars

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Guang-Yu Shi, Tadahiro Hayasaka, Atsumu Ohmura, Zhi-Hua Chen, Biao Wang, Jian-Qi Zhao, Hui-Zheng Che, and Li Xu

, and S. Xu , 1998 : The description of Chinese radiation data and their quality control procedures (in Chinese). Meteor. Sci. , 2 , 53 – 56 . Maxwell , E. , S. Wilcox , and M. Rymes , 1993 : User’s manual for SERI QC software—Assessing the quality of solar radiation data. National Renewable Energy Laboratory Rep. NREL-TP-463-5608, NREL, 1617 pp . Muneer , T. , 2004 : Solar Radiation and Daylight Models . Elsevier, 392 pp . Muneer , T. , and M. S. Gul , 2000 : Evaluation

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Jesse E. Bell, Michael A. Palecki, C. Bruce Baker, William G. Collins, Jay H. Lawrimore, Ronald D. Leeper, Mark E. Hall, John Kochendorfer, Tilden P. Meyers, Tim Wilson, and Howard J. Diamond

observations, which include solar radiation, surface IR temperature, 1.5-m wind speed, and a wetness detector. This article provides a brief review of the nature of soil-climate observations in the United States and the role that USCRN will play in soil-climate monitoring. The quality control of USCRN soil moisture and temperature data will also be discussed, including issues of network maintenance. Applications of the USCRN soil moisture and temperature to science and operations will be presented, with

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Erik Nilsson, Hans Bergström, Anna Rutgersson, Eva Podgrajsek, Marcus B. Wallin, Gunnar Bergström, Ebba Dellwik, Sebastian Landwehr, and Brian Ward

temperature was close to normal (9.6°C compared to 10.2°C as a mean value for the period 1961–1990) and there were much more precipitation, about 85 mm—almost 3 times more than normal. These different conditions are mentioned here because data from these two months were used to determine a reasonable selection criteria in our quality control of CO 2 fluxes. In June 2015 we had 16 days with precipitation, but only 1 day with more than 10 mm. The monthly precipitation was only marginally less (1 mm) than

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Gregory P. Gerbi, Emmanuel Boss, P. Jeremy Werdell, Christopher W. Proctor, Nils Haëntjens, Marlon R. Lewis, Keith Brown, Diego Sorrentino, J. Ronald V. Zaneveld, Andrew H. Barnard, John Koegler, Hugh Fargher, Matthew DeDonato, and William Wallace

thresholds defined by Bailey and Werdell, which included testing for extreme variation between pixels, excessive solar zenith or satellite zenith angles, minimum numbers of unflagged pixels, and if multiple satellite files matched the in situ data, only the closest in time was used. g. Statistics for analysis and quality control Because this study is effectively validating the float observations, we take the satellite observations as the “truth.” Our primary metric is the ratio of float to satellite

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Denny P. Alappattu and Qing Wang

correct the depth error. Such depth bias does not exist in the processed AXBT profiles. This depth bias in the AXCTD measurements produces misleading results, especially when the ocean measurements come from a mixture of both AXBT and AXCTD probes. An important step in quality control of the AXCTD data is thus to remove the bias using an objective and consistent method, which is the focus of this work. In addition, since AXBT and AXCTD data are used together, a comparison of the temperature

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Michael Frech, Martin Hagen, and Theo Mammen

central unit in Offenbach, Germany, where the central processing suite Polarimetric Radar Algorithms (POLARA) is hosted ( Tracksdorf et al. 2013 ; Frech and Steinert 2015 ). Typically, 36 radar moments are transmitted with every sweep. In POLARA, quality-controlled data are used to compute quantitative precipitation estimates (QPE) and hydrometeor classification (HMC) products for all radar sites. Single-site products and composites are then visualized in the Ninjo display system. The scan theorem

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