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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

South American Low-Level Jet Experiment (SALLJEX; Vera et al. 2006 ) to generate enriched analyses through nudging. In Brazil the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) runs the Regional and Global Physical-Space Statistical Analysis System ( da Silva et al. 1995 ; Herdies et al. 2002 , 2007 ), and more recently they have carried out studies with a global three-dimensional variational data assimilation (3DVAR) system based on the Gridpoint Statistical Interpolation analysis

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Chengsi Liu and Ming Xue

Ide 2015a ) and regional ( Pan et al. 2014 ) models. More recently, EnVar algorithms have been extended into the fourth time dimension to form 4D EnVar algorithms. There are two basic types of such algorithms. One is a direct extension of the 3D extended control variable algorithm of Lorenc (2003) into four dimensions, which can also be considered as introducing the ensemble BEC into a standard 4DVar framework using the extended control variable approach. Being based on the standard 4DVar that

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

forecasting. Airborne DWLs have been used in some field campaigns. For instance, Weissmann et al. (2012) showed that assimilating airborne DWL observations from The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign project into a global model had positive impacts on a forecast of typhoon and atmospheric conditions. The first space-based DWL is planned to be deployed on the Atmospheric Dynamics Mission Aeolus instrument (ADM-Aeolus) by the European Space

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Stefano Migliorini

study is provided. Section 4 discusses the effects of the chosen forecast and observation error as well as of the observation operator specifications on the signal-to-noise characteristics of the satellite instrument, while section 5 provides details on the selection of optimal channels for atmospheric humidity estimation in all-sky conditions as resulting from the use of the selection method described in this paper, including a list of the selected channels. Finally, a summary of the work and

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Nicholas A. Gasperoni and Xuguang Wang

assimilation. Houtekamer and Mitchell (1998) showed that the effects of sampling error can be suppressed by excluding distant observations from influencing the analysis at a given grid point. They experimented filtering covariance estimates using a distance-dependent correlation function, referred to as covariance localization ( Houtekamer and Mitchell 2001 ). Since then, much research has been done in developing localization methods to improve EnKF analyses using limited ensembles (e.g., Hamill et al

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James A. Cummings and Ole Martin Smedstad

), as evidenced further by the series of World Meteorological Organization (WMO) conferences and technical reports (e.g., Andersson and Sato 2012 ). Gelaro and Zhu (2009) compare results from a standard OSE with the adjoint-based method and show that the two approaches provide consistent estimates of the overall impacts of the major observing systems assimilated. Moore et al. (2011b) have applied the method in the ocean using the Regional Ocean Modeling System (ROMS) four

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

sampling errors have more serious effects on precipitation-related variables (precipitation rate, vertical wind speed, etc.) than other variables. This is because sample numbers of nonzero precipitation were much smaller than those of other variables for most grid points. AE also reported that the CRM precipitation-related variables have distinct forecast error features (narrow horizontal correlation scales, etc.), which differ from the other variables. The sampling error is the common problem 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

radiosonde and aircraft observations, a new procedure for initializing the forecast model, and the assimilation of a large number of additional observations. The goal of this study is to describe and evaluate the impact of these recent changes to the operational Global Deterministic Prediction System (GDPS) at EC. A nearly identical data assimilation system is also now used for the operational Regional Deterministic Prediction System (RDPS) as detailed in the companion paper by Caron et al. (2015) . The

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Daryl T. Kleist and Kayo Ide

1. Introduction To combine the advantages of ensemble and variational methods while at the same time attempting to minimize the effects of their weaknesses, hybrid assimilation methods have been proposed and developed by supplementing the ensemble with a static background error covariance ( Hamill and Snyder 2000 ; Lorenc 2003 ; Buehner 2005 ). Typically, these hybrid methods utilize the variational framework for the purposes of calculating the analysis increment [hybrid 3D ensemble

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Robin J. T. Weber, Alberto Carrassi, and Francisco J. Doblas-Reyes

system models for seasonal-to-multiyear time horizons have recently appeared ( Magnusson et al. 2013 ; Smith et al. 2013 ; Hazeleger et al. 2013 ). These studies represent a first attempt to assess their respective performance using exactly the same observational and model setup and are therefore of central importance in guiding future development of initialized climate prediction systems. Results have been far from conclusive, varying regionally and among models. Carrassi et al. (2014

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