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  • Data quality control x
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Florian Harnisch and Martin Weissmann

assimilation system seems to be capable of handling extreme observations in the TC center (CeObs). The quality control works reliably and a large fraction of the data are flagged and rejected to minimize unrepresentative structures in the model. However, in terms of track forecast errors the influence is neutral on average. There is a significant case-to-case variability with these observations and large positive (e.g., 0000 UTC 9 September) as well as negative (e.g., 0000 UTC 10 September) cases can be

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Martin Weissmann, Florian Harnisch, Chun-Chieh Wu, Po-Hsiung Lin, Yoichiro Ohta, Koji Yamashita, Yeon-Hee Kim, Eun-Hee Jeon, Tetsuo Nakazawa, and Sim Aberson

) and one that assimilates dropsondes (DROP). Special TEMP and SYNOP were not denied. All other observations from the NCEP archive were ingested into the assimilation system for both runs. The experiments were conducted in a cycled mode for the whole T-PARC period. Dropsondes in the Atlantic were also removed in the NODROP run. The NCEP Global Data Assimilation System (GDAS) consists of a quality control algorithm, a TC vortex initialization procedure, data assimilation, and the global spectral

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Kirstin Kober, Annette M. Foerster, and George C. Craig

. 2 in Groenemeijer and Craig (2012) ] is nonhomogenous. The resulting evaluation domain used in this study covers 104 × 68 grid points. b. Radar data The forecast quality of the PC08 and the Tiedtke ensemble forecasts of hourly precipitation is evaluated in comparison with precipitation fields derived from radar observations. The German radar composite provided by the Deutscher Wetterdienst (DWD) is combined from quality-controlled measurements of radar reflectivites obtained from 16 Doppler

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Hilke S. Lentink, Christian M. Grams, Michael Riemer, and Sarah C. Jones

Center “Waves to Weather” (TRR 165), project A4: “Evolution and predictability of storm structure during extratropical transition of tropical cyclones.” Observational data were obtained in the framework of T-PARC. We thank the international consortium that supported the T-PARC field campaign and acknowledge the involvement of the U.S. National Science Foundation (NSF)–sponsored National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) for data management and quality control

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Lisa-Ann Quandt, Julia H. Keller, Olivia Martius, and Sarah C. Jones

Forecasts (ECMWF, 51 members), the National Centers for Environmental Prediction (NCEP, 21 members), and the Met Office (UKMO, 24 members). With this selection, each TIGGE forecast contained 96 members. The members were retrieved in 12-h forecast steps at an interpolated grid of 1° × 1°. The initial conditions of the three control forecasts at the same spatial resolution as the forecasts were used as analysis data. As these differed only slightly, an unweighted mean of the control runs was used as an

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Kirstin Kober and George C. Craig

variances are computed diagnostically by implementing the above equations in the parameterization in the COSMO model by Raschendorfer (2001) . c. Observational data The precipitation forecasts are compared with precipitation fields derived from radar observations. The German radar composite provided by the DWD is computed from quality controlled measurements of radar reflectivities obtained from 16 Doppler radars. The reflectivities are available every 5 min with 1-km horizontal resolution and

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Lars Wiegand, Arwen Twitchett, Cornelia Schwierz, and Peter Knippertz

heavy precipitation. Interensemble comparisons reveal both model errors and problems with the design of the EPS as well as the added value of a multimodel ensemble. Furthermore, the study examines the dynamical linkage between the forecast qualities of upper- and lower-level features with a novel correlation approach. The paper continues with the data and methodology section. Section 3 describes the synoptic evolution of the PV streamer under study and its impacts. In section 4 the forecast

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Julian F. Quinting, Michael M. Bell, Patrick A. Harr, and Sarah C. Jones

and 150 km north). Vertical cross sections are used in the following to illustrate the structure of the precipitation and the wind field. The elevation of the reflectivity pattern from the ground in the SAMURAI analysis in these cross sections ( Fig. 6 ) results from the quality control process. In fact, precipitation existed at low levels but its signal was removed through ground clutter removal. Fig . 6. Reflectivity (dB Z ; shaded) along (a) 46 km north and (b) 160 km east. Wind vectors are

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