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Debasish PaiMazumder and Nicole Mölders

.S. precipitation data. Bull. Amer. Meteor. Soc. , 75 , 215 – 227 . Groisman , P. Y. , V. V. Koknaeva , T. A. Belokrylova , and T. R. Karl , 1991 : Overcoming biases of precipitation measurement: A history of the USSR experience. Bull. Amer. Meteor. Soc. , 72 , 1725 – 1733 . Hanna , S. R. , 1994 : Mesoscale meteorological model evaluation techniques with emphasis on needs of air quality models. Mesoscale Modeling of the Atmosphere, Meteor. Monogr., No. 25, Amer. Meteor. Soc., 47

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Hilawe Semunegus, Wesley Berg, John J. Bates, Kenneth R. Knapp, and Christian Kummerow

and Grody, 1998 ; Jackson et al. 2002 ; Zhang et al. 2006 ). To improve geophysical parameters such as global rainfall estimation in support of the National Aeronautics and Space Administration (NASA) Global Precipitation Mission (GPM), Berg and Kummerow (2006) developed SSM/I quality control procedures that have been shown to significantly remove spurious geolocation, radiance, and climatologically anomalous data. Vila et al. (2010) have demonstrated the effectiveness of these statistically

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Madison L. Miller, Valliappa Lakshmanan, and Travis M. Smith

generated using the default WSR-88D velocity dealiasing technique and without any quality control techniques. The spikes in the high azimuthal shear values are caused by poor velocity dealiasing along radials and can make data interpretation difficult, if not impossible, in some areas. One of the goals of the Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) project, a cooperative effort between National Oceanic and Atmospheric Administration's (NOAA) NSSL and the National Climatic Data Center

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Anthony C. Riddle, Leslie M. Hartten, David A. Carter, Paul E. Johnston, and Christopher R. Williams

radio astronomy and dealing with both signals in noise and noise as a signal. He died in December 2004, seven years after “retiring” from CIRES. We coauthors, who worked with him for many years at NOAA’s Aeronomy Laboratory and still make use of his computer code, datasets, and scientific results, miss Tony’s energetic and rigorous approach to data analysis and quality control, his creativity, and most of all his ready and good-humored conversation. This research was supported by grants from NOAA

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Scot M. Loehrer, Todd A. Edmands, and James A. Moore

One of the most important datasets to come from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) is the most complete, high-resolution upper-air sounding dataset ever collected in the equatorial western Pacific Ocean. The University Corporation for Atmospheric Research/Office of Field Project Support&UCAR/OFPS (recently combined with the UCAR/Joint International Climate Projects Planning Office and renamed the Joint Office for Science Support); was given the responsibility of processing, quality controlling, and archiving the dataset. OFPS, in consultation with the TOGA COARE scientific community, developed a four-stage process to provide the community with a thoroughly quality controlled dataset.

The TOGA COARE sounding dataset includes over 14 000 soundings, collected from 14 countries, in over 20 different original formats. The first OFPS processing step was the conversion of all soundings to a single, easy to use format, the OFPS quality control format. The second stage was a series of automated internal consistency checks on each sounding. This stage was particularly important as it directly led to the improvement of several of the datasets. The third step was a visual examination of each sounding to provide another layer of internal consistency checks, for dewpoint and wind in particular. The final process used spatial quality control checks to put each station into context with its neighboring stations as well as the network as a whole. These checks provided statistics from which both systematic and individual sounding problems could be determined. Finally, some derived sounding parameters such as convective available potential energy (CAPE) were calculated for each sounding. The CAPE calculations provided a quick method to qualitatively examine the high-resolution sounding data for low-level humidity problems. A composite dataset of all soundings at a uniform vertical resolution of 5 hPa was created to provide the community with a sounding dataset that has been found to be useful in certain modeling studies.

The processed TOGA COARE sounding data, as well as statistical output from the OFPS spatial quality control procedures, are available on-line via the Internet using the World Wide Web (WWW) through the OFPS data management system. Access via the WWW allows a full range of on-line data browsing and ordering capabilities.

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Guangxin He, Gang Li, Xiaolei Zou, and Peter Sawin Ray

1. Introduction Radar observations play an increasingly important role in numerical weather prediction (NWP) where forecasts are initialized with data from many sources. The inclusion of radar data in real-time initialization requires substantial automation, adequate accuracy, and techniques that constitute a robust quality control (QC) on the data. One challenge with incorporating Doppler radar data is related to velocity aliasing. The range of unambiguous velocities is derived from the

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Bruce Ingleby

recent data (their Fig. 2), although there will be differences due to data sampling, quality control, and other factors ( Ingleby 2001 ). c. Processing Reports were subject to the following quality control steps: (a) thinning, so that reports from the same station are at least 1 h apart; (b) track checking, using the algorithm of Ingleby and Huddleston (2007) ; and (c) land checking, using the OSTIA land mask. The track check examines a month’s position data for each station, and if the

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Imke Durre, Xungang Yin, Russell S. Vose, Scott Applequist, and Jeff Arnfield

the station coordinates or station list elevation. In addition, 14 stations for which no monthly median elevation could be calculated, and which contained a very small number of soundings, were excluded from IGRA 2. 2) Handling of mobile stations The locations of mobile observation platforms present a special challenge in quality control. Their data are not suitable for any check that is based on a station’s climatological range or time series and therefore were excluded from such tests. However

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Shoji Hirahara, Masayoshi Ishii, and Yoshikazu Fukuda

compared with the SST analysis of the recent years based on satellite and in situ data. The subsampled data and their analysis are referred to below as pseudo-observations and pseudoanalysis, respectively. Quality-controlled and merged data used in the pseudoanalysis are subsampled on a daily basis, which are nearest to the date (month and day only) and position of the past data. Duplicate sampling within 15 days is allowed. The number of subsampled observations is almost the same as that of the

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Frédéric Fabry, Clotilde Augros, and Aldo Bellon

1. The mystery: Observations of sharp vertical wind shear During a visit to Météo-France in January 2012, Frédéric Fabry was courteously shown some of the latest work from his hosts. One of these presentations by Clotilde Augros dealt with a new algorithm for aviation safety. The algorithm seeks to detect lines of low-level wind shear from C-band and S-band Doppler data ( Augros et al. 2010 ). Most of the lines detected did come from low-level phenomena. But, occasionally, the algorithm also

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