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Xin Zhang, Xiang-Yu Huang, Jianyu Liu, Jonathan Poterjoy, Yonghui Weng, Fuqing Zhang, and Hongli Wang

1. Introduction Since the 1980s, the four-dimensional variational data assimilation (4D-Var) technique ( Le Dimet and Talagrand 1986 ; Lewis and Derber 1985 ) has become one of the most widely used advanced analysis methods in atmospheric and oceanic research and operational centers. The European Centre for Medium-Range Weather Forecasts (ECMWF) was the first operational center to implement the 4D-Var system ( Courtier et al. 1994 ; Rabier et al. 2000 ). Following ECMWF, other national

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Sid-Ahmed Boukabara, Isaac Moradi, Robert Atlas, Sean P. F. Casey, Lidia Cucurull, Ross N. Hoffman, Kayo Ide, V. Krishna Kumar, Ruifang Li, Zhenglong Li, Michiko Masutani, Narges Shahroudi, Jack Woollen, and Yan Zhou

OSSEs are now performed with operational DA and forecast components (e.g., Boukabara et al. 2016 ). Also, OSSEs have been applied to other forecast systems (e.g., for hurricanes; Atlas et al. 2015a ). For a more thorough description of the scientific and technological background of OSSEs and an overview of recent and anticipated progress in OSSEs, see Hoffman and Atlas (2016) and references therein. In response to the challenges of creating, maintaining, and validating a state-of-the-art OSSE

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David J. Stensrud, Michael H. Jain, Kenneth W. Howard, and Robert A. Maddox

, Boulder, Amer. Meteor. Soc., 109-110.Davies-Jones, R. P., 1984: Streamwise vorticity: The origin of updraft rotation. J. Atmos. Sci., 41,2991-3006.Dey, C. H., 1989: The evolution of objective analysis methodology at the National Meteorological Center. Wea. Forecasting, 4, 297-312.Doswell, C. A. III, 1985: The operational meteorology of convectiveweather. Volume II: Storm scale analysis. NOAA Tech. Memo.ERL ESG-15, NTIS PB 85-226959, 240 pp.Doviak, R. J., and D. S. Zrni6, 1984: Doppler Radar

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Wei Li, Yuanfu Xie, Zhongjie He, Guijun Han, Kexiu Liu, Jirui Ma, and Dong Li

error is removed from the observation, and it can thus provide a better or more accurate analysis. In this paper, the multigrid data assimilation scheme is applied to assimilate SST and temperature profiles of the China Seas into a numerical model in a retroactive real-time forecast experiment. A comparison of the results to those of the traditional 3DVAR using correlation scales is presented. In the following section, the theory and verification of the multigrid data assimilation scheme are

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Renée Elio, Johannes De Haan, and G. S. Strong

desirable to test the operational aspects of METEOR as a system. This included automatic data collection from Red Deer to Edmonton, and the transferof predictions back to Red Deer. During the 2-week test, 3 days had to be omittedbecause of dam communication problems. Of the remaining 11 days, METEOR correctly predicted thatno severe storm would occur on 9 days. The prototypewas not configured to forecast the 'intensity of weakintensity storms, only that there would be no severestorms (CDC >~ +3

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Pekka J. Rossi, V. Chandrasekar, Vesa Hasu, and Dmitri Moisseev

( Joe et al. 2012 ). The Thunderstorm Radar Tracking (TRT) system ( Hering et al. 2008 ) is employed in an operative environment in Switzerland ( Rotach et al. 2009 ) and in France ( Joe et al. 2012 ). Sometimes these algorithms are embedded in larger operational multisource forecast systems. For example, the operational systems described by Mueller et al. (2003) and Nisi et al. (2014) use information from radar-based storm objects and various additional real-time information sources, such as

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Lotfi Aouf, Jean-Michel Lefèvre, and Danièle Hauser

). Therefore, our prescribed first-guess errors on wind speed are larger than for the ECMWF wind analyses, but are relatively similar to the error of typical wave forecasts from operational models. By comparing outputs from runs C and D to the truth (run A), we computed a mean estimate of background errors over all sea points. For run C, the mean value of the standard deviation for significant wave height and mean period are 0.36 m and 0.85 s. These valuesincrease to 0.5 m and 0.90 s for the case of run D

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Louis Garand and Jacques Hallé

areas in January. The model horizontal resolution is similar (1° × 1°) to that of the Humsat parameter extraction system, which computes the average 6.7- μ m radiance (then inverted to BT) within 1° squares to eventually be used in fits. Brightness temperature was computed assuming no clouds, and therefore the fits are valid for clear-sky conditions. It was judged preferable to use a short-term forecast rather than an analysis because current operational analyses stop at 300 mb (extrapolation above

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Régis Borde, Olivier Hautecoeur, and Manuel Carranza

1. Introduction Atmospheric motion vectors (AMVs) are one of the most important meteorological products extracted from satellite imagery because they are assimilated every day in numerical weather prediction (NWP) models. They have been derived operationally from geostationary satellites [GOES, Meteorological Satellite (Meteosat), Multifunctional Transport Satellite (MTSAT)] for more than two decades by tracking clouds or water vapor tracers through successive images. Polar wind extraction was

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C. J. Merchant, A. R. Harris, E. Maturi, O. Embury, S. N. MacCallum, J. Mittaz, and C. P. Old

, climate, numerical weather prediction (NWP), and ocean and high seas forecasting. The accurate GOES SST data allows the climate community users to account for the diurnal SST effects. GOES-12 became the operational GOES-E platform on 1 April 2003, replacing the ageing GOES-8 . The latter was the original three-axis GOES platform launched in 1994. The GOES-8 , -9 , and -10 imager, with three infrared channels (3.9, 11, and 12 μ m), required a significant investment of resources over several

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