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Reinhold Steinacker, Dieter Mayer, and Andrea Steiner

1. Introduction A continually increasing number of meteorological observation sites is producing larger and larger amounts of data. Meteorologists can only benefit from this extensive quantity of measurements if the data quality meets the requirements implied by the intended applications. On the one hand, high quality long-term observational data are essential for identifying climate changes or for validating climate model simulations ( Feng et al. 2004 ). On the other hand, quality controlled

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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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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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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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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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Michael M. Bell, Wen-Chau Lee, Cory A. Wolff, and Huaqing Cai

airborne Doppler data contain both weather and nonweather echoes that require editing and quality control (QC) prior to wind synthesis, but interactive QC has been a hindrance for researchers because of the time and training required to properly identify nonweather radar echoes. To date, this interactive editing process has not been systematically documented. The purpose of the current study is to (i) document the characteristics of nonweather echoes in airborne Doppler radar, (ii) design an algorithm

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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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Youcun Qi, Steven Martinaitis, Jian Zhang, and Stephen Cocks

evaporation in rain were attributed an additional 1%–3% increase in undercatch. The use of proper correction software was also shown to improve the accuracy of gauge measurements ( Lanza et al. 2005 ). Steiner et al. (1999) demonstrated how non-quality-controlled gauge data could substantially affect the statistical validation of radar-based rainfall estimations; thus, a comprehensive gauge quality-control (QC) procedure is necessary. Some previous studies addressed gauge errors through intercomparisons

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Altug Aksoy, Joseph J. Cione, Brittany A. Dahl, and Paul D. Reasor

. There may also be an assumption that existing online quality control (QC) methods will continue to be suitable if incoming data are already of good quality. Online QC here is defined as a QC procedure that is applied in addition to the traditional QC step, which occurs during the preprocessing of observations, and takes advantage of the knowledge of model background information during assimilation, hence the term “online.” Indeed, online QC methods have been proposed and implemented since the

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