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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

measuring 50 km × 200 km, but a spacing of 5 km is not. Done et al. (2004) noted the importance of explicit cloud microphysics in high-resolution simulations with a horizontal grid spacing of 4 km. Zhang et al. (2006) showed that the error in mesoscale forecasts grows rapidly with time and arises from moist convection, and after comparing horizontal grid spacings of 30 and 3.3 km they suggested that accurate initial conditions contribute to improving the predictability of mesoscale forecasts. Today

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Jean-François Caron, Thomas Milewski, Mark Buehner, Luc Fillion, Mateusz Reszka, Stephen Macpherson, and Judy St-James

. Since there is currently no operational equivalent to the global EnKF ( Houtekamer et al. 2014 ) at the regional scale at EC, we simply based our 4DEnVar scheme for the RDPS limited-area analysis on the use of 4D ensemble covariances derived from the global EnKF as in the GDPS configuration described in Part I . Our approach is thus similar to the National Centers for Environmental Prediction (NCEP), which recently replaced the 3DVar scheme in the North American Mesoscale Forecast System (NAM) and

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Juanzhen Sun, Hongli Wang, Wenxue Tong, Ying Zhang, Chung-Yi Lin, and Dongmei Xu

-observation test. Such a test was performed by placing a single observation of u with an innovation (difference between observation and background) of 1 m s −1 on the 20th model level in the middle of the inner analysis domain shown in Fig. 1 . CV_ ψχ and CV_UV can result in different u increments due to their different BE covariance matrices. The background field for the single-observation experiments is the North American Mesoscale Forecast System (NAM) analysis at 0000 UTC 9 August 2008, which is the

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Ting-Chi Wu, Christopher S. Velden, Sharanya J. Majumdar, Hui Liu, and Jeffrey L. Anderson

2009 ; Langland et al. 2009 ). NWP systems have recently advanced dramatically with regards to model grid resolution and convective physics. As a result, much attention has turned to using state-of-the-art mesoscale models and data assimilation schemes to improve TC forecasting. Such improvements include more frequent updates with the use of higher density and temporal resolution observation datasets and the choice of thinning and superobbing, 1 and error assignments of these observations that

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Kazumasa Aonashi, Kozo Okamoto, Tomoko Tashima, Takuji Kubota, and Kosuke Ito

1. Introduction Recently, many numerical weather prediction (NWP) centers have routinely used cloud-resolving models (CRMs) that explicitly forecast hydrometers for the improvement of precipitation forecasts (e.g., Saito et al. 2006 ). To make the most of the CRMs at short lead times, the assimilation of precipitation information (e.g., Makihara 2000 , 63–111) is desirable ( Aonashi and Eito 2011 , hereafter referred to as AE ). While precipitation data were confined to areas where ground

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James A. Cummings and Ole Martin Smedstad

1. Introduction Assessment of the impact of observations on reducing ocean model forecast error from data assimilation is a fundamental aspect of any ocean analysis and forecasting system. The purpose of assimilation is to reduce the model initial condition error. Improved initial conditions should lead to an improved forecast. However, it is likely that not all observations assimilated have equal value in reducing forecasting error. Estimation of which observations are best and the

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Hailing Zhang and Zhaoxia Pu

. Scope of this study and observations Inspired by the poor forecasts initialized at 0000 UTC 25 August 2005 in previous studies ( Langland et al. 2009 ; Aberson 2010 ) and the promising results from the mesoscale WRF simulations with the EnKF method ( Weng and Zhang 2012 ; Pu et al. 2013 ), we chose this particular time (0000 UTC 25 August 2005) as an initial time to begin data assimilation experiments in this study. In addition, although several previous studies have done numerical simulation with

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Mark Buehner, Ron McTaggart-Cowan, Alain Beaulne, Cécilien Charette, Louis Garand, Sylvain Heilliette, Ervig Lapalme, Stéphane Laroche, Stephen R. Macpherson, Josée Morneau, and Ayrton Zadra

repeated filter applications in an assimilation cycle. This effect can be seen in Fig. 7 for a pair of data assimilation experiments using the different initialization techniques. The mesoscale portion of the energy spectrum is significantly depleted when the full-field digital filter is employed, with kinetic energy associated with motions on these scales reduced by over 50% in the analysis as compared with a short-range forecast. This leads to a dramatic spinup over the first two days of

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María E. Dillon, Yanina García Skabar, Juan Ruiz, Eugenia Kalnay, Estela A. Collini, Pablo Echevarría, Marcos Saucedo, Takemasa Miyoshi, and Masaru Kunii

advantages of regional models compared with global systems might depend on the chosen variable and/or the vertical level considered. b. A case study For a preliminary evaluation of short-range ensemble forecasts produced by the different DAS compared in this work, the mesoscale convective system (MCS) that took place over central and eastern Argentina between 6 and 7 December 2012 has been selected. These types of MCSs are quite frequent over our region, accounting for more than half of the observed warm

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Shigenori Otsuka and Takemasa Miyoshi

: 10.1175/1520-0469(1998)055<0399:OSFSWO>2.0.CO;2 . Matsueda , M. , M. Kyouda , H. L. Tanaka , and T. Tsuyuki , 2006 : Multi-center grand ensemble using three operational ensemble forecasts . SOLA , 2 , 33 – 36 , doi: 10.2151/sola.2006-009 . Meng , Z. , and F. Zhang , 2007 : Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments . Mon. Wea. Rev. , 135 , 1403 – 1423 , doi: 10.1175/MWR3352.1 . Miyoshi , T

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