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

t is defined as either for 3D-FGAT, for 4D-Var, or for En-4D-Var, where 𝗠 t is the tangent linear model that maps perturbations from the beginning of the assimilation window to time t and 𝗕 1/2 is the square root of the background-error covariance matrix valid either at the beginning (4D-Var) or middle (3D-FGAT) of the assimilation time window or the 4D ensemble covariances valid for 5 time levels over the entire (0 ≀ t ≀ T ) window (En-4D

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Munehiko Yamaguchi, Takeshi Iriguchi, Tetsuo Nakazawa, and Chun-Chieh Wu

nature of the atmosphere as well as the imperfection of NWP systems. Tropical cyclone track forecasts are no exception. Consequently, sometimes an almost perfect forecast may only contain position error of less than 50 km in a 3-day forecast. However, sometimes the 3-day forecast error can be over 1000 km. For this reason, the Ensemble Prediction System (EPS) has been attracting attention because it is expected to provide uncertainty information inherent to each forecast event (e.g., Puri et al

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

with the range used in Dowell et al. (2004) and Montmerle and Faccani (2009) , and is also a conservative version of the 2.4 m s βˆ’1 estimated in Xu et al. (2003) . e. Successive covariance localization A successive covariance localization (SCL) technique is designed to assimilate dense radar observations that contain information about the state of the atmosphere at a wide range of scales. The method is also designed to reduce computational costs and sampling errors. This technique uses the

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Hyun Mee Kim and Byoung-Joo Jung

Fig. 3a is decomposed into three levels, each representing one part of the atmosphere ( Fig. 12 ). The energy-weighted SVs in the lower part (from 700 hPa to the surface), middle part (380–650 hPa), and upper part (100–300 hPa) of the atmosphere are shown in Figs. 12a,b,c , respectively. As shown in Fig. 10 , large sensitivities in the midlatitude are located in the lower part ( Fig. 12a ), not in the upper part where the upper trough is located. In the midlevel, major sensitivities are located

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Matthieu Plu

. The existence of a natural, inherent predictability bound for every atmospheric process is due to the nonperiodic property of the atmosphere ( Lorenz 1963 ). Several studies have been assessing the inherent predictability of tropical cyclone tracks in different basins. A common measure of predictability is the doubling time of small position errors. Fraedrich and Leslie (1989) applied a nonlinear system analysis to a climatology of tracks around Australia, followed by Aberson (1998) and

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Munehiko Yamaguchi, Ryota Sakai, Masayuki Kyoda, Takuya Komori, and Takashi Kadowaki

moist processes) are called moist (dry) SVs. In TEPS, dry SVs are calculated targeting for a midlatitude area in the RSMC Tokyo-Typhoon Center’s responsibility area, aiming to identify the dynamically most unstable mode of the atmosphere like the baroclinic mode ( Buizza and Palmer 1995 ). Moist SVs are calculated targeting for TC surroundings where moist processes are crucial ( Barkmeijer et al. 2001 ). TEPS can target up to three TCs at the same time. If more than three TCs exist, three TCs are

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