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Norihisa Usui, Yosuke Fujii, Kei Sakamoto, and Masafumi Kamachi

incremental 4DVAR because results of the fine-resolution model are not taken into account as the background state in MOVE-4DVAR-WNP. Such improvement will be subject of future study. 4. Experimental setup We conducted two sets of assimilation experiments, which are summarized in Table 1 . The first set is WNP-3DVAR and WNP-4DVAR, which aim to evaluate mesoscale variability. WNP-3DVAR was carried out using MOVE-3DVAR-WNP, in which the 3DVAR scheme was applied to MRI.COM-WNP. It should be noted that MOVE-3

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

forecast impacts resulting from various improvements to the observational data assimilated are presented while section 5 presents a thorough forecast performance evaluation of the configuration implemented at EC operations in the fall of 2014. Finally, a summary of the results as well as future directions are presented in section 6 . 2. Design of the RDPS a. Forecast model configurations and cycling strategies The RDPS represents the main NWP guidance for forecasters of the Meteorological Service 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

using the same model configuration of LETKF-single. In addition, the GFS forecasts are also verified and included in the comparison. Figure 9 shows the bias and RMSD calculated for the 12-h forecasts of the experiments initialized with the analysis ensemble mean of LETKF-multi and LETKF-single, and of WRF-GFS and GFS single runs, with respect to the 1200 UTC 6 December radiosondes observations, in order to evaluate their performance for the case study. For the wind components, the RMSD for LETKF

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D. J. Lea, I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow

covariances, which may then be applied to more sophisticated coupled data assimilation techniques. The subject of this paper, however, is focused on assessing the performance of the weakly coupled DA system and identifying aspects of the system that may be improved in future. Assessing the coupled error covariances will be left to future work. In section 2 we describe the coupled model components and the data assimilation systems used in more detail. In section 3 the results of a 13-month run of the

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Hyo-Jong Song and In-Hyuk Kwon

:// ). As a result, the STCS-3DVAR obtains the analysis field through the following final step: b. Experimental setting to test the STCS-3DVAR To evaluate the STCS-3DVAR, observing system simulation experiments (OSSEs) were conducted using the Community Atmosphere Model with Spectral Element dynamical core (CAM-SE) with ne = 16 and np = 4 ( Evans et al. 2013 ). We assumed that the model run of CAM-SE with a year spinup using a default initial condition obtained from the website of the Community Earth

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Shigenori Otsuka and Takemasa Miyoshi

1. Introduction A multimodel ensemble aims to cope with model imperfections in numerical weather prediction (NWP) and has been studied extensively in recent years. In operational NWP, ensemble predictions with perturbed initial conditions have widely been used to evaluate the forecast uncertainties due to the initial condition uncertainties. This type of ensemble prediction system (EPS) was developed based on the theory of error growth due to the chaotic nature of the atmosphere (e.g., Yoden

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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

2014 ). However, the cost functions therein typically use a different weighting on the norm than , in one case yielding an optimum that is the symmetric left -multiplying transform matrix—not to be confused with the right-multiplying one of Theorem 2. Theorem 2 and the related properties should benefit the performance of filters employing the square root update, whether for the analysis step, the model noise incorporation, or both. In part, this is conjectured since minimizing the displacement of

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

1. Introduction A geophysical prediction is made by integrating the model in time from its initial condition. The quality of the forecast will rely on the quality of the initial condition, and the quality of the model, given by the implementation of the set of equations describing nature. Predictions evaluated beyond the deterministic limit of two weeks typical of numerical weather prediction are useful mainly when considering the statistical properties of the natural system over forecast

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Lars Nerger

reduce the estimated state error variance. Whitaker et al. (2008) also compared the assimilation performance of the EnSRF with the LETKF when applied with a global atmospheric model and found only small differences. Similarly, Holland and Wang (2013) compared the LETKF with the EnSRF without particular observation ordering for the assimilation with a simplified atmospheric model. They found only small differences in the state estimates with slightly smaller errors in the LETKF estimates. While

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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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