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Zhiyong Meng and Fuqing Zhang

-void areas for the LAM EnKF applications, especially considering our increasing desire to explicitly resolve moist convection. Associated with the data-sparseness problem, the error features of any given mesoscale forecast are poorly known, and as a result it is more difficult to generate initial perturbations and to verify the LAM EnKF results relative to the results of its large-scale counterpart. Moreover, the error growth dynamics of meso- to convective-scale systems are substantially different from

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Jean-François Caron and Luc Fillion

1. Introduction For numerical weather prediction (NWP) forecasts at mesoscale and very-short-range time scales (e.g., nowcasting), the forecast of precipitation is of major interest but also poses the greatest challenge. A large part of the quality of the forecast relies on the quality of the initial conditions (the so-called analysis). The mesoscale analysis must contain the necessary information to allow the NWP model to start with precipitation areas at the right location and to correctly

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Takuya Kawabata, Tohru Kuroda, Hiromu Seko, and Kazuo Saito

(Meso-4DVAR; Koizumi et al. 2005 ) in 2002, making it the first operational regional 4DVAR system in the world. By assimilating 1-h accumulated rainfall amounts derived from radar-reflectivity data, they improved the accuracy of the JMA operational mesoscale forecasts. However, the horizontal grid spacing of Meso-4DVAR was 20 km, and the precipitation scheme in the adjoint model adopted only large-scale condensation and convective adjustment. In 2009, Meso-4DVAR was replaced with a different 4DVAR

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Thomas M. Hamill and Jeffrey S. Whitaker

1. Introduction The ensemble Kalman filter (EnKF; Evensen 1994 ; Houtekamer and Mitchell 1998 ) and its variants (e.g., Hamill and Snyder 2000 ; Anderson 2001 ; Whitaker and Hamill 2002 ; Hunt et al. 2006 ) are being explored for their use in improving the accuracy of initial conditions and for initializing ensemble weather predictions. The EnKF produces an ensemble of parallel short-term forecasts and analyses; background-error covariances from the ensemble are used in the

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

perturbations provide better ensemble prediction than BV or SV. This study addresses only the former aspect by verifying deterministic forecast skills. The EnKF methods that do not use perturbed observations are categorized as ensemble square root filters (EnSRF; Anderson 2001 ; Whitaker and Hamill 2002 ; Tippett et al. 2003 ). The Japanese Meteorological Agency (JMA) started the development of the local ensemble transform Kalman filter (LETKF; Hunt et al. 2007 ), a kind of EnSRF, in the summer of 2005

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Loïk Berre and Gérald Desroziers

). In this case, background error variances were calculated by using a sliding window with a total sample of 81 forecasts made of 9 perturbed forecast fields (from a 9-member ensemble) coming from 9 successive analysis networks. In particular, it was observed that this temporal averaging allowed the robustness of variance estimates to be strengthened. A similar technique has been used in the context of a mesoscale ensemble square root filter ( Xu et al. 2008 ), to compute local time averages of

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Monika Krysta, Eric Blayo, Emmanuel Cosme, and Jacques Verron

finding the minimum takes the form of an ill-posed inverse problem requiring an additional regularization term. Some external knowledge on the system is then introduced, namely an initial guess (or background) x b (known as x f in the sequential approach), and the corresponding background error covariance matrix whose sequential counterpart is the forecast error covariance matrix . NWP favors purely statistical approaches to specify , as can be found for example in Parrish and Derber (1992

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Marc Bocquet, Carlos A. Pires, and Lin Wu

infers data from indirect measurements. The proper statistical modeling depends on how uncertainty evolves under the full data assimilation system dynamics. In particular, in the context of forecasting, this modeling should properly account for the uncertainty growth–reduction cycle, which is controlled by the forecast–analysis steps of the data assimilation cycle. Truncating statistics to the first- and second-order moments (bias and error covariance matrix) may be made necessary because of the

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Shu-Chih Yang, Eugenia Kalnay, and Brian Hunt

so that ensemble perturbations are centered at a more accurate state. Both the RIP and the QOL methods can be applied to improve the LETKF scheme for nonlinear cases, like a “cold start” of an EnKF or when the background error statistics suddenly change. An example of a cold start is the initialization of regional data assimilation from a global analysis obtained at coarser resolution, thus lacking features that represent the underlying mesoscale evolution. Also, when the model trajectory

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Alberto Carrassi and Stéphane Vannitsem

context of a mesoscale and regional-scale model affected by significant model error due to physical parameterizations, while in Fujita et al. (2007) the EnKF was used to assimilate surface observations with the ensemble designed also to represent errors in the model physics. Houtekamer et al. (2009) have examined several approaches to account for model error in an operational EnKF used in a numerical weather prediction (NWP) context. They found that, from the approaches they considered, a

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