Search Results

You are looking at 1 - 9 of 9 items for :

  • Boundary conditions x
  • Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter x
  • All content x
Clear All
Takuya Kawabata, Tohru Kuroda, Hiromu Seko, and Kazuo Saito

and lateral boundary conditions at the beginning time of the assimilation window, x lbc is the lateral boundary conditions at the end time, and and are first-guess fields of x 0 and x lbc , respectively. Here represents the background covariance matrix associated with the initial (lateral) boundary conditions. Lateral boundary conditions, except at the initial and end times, are obtained by linear interpolation. Here H is the observation operator, y o comprises the observations, and

Full access
Zhiyong Meng and Fuqing Zhang

applications of the EnKF in global and limited-area models. First, the LAM EnKF needs a proper way to perturb lateral boundary conditions. Second, as a result of the smaller scale of the systems of interest, model error might be more severe since the dynamics and physics of meso- to convective-scale systems are less well understood and thus likely to be more poorly represented in the model. Furthermore, there are more inhomogeneities in the spatial and temporal coverage of observations and more data

Full access
Mark Buehner and Ahmed Mahidjiba

, tangent-linear, and adjoint versions of the GEM model configured with a 120 × 60 horizontal grid (3° latitude and longitude grid spacing) and 28 vertical levels. The tangent-linear and adjoint models, as partially described by Tanguay and Polavarapu (1999) , are employed with a simplified planetary boundary layer parameterization ( Laroche et al. 2002 ). The initial conditions used to obtain the linearization trajectory for the SV calculation were the same EnKF-derived mean analyses used to specify

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

in time, so boundary conditions are not needed. c. Simple model results Figure 3a shows the dependence of RMSE for each analysis as a function of localization distance L . LETKF rather than the generic EnKF formula is used for R localization; the differences in accuracy and balance metrics between LETKF and EnSRF R localization for this experiment (not shown) are on the order of 1%, so the comparison is fair. The R localization has an optimal scale of L = 500 km

Full access
Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

filter (ETKF; Bishop et al. 2001 ), which does not use perturbed observations, as the operational EPS ( Bowler et al. 2008 ); the system is the Met Office Global and Regional Ensemble Prediction System (MOGREPS). EnKF assimilates observations to generate ensemble initial conditions. It has two goals: 1) to obtain an accurate ensemble mean analysis and 2) to obtain ensemble perturbations well representing the analysis uncertainty. Canadian EPS uses EnKF for both aspects, whereas MOGREPS uses EnKF

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

once. Similarly, the estimated analysis error variance, , at t n is The iteration starts with and . If there is only one iteration, and give the same analysis and error variance as the KF solution. In the following perfect model experiment, we choose C = 1.25 but the results are rather insensitive to this choice. The truth starts from x 0 = 0, and an observation is created at every time step with an error variance of 1. The initial conditions at t = 0 for the analysis value and

Full access
Tijana Janjić, Lars Nerger, Alberta Albertella, Jens Schröter, and Sergey Skachko

the localization methods discussed in the previous sections with a small dynamical model, data assimilation experiments with the Lorenz-40 dynamical system of Lorenz and Emanuel (1998) were performed. This nonlinear model has been used to assess ensemble-based assimilation schemes in a number of studies (e.g., Whitaker and Hamill 2002 ; Ott et al. 2004 ; Sakov and Oke 2008 ). The model is governed by 40 coupled ordinary differential equations in a domain with cyclic boundary conditions. The

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

imprecise initial state of the system. It could also stem from the more or less precise identification of forcings of the dynamical systems, such as emission fields (in atmospheric chemistry), radiative forcing, boundary conditions, and couplings to other models that may be imperfect. The deficiency of the model itself is another source of uncertainty. To account for this type of uncertainty, models could explicitly be made probabilistic. This occurs when some stochastic forcing is implemented to

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

associated eddies. Here, we consider a flat bottom square domain [0, L ] × [ y 0 − L /2, y 0 + L /2], where y 0 is the value of y at the center of the basin. The governing equations are where ( u , υ ) is the horizontal velocity and h is the water layer thickness. The system is closed with impermeability and no-slip boundary conditions: u = υ = 0. The g * is a reduced gravity and ρ 0 is the water density; f is the Coriolis parameter, given by the β -plane approximation; f ( y

Full access