Search Results

You are looking at 1 - 10 of 14 items for :

  • Model performance/evaluation x
  • Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter x
  • All content x
Clear All
Thomas M. Hamill and Jeffrey S. Whitaker

characteristics of ensemble predictions initialized from EnKFs with real observations. Of particular concern is ensuring that the spread (the standard deviation of ensemble perturbations about the mean) of ensemble forecast perturbations are consistent with the ensemble-mean forecast error; commonly, spread growth is smaller than error growth. The spread growth in forecasts from operational EnKFs is likely to be affected in part by the choice of methods for dealing with the model uncertainty during the

Full access
Alberto Carrassi and Stéphane Vannitsem

of freedom. In the context of ensemble-based schemes [see, e.g., Evensen (1994) for the ensemble Kalman filter (EnKF)] a lot of efforts have been devoted to the representation of model error through an optimal ensemble design. Among these studies, Hamill and Whitaker (2005) have investigated the ability of two methods, covariance inflation and additive random error, to parameterize error due to unresolved scales. Meng and Zhang (2007) have analyzed the performance of the EnKF in the

Full access
Zhiyong Meng and Fuqing Zhang

-area models (LAMs), which is the focus of the current review. 1 The first LAM application of the EnKF was found in Snyder and Zhang (2003) and Zhang et al. (2004) , where synthetic radar data was assimilated into a cloud model. Those two studies demonstrated that the EnKF analysis can faithfully approximate the truth in terms of both dynamic and thermodynamic variables of a supercell storm ( Fig. 1 ). Fig . 1. The performance of a convective-scale EnKF in assimilating radar radial velocity for

Full access
Steven J. Greybush, Eugenia Kalnay, Takemasa Miyoshi, Kayo Ide, and Brian R. Hunt

the results of section 3 using a simple model. For very long localization distances ( L = 2000 km), presumed spurious correlations can lead to larger values of both error and imbalance. Examination of performance time series reveals that values of imbalance tend to stabilize, along with the error, after 20 days of spinup, although there are day-to-day fluctuations on the order of 0.5 m s −1 that are reflected in both the nature run and assimilation analyses. Figure 6 also depicts

Full access
Mark Buehner and Ahmed Mahidjiba

optimal interpolation data assimilation scheme ( Houtekamer et al. 1996 ), NCEP used bred vectors ( Toth and Kalnay 1993 ), and ECMWF used SVs. The results generally favored the ECMWF system. However, because of differences in forecast model, data assimilation system, and simulation of model error, the authors point out that these differences cannot be used to evaluate the different strategies for obtaining the initial ensemble perturbations. It was thought that the improved performance of the ECMWF

Full access
Shu-Chih Yang, Eugenia Kalnay, and Brian Hunt

may depend on the density of observations. Experimental results suggest that more iterations with a stricter threshold are required to optimize the performance of the RIP method with fewer observations (see section 4d ). Although the success of the RIP scheme was already demonstrated in Kalnay and Yang (2010) , we should note that the computational cost of the RIP scheme is relatively high since all the ensemble members have to be integrated using the nonlinear model. In the next subsection, the

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

example of a numerical implementation This section presents a first implementation of the hybrid method described above, in the context of a simple ocean model. Its aim is to demonstrate that the hybrid algorithm can significantly improve the performance of assimilation with respect to the 4D-Var approach in which is kept static. In the context of twin experiments, we choose a test case where the background error statistics are partly, but not perfectly known, which is in fact the case in most

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

was tested on both simulated observations in the perfect-model scenario ( Szunyogh et al. 2005 ) and on observations of the real atmosphere ( Miyoshi and Sato 2007 ; Szunyogh et al. 2008 ; Whitaker et al. 2008 ). In particular, Szunyogh et al. (2008) and Whitaker et al. (2008) assimilated nonradiance observations in a reduced-resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and found that the performance of the

Full access
Marc Bocquet, Carlos A. Pires, and Lin Wu

schemes are compared on the analysis root-mean-square error (analysis rms error). Still, the particle filter requires 10 4 members to match the EnKF performance. Results are shown in Fig. 1 . The size of the system N = 10 was chosen so that the EnKF/bootstrap filter cross-over could be observed with a reasonable computation load. The collapse of the particle filter with increasing state space dimension can be illustrated on the same Lorenz-95 model. Four configurations are chosen identical to the

Full access
Chiara Piccolo

cost function evaluated by integrating a linearized forecast model rather than the full nonlinear model. Details on 4DVAR and its incremental formulation can be found in Courtier et al. (1994) . Within each assimilation window the model is assumed to be perfect; background and observation errors are assumed to be mutually uncorrelated and unbiased, so that they can each be represented by a zero mean Gaussian distribution. The perfect model assumption is limited by the model resolution and by the

Full access