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Young-Joon Kim, William F. Campbell, and Steven D. Swadley

system, or simply the solver that takes innovations and produces corrections; context will make it clear which is the correct meaning. Data assimilation procedures typically begin with quality control (QC) procedures to mitigate errors in the observation data. Our QC procedures for AMSU-A radiances include elimination of redundant data, screening of known bad satellite channels and low-peaking channels over land–sea ice, gross error checks for unphysical values, an innovation outlier check

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Li Bi, James A. Jung, Michael C. Morgan, and John F. Le Marshall

: 15 September–30 October 2006 and 15 February–30 March 2007. Our first choice would have been the more extreme months of January and July. However, due to data collection problems, including satellite outages, October and March were the two seasons with the best data coverage. The first two weeks of each time period were removed from these results to allow the assimilation system and forecast model to adjust to the new data. a. Quality control and data thinning The WindSat data in this experiment

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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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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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Daniel P. Tyndall, John D. Horel, and Manuel S. F. V. de Pondeca

, was developed for this research to minimize the computational cost of running large numbers of sensitivity experiments using a full analysis system over the entire continental United States (i.e., the RTMA). Since some of the most complex aspects of any data assimilation system are associated with the preprocessing and quality control of the data, the LSA was designed to use the RTMA’s terrain, derived from U.S. Geological Survey (USGS) elevation datasets with the help of the preprocessing

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William R. Moninger, Stanley G. Benjamin, Brian D. Jamison, Thomas W. Schlatter, Tracy Lorraine Smith, and Edward J. Szoke

-to-date 13-km-version code. In February 2006 and subsequently in April 2007, the analysis and model code in the dev–dev2 versions of the RUC used for the TAMDAR impact experiments were upgraded to improve the observation quality control and precipitation physics. These modifications were generally the same as those implemented into the operational NCEP 13-km RUC, with the exception that dev and dev2 do not ingest radar data (implemented in the NCEP RUC in November 2008). The studies herein focus on

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Pao-Liang Chang, Wei-Ting Fang, Pin-Fang Lin, and Yu-Shuang Tang

velocity data into a series of wavenumbers from 0 to 200 at a constant radius and elevation. Based on the rotational speed of the radar antenna, the predominant oscillation periods (frequencies) can be determined by selecting the wavenumbers of crucial components. Additionally, quality control (QC) procedures are performed to mitigate the effects of the oscillations based on the FFT analysis by using a bandpass filter ( Warde and Torres 2017 ); the effects of these oscillations will be examined in

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

and Prediction System (GRAPES), which is a 3D variational data assimilation system developed in China. It is anticipated that radar data assimilation at the convective scale has the potential to improve the prediction of hazardous weather in China and elsewhere. The integration of radar data into real-time NWP products requires substantial automation, adequate data accuracy, and robust quality control (QC) procedures. One challenge with radar data is correcting velocity aliasing. The unambiguous

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Feng Gao, Xiaoyan Zhang, Neil A. Jacobs, Xiang-Yu Huang, Xin Zhang, and Peter P. Childs

observation counts are displayed in Fig. 2 . The wind observations are fewer than those for RH, which is less than for the temperature. This is because the wind observation requires an accurate aircraft heading reading, so whenever the plane is banking or rolling in a turn over a frame-specific threshold, the wind data are flagged. On occasion, RH data will also be flagged, which typically happens during brief icing events; these data were withheld based on the current quality control flagging system

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