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

initial control subspace are obtained from a sample that is not over a long enough period to define a background error covariance matrix of sufficient quality. Therefore, keeping this matrix unchanged does not allow for a fully accurate correction of the model state by the 4D-Var, while making this initial basis evolve ensures significant improvement of data assimilation results. It is to be noted that the absolute errors of h are larger than the expected 5 cm introduced by the observations. This is

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

analysis increments from the EnKF and variational data assimilation systems can be significantly different, even when using the same background and observation-error statistics. The observation bias correction and all quality control decisions were extracted from an independent 4D-Var data assimilation experiment with a configuration very similar to the 2008 operational system. Consequently, all bias correction and quality control procedures were deactivated in the experimental configurations of

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Zhiyong Meng and Fuqing Zhang

, significant data thinning of observations may be necessary. The process of combining multiple observations into one high-accuracy “super” observation (SO) is often referred to as “superobbing.” A data thinning and quality control procedure was developed in Zhang et al. (2009a) to generate SOs for ground-based Doppler radars (e.g., WSR-88Ds), with the observation error for radial velocity assumed to be 3 m s −1 . To avoid averaging of radial velocities (Vr) with significantly different directions, the

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

-Var. Yang et al. (2009) compared the LETKF with 3D-Var and 4D-Var in idealized experiments with a quasigeostrophic model and also obtained similar quality results from the 4D-Var and LETKF, which were both better than those obtained with 3D-Var. The goal of this two-part study is to compare the variational and EnKF approaches within the context of global deterministic NWP. In the next section details regarding the configurations of the EnKF and variational data assimilation systems used in this

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Marc Bocquet, Carlos A. Pires, and Lin Wu

property called heteroscedasticity), in particular when aggregate statistics are used in data assimilation systems. 2) Observation error prior Observation error priors may also require non-Gaussian modeling. Otherwise, quality-control (QC) filters are mandatory ( Lorenc 1986 ). Gaussian modeling of observation errors correspond to a least squares penalty. Therefore, the data assimilation system will be forced to comply with outliers, which can be regarded as good when these observations mark correctly

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José A. Aravéquia, Istvan Szunyogh, Elana J. Fertig, Eugenia Kalnay, David Kuhl, and Eric J. Kostelich

radiance observations. These radiance observations fill important data voids in the coverage by the conventional data (see Figs. 1 and 2 ). We process many more observations than indicated by Table 1 , but the number of observations is reduced by selecting only a subset of the radiance observations for assimilation and by rejecting observations that do not pass quality control. The data selection strategy and the quality control procedure are explained in section 4 . Table 1. Number of

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Alberto Carrassi and Stéphane Vannitsem

1. Introduction Most operational weather prediction centers worldwide adopt a variational data assimilation algorithm ( Sasaki 1970 ; Le Dimet and Talagrand 1986 ; Rabier et al. 2000 ). The state estimation in the variational assimilation is formulated as an optimal control problem, and aims at determining the trajectory that best fits the observations and accounts for the dynamical constraints given by the law supposed to govern the flow. The accuracy of the variational

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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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Craig H. Bishop and Daniel Hodyss

was faster than PAECL would have been even greater. Since our adaptive localization functions are built from ensemble perturbations, the quality of the adaptive localization is closely linked to the quality of the ensemble perturbations. If assimilating real data, this quality depends on how well the ensemble generation scheme accounts for all sources of error. Accounting for unknown sources of model error is very difficult. In our experiment, we modeled the error-ensemble mismatch using

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

. Fig . 1. Schematic showing the forecast–analysis cycle. Fig . 2. Schematic showing data flows and job sequences of (a) operational 4D-Var and (b) LETKF. Each box corresponds to a job, except for the box indicating decoded observation data (Obs). QC, fcst, and Ensmean refer to quality control, forecast, and computed ensemble mean state, respectively. The arrow types are as shown in the legend, with gridpoint values (GPVs). Fig . 3. The 40 model levels defined by sigma-pressure hybrid coordinate

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