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Anne Ru Cheng, Tim Hau Lee, Hsin I. Ku, and Yi Wen Chen

, can occasionally be seen and are a result of mistakes made during the operation of the instruments, the inaccuracies of the instruments, the failure of data transmission, or possibly interference from human activity. It is thus desirable for the data management center to build a quality control (QC) system to ensure that the incoming data are accurate before any further analysis is taken. The temperature QC issues have been discussed in many articles. For the short-term and/or instantaneous

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Zhen Zeng and X. Zou

Corporation for Atmospheric Research’s (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archival Center (CDAAC; version 002) for this study. A total of 4884 RO profiles, taken before CDAAC quality control (QC), are available during this month and are used as input into a PCA QC procedure. The optimized bending angle and refractivity ( Kuo et al. 2004 ) from these 4884 RO profiles from the surface to 40-km height are investigated. The original

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Jinfeng Ding, Xiao-Yong Zhuge, Yuan Wang, and Anyuan Xiong

the AMDAR system varies with time and flight phase—that is, 6 s in the first 60 s and then 35 s during the ascending phase, 180 s while in the cruise phase, and 60 s during the descending phase. These original reports are decoded and quality controlled by China’s meteorological information center. The quality control scheme is properly adjusted from the Meteorological Assimilation Data Ingest System (MADIS) automated aircraft reports quality control scheme to a Chinese version ( Tao et al. 2009

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Guo-Yuan Lien, Takemasa Miyoshi, and Eugenia Kalnay

experiments shown in this study. We think that the impact is small because this correlation-based quality control criterion may overlap with other quality control criteria, such as the 24mR criterion; that is, the precipitation data at those very bad areas could be already rejected by other criteria, so the impact is not large. 4. Results a. Global analysis and forecast errors Figure 2 shows the evolution of the global analysis RMS errors (RMSEs) of the 500-hPa u wind verified against the ERA

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Tian-You Yu, Marko B. Orescanin, Christopher D. Curtis, Dusan S. Zrnić, and Douglas E. Forsyth

novel approach to address these fundamental limitations is presented using a phased-array weather radar (PAR), which can achieve the goals of rapid scanning and high data quality. In estimation theory, the statistical error (i.e., the standard deviation) of an estimator can be reduced by averaging a number of independent measurements ( Papoulis and Pillai 2002 ). Radar measurements from adjacent ranges and radials can be averaged to improve the quality of the estimate at the expense of resolution

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Kenneth E. Kunkel, Thomas R. Karl, and David R. Easterling

.S. precipitation data are now underway as nineteenth century data from stations operated prior to the COOP are in the process of being digitized and quality controlled. Such data are of great interest because even a 110-yr record is relatively short when evaluating multidecadal variations. Furthermore, there is evidence of very wet conditions during the nineteenth century in the central United States, including very high levels of Lakes Michigan–Huron ( Changnon 2004 ) and high streamflows on the upper

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Brian Emery and Libe Washburn

properties of the MUSIC direction of arrival (DOA) function have been shown to improve comparison results when used for thresholding ( Kirincich et al. 2012 ). These improvements are significant, particularly as quality control metrics, but they fall short of providing an uncertainty estimate with each velocity observation. Outside of oceanography MUSIC has been thoroughly studied and several publications derive analytical expressions for DOA error. Stoica and Nehorai (1989) derived the DOA

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Max Yaremchuk, Dmitri Nechaev, and Gleb Panteleev

states for generation of the reduced control by singular value decomposition. Another strategy studied by ( Cao et al. 2007 ; Daescu and Navon 2007 ) is based on the reduction of the model itself using EOF approach. Although the latter technique improves computational efficiency, the issue of finding an optimal low-dimensional state subspace remains an open question. This paper presents a version of the reduced control space 4DVAR data assimilation method. In contrast to previous studies (e

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Tse-Chun Chen and Eugenia Kalnay

understanding of the complex DA systems ( Zhu and Gelaro 2008 ; Cardinali 2009 ; Gelaro et al. 2010 ; Lorenc and Marriott 2014 ; Ota et al. 2013 ; Sommer and Weissmann 2014 ). Several studies have investigated the application of (generic) FSO impacts. Lien et al. (2018) demonstrated with an example of precipitation assimilation that using long-term averaged noncycled EFSO impact as guidance can accelerate the development of data selection and quality control procedures for new observing systems

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Stéphane Laroche, Pierre Gauthier, Monique Tanguay, Simon Pellerin, and Josée Morneau

orders of magnitude, which is usually achieved within 90 inner loops. A 6-h assimilation window, centered at the synoptic time is used for the satellite radiances. For the other data types, the assimilation window is restricted to 3 h, because the time inconsistency between the background and observation beyond 90 min from the synoptic time may introduce large errors in the innovation vector. This is especially true for wind data. After making the quality control of observations, satellite and

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