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Kun-Hsuan Chou, Chun-Chieh Wu, Po-Hsiung Lin, Sim D. Aberson, Martin Weissmann, Florian Harnisch, and Tetsuo Nakazawa

Global Data Assimilation System uses a quality control algorithm, a TC vortex relocation procedure, and the Global Spectral Model. The quality control involves optimal interpolation and hierarchical decision making to evaluate the observations before going into detailed analysis ( Woollen 1991 ). A vortex relocation procedure ( Liu et al. 2000 ) in which TCs in the first guess field are relocated to the analyzed position in each 6-h analysis cycle (as in Kurihara et al. 1995 ) ensures that the

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Fuqing Zhang, Yonghui Weng, Jason A. Sippel, Zhiyong Meng, and Craig H. Bishop

the forecast model grid spacing for the EnKF data assimilation, significant data thinning and quality control of observations become necessary. The process of combining multiple observations into one high-accuracy “super” observation (SO) is often referred to as “superobbing.” An SO for radar radial velocity is created through horizontal averaging in polar space of the raw polar volume of data ( Lindskog et al. 2000 , 2004 ; Alpert and Kumar 2007 ). To minimize horizontal correlations of the SOs

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Rolf H. Langland, Christopher Velden, Patricia M. Pauley, and Howard Berger

increases in the amount and quality of observations as well as improvements in forecast models and data assimilation procedures ( Goerss and Hogan 2006 ). The use of multimodel ensembles for “consensus forecasts” is also providing improved guidance to forecasters for TC track prediction ( Goerss 2000 ). However, significant societal and economic value can still be gained by additional increases in the accuracy of track forecasts for landfalling tropical cyclones. In recent years, the use of “targeted

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Sim D. Aberson

changes to the operational data processing, quality control, and data assimilation systems have occurred to correct these problems. Though the surveillance program fully underwent transition to operations by 2007, research into this program has continued. Aberson and Etherton (2006) compared the assimilation of dropwindsonde data into a barotropic model using three-dimensional variational and ensemble transform Kalman filter techniques in two cases in Hurricane Humberto (2001). Qu and Heming

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Sim D. Aberson

reports from the National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC). The environments of three tropical cyclones in the Atlantic and four in the west Pacific were sampled with aircraft 2 ( Fig. 1 ). NHC and JTWC best tracks are used for forecast verification. The GFS version operational during September 2008 was used to assess the dropwindsonde data impact on forecasts. The system consisted of a quality control algorithm, a tropical cyclone vortex initialization procedure, a

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Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

-Var. Yang et al. (2009) compared the LETKF with 3D-Var and 4D-Var in idealized experiments with a quasigeostrophic model and also obtained similar quality results from the 4D-Var and LETKF, which were both better than those obtained with 3D-Var. The goal of this two-part study is to compare the variational and EnKF approaches within the context of global deterministic NWP. In the next section details regarding the configurations of the EnKF and variational data assimilation systems used in this

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Ryan D. Torn and Gregory J. Hakim

the 300-hPa wind. The performance of this filter configuration is similar to the real-time EnKF system described by Torn and Hakim (2008) . Analyses and forecasts are also verified on the outer domain using position and intensity estimates from NHC tropical storm best-track data. Table 1 displays the RMS difference between the NHC best-track position and intensity for the ensemble-mean analysis and background forecast (denoted “control”). These quantities are defined by the grid point of lowest

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Peter Black, Lee Harrison, Mark Beaubien, Robert Bluth, Roy Woods, Andrew Penny, Robert W. Smith, and James D. Doyle

correction (FEC) on two receivers was implemented to improve data reception at ranges > 200 km. Beyond sensor uncertainties, overall data quality is affected by data gaps in the telemetered data. To minimize gaps, the XDD uses FEC to recover data packets having bit errors. In addition, merged data from four redundant receivers minimizes overall data dropouts that appear at different times on the four different receivers, providing a more continuous stream of observations for use in optimal time series

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