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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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Laura Bianco, Daniel Gottas, and James M. Wilczak

1. Introduction The implementation of real-time data quality control is of fundamental importance for observations that are assimilated into operational numerical weather prediction models. One of the most vexing quality-control problems affecting radar wind profilers has been signal contamination from nocturnally migrating birds ( Wilczak et al. 1995 ). Although techniques have been developed that helped reduce the level of contamination ( Wilczak et al. 1995 ; Merritt 1995 ), these were

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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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Yu Zhang, Min Chen, and Jiqin Zhong

assimilation is still unclear, and there is no quality control procedure tailored to data assimilation yet. In variational data assimilation, quality control is one of the most important components for removing bad observations and ensuring validation of data assimilation. There are several sources of error in meteorological observations, including instrumental errors, representative errors, and gross errors caused by instrumental and telecommunication failures ( Xu et al. 2013 ). Data assimilation systems

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Rütger Rollenbeck, Katja Trachte, and Jörg Bendix

. (2003) , Durre et al. (2010) , Aguilar et al. (2003) , and WMO (2011) . Those standard quality controls of climate data, like checks for constant limits and temporal gradients checks ( Vejen et al. 2002 ; Aguilar et al. 2003 ; Durre et al. 2010 ), are adjusted to the requirements of the instrumental and geographical settings of the research sites. This adjustment can be made by using knowledge about climate conditions at the considered sites or by reiterating the processing chain with limits

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G. Kelly, E. Andersson, A. Hollingsworth, P. Lönnberg, J. Pailleux, and Z. Zhang

serious errors and biases in the operational temperature and moisture satellite retrievalsproduced by statistical methods. We show ttmt similar errors and biases are found in the physical re~evalsproduced operationally since September 1988. We report experiments on quality control algorithms to dealwith the errors in the satellite data. The quality control changes resulting from this work were implemented inthe European Centre for Medium R~nge Weather Forecasts (ECMWF) system in January 1989. The

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Zhifang Xu, Yi Wang, and Guangzhou Fan

). Apparently, a range of elements, including terrain, topography, and the placement of observation instruments, have made the quality control of surface observing data even more difficult. A credible quality control process is, therefore, the first step one has to take in a bid to improve surface data assimilation in numerical weather prediction. A complex quality control ( Gandin 1988 ; Collins 1998 , 2001a , b ) is applied to radiosonde height and temperature at the National Centers for Environmental

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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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Thomas Nehrkorn

1. Introduction This paper describes an application of optimum interpolation (OI) for quality control (QC) and combination of wind profiler data with other observations of wind profiles. The method was applied to data collected at the National Aeronautics and Space Administration’s (NASA’s) Kennedy Space Center. The Kennedy Space Center (KSC) maintains a large array of weather observing systems to support its launch and landing operations for the space shuttle and expendable rockets. For

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