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

sites), Newfoundland (48) and Labrador (16), Nova Scotia (66), Prince Edward Island (19), and Quebec (154). The data have been gathered from HLY01 (hourly weather) and HLY15 (wind) ASCII individual files. These sites have been, to various degrees, previously quality controlled in both real-time and delayed mode by Environment Canada ( MSC 2013 ). The files were acquired in subsequent batches in May 2008, February 2009, and March 2009. The series span from 1 January 1953, with 44 sites available, to

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Valliappa Lakshmanan, Christopher Karstens, John Krause, and Lin Tang

al. 2013 ). Although the calibration issues of dual-polarization radar measurements ( Ryzhkov et al. 2005 ) could be addressed case by case in research studies, an automated real-time algorithm needs to be tolerant of shortcomings in calibration across a radar network. Therefore, there is a need for a fully automated quality control application that is capable of censoring weather radar data in real time fully utilizing the polarimetric moments. Techniques such as a fuzzy logic approach (e

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Lin Tang, Jian Zhang, Micheal Simpson, Ami Arthur, Heather Grams, Yadong Wang, and Carrie Langston

-dimensional coverage of weather systems. As a result, radar data are essential and crucial for the MRMS product generation. The radar data quality control (QC) plays a critical role in assuring high-quality MRMS products. MRMS radar data QC contains two major components: the dual-polarization radar QC developed by Tang et al. (2014) (hereafter “dpQC”), and the single-polarization radar QC developed by Zhang et al. (2004) and Lakshmanan et al. (2012) . The former was applied to the U.S. Weather Surveillance

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Qin Xu, Kang Nai, Li Wei, Pengfei Zhang, Shun Liu, and David Parrish

. At the lowest few elevations, however, a uniform PRT is required for effective ground clutter filtering. For this reason, and to mitigate the range folding, the phase-coding technique ( Frush et al. 2002 ) has been recently implemented on the uniform PRT. Because of this, radar velocity aliasing will remain a serious problem for low-elevation scans (because of the use of uniform PRT). Radar data quality control is critical for radar data assimilation, and dealiasing is an important and yet often

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Hui Liu, Ying-Hwa Kuo, Sergey Sokolovskiy, Xiaolei Zou, Zhen Zeng, Ling-Feng Hsiao, and Benjamin C. Ruston

experiment did not implement all possible quality controls of RO data. The LSW is a new variable available in the latest versions of the CDAAC Level 1b data product. It is named “Bend_ang_stdv” in the atmPrf files. Another possible application of the LSW is to use it to adjust RO observation errors in RO data assimilation. This approach is being investigated, and the results will be reported in a follow-on paper. Acknowledgments This research was jointly supported by National Science Foundation (NSF

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Wenjing Jia, Dong Wang, Nadia Pinardi, Simona Simoncelli, Andrea Storto, and Simona Masina

different World Ocean regions ( Oka 2005 ; Wong et al. 2003 ; Böhme and Send 2005 ; Owens and Wong 2009 ), and data transmission errors were documented ( Boyer et al. 2013 ). Data quality control (QC) is thus necessary to be used to abate or solve most of the problems. For example, Barker et al. (2011) detected and analyzed the pressure drift; salinity troubles have been calibrated by Wong et al. (2003) for the open tropical and subtropical oceans, Böhme and Send (2005) for the polar regions

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Xudong Liang, Yanxin Xie, Jinfang Yin, Yi Luo, Dan Yao, and Feng Li

Chinese Meteorological Administration are also used. There are 143 available radars at 0800 UTC 23 June. The data were preprocessed by the quality-control procedures of gross error checking (velocity larger than Nyquist speed is omitted), isolated point (the point whose surrounding points are without valid value) removing and ground clutter removing. The near-zero velocities (less than 1.0 m s −1 ) were also removed. c. Test of the two-step dealiasing procedure In this experiment, the radial

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Yukiharu Hisaki

regularization constraint for the dual radar system. However, it is not feasible for the present method because of the large number of unknowns to be estimated. The radio wave and ocean wave conditions of the analyzed data are not good in the present study; for example, the signal-to-noise ratios (SNRs) of Doppler spectra are low, as described in section 4b . In addition, the wave heights are low, and the temporal variability of waves is low ( section 4c ). Under these conditions, the quality control of the

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Stephanie Guinehut, Christine Coatanoan, Anne-Lise Dhomps, Pierre-Yves Le Traon, and Gilles Larnicol

datasets for such studies since they are only subject to simple automated quality checks. A recent example showed that some signals have been misinterpreted as climate signals while they were due to errors in the Argo datasets ( Lyman et al. 2006 ; Willis et al. 2007 , 2009 ). The best Argo quality data for climate research applications are only available in delayed mode, but to date, only half of the profiles older than one year have been delayed-mode controlled. Delayed-mode Argo quality controlled

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Stefan Sperka and Reinhold Steinacker

1. Introduction Since more long-term observations are available every year, quality control and bias correction are becoming increasingly important. On the one hand, long observation time series are used to detect climate changes; on the other hand, they can be used to validate climate model simulations ( Feng et al. 2004 ). Therefore, robust and easy applicable techniques to control and correct meteorological observations have to be developed to meet the standards for data suggested by the

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