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Imke Durre, Matthew J. Menne, Byron E. Gleason, Tamara G. Houston, and Russell S. Vose

National Technical Information Service, 5285 Port Royal Rd., Springfield, VA 22161, and online at http://www.ornl.gov/~webworks/cppr/y2002/rpt/101454.pdf ] . Eischeid , J. K. , C. B. Baker , T. Karl , and H. F. Diaz , 1995 : The quality control of long-term climatological data using objective data analysis. J. Appl. Meteor. , 34 , 2787 – 2795 . Eischeid , J. K. , P. A. Pasteris , H. F. Diaz , M. S. Plantico , and N. J. Lott , 2000 : Creating a serially complete, national

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Christopher A. Hiemstra, Glen E. Liston, Roger A. Pielke Sr., Daniel L. Birkenheuer, and Steven C. Albers

most cases utilizing topographic variation as a controlling factor (e.g., Thornton et al. 1997 ; Liston and Elder 2006b ). At coarser scales, mesoscale data assimilation and forecast systems that incorporate a wide variety of data are available and widely used (see Lazarus et al. 2002 for an overview). However, more information on the strengths and shortcomings of potential meteorological data is desirable before incorporating assimilations into models. The focus of this paper, the National

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Daisuke Hotta, Tse-Chun Chen, Eugenia Kalnay, Yoichiro Ota, and Takemasa Miyoshi

, 2007 : Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter . Physica D , 230 , 112 – 126 , doi: 10.1016/j.physd.2006.11.008 . 10.1016/j.physd.2006.11.008 Ingleby , N. B. , and A. C. Lorenc , 1993 : Bayesian quality control using multivariate normal distribution . Quart. J. Roy. Meteor. Soc. , 119 , 1195 – 1225 , doi: 10.1002/qj.49711951316 . 10.1002/qj.49711951316 Isaksen , L. , M. Fisher , E. Andersson , and J. Barkmeijer , 2005

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Eric A. D'Asaro and Craig McNeil

vessel's crane. (b) SBE-9/11 CTD with rosette was deployed off the fantail and took water samples close to the operating floats. (c) Two packages could be navigated in the vertical to a fraction of 1 m using the vessel's echosounder and were no more than 10 m apart horizontally. The Winkler data were used to generate a calibration point for each of the floats at each of the levels ( Fig. 2 ). These were interpolated by eye to the float level, guided by the profiles from the CTD. This resulted in a

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Yong Hyun Kim, Sungshin Kim, Hye-Young Han, Bok-Haeng Heo, and Cheol-Hwan You

of various types of radar clutters In the past several years, various techniques have been proposed on radar data quality control (QC). Many of these studies were primarily concerned with the detection of NG and AP clutter ( Pratte et al. 1993 ; Pamment and Conway 1998 ; Grecu and Krajewski 1999 ; Kessinger et al. 1999 ; Steiner and Smith 2002 ; Berenguer et al. 2006 ; Cho et al. 2006 ; Lakshmanan et al. 2007 ; Hubbert et al. 2009 ), while others were concerned with the detection of less

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Thomas J. Lockhart

JUNE I989 NOTES AND CORRESPONDENCE 525 NOTES AND CORRESPONDENCEComments on "A Quality Control Program for Surface Mesonetwork Data" THOMAS J. LOCKHART Meteorological Standards Institute, Fox Island, Washington August 1988 The purpose of this letter is to compliment CharlesWade on his excellent paper

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Dongsoo Kim, Brian Nelson, and Dong-Jun Seo

to describe precipitation for finer-scale events, such as diurnal variations of convective storms, heavy rains that trigger debris flow, and verifications of model forecasts, to name a few. For any scientific study, high quality data are necessary. Often, however, missing values render the record incomplete, and therefore the users have to estimate the missing values. The reprocessing effort allows for the recovery of certain missing data points and for the rigorous quality control of the raw

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David B. Wolff, David A. Marks, and Walter A. Petersen

. Arthur Hou. Also, we are grateful for the support of NASA management, specifically Drs. Ramesh Kakar (NASA/HQ), Scott Braun, and Matt Schwaller (NASA/GSFC), for their support of this research. We thank the NASA Wallops Field Support Office, especially Michael Watson, Nathan Gears, and Gary King, for their support and maintenance of the NPOL radar. Jason Pippitt of the TRMM/GPM Ground Validation Office performed data processing, analysis, and quality control of the NPOL IFloodS and KPOL datasets

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Qin Xu and Kang Nai

s −1 ) other than VCP31 (for the three cases listed in the last three rows), the first-guess radial-velocity field is simply produced by the AR-VAD analysis (instead of the modified AR-VAD analysis in section 2b of XN12 ) in the first step of the AR-Var analysis. The AR-Var-based dealiasing has been incorporated into the radar data quality control package ( Zhang et al. 2005 ; Liu et al. 2005 ) delivered to the National Centers for Environmental Prediction (NCEP) for operational tests ( Liu et

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R. Andrew Weekley, Robert K. Goodrich, and Larry B. Cornman

1. Introduction The analysis of times-series data plays a fundamental role in science and engineering and relies on the identification and classification of various features in the data. Quality control may be viewed as a subclass of problems in general feature identification and classification (e.g., differentiating between a “good” signal and a “contaminated” signal). Existing time-series algorithms detect outliers when the assumptions inherent in the technique are reasonably well satisfied

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