1 / 7

Ensemble data assimilation and model error estimation algorithm Developed by

Ensemble data assimilation and model error estimation algorithm Developed by Milija Zupanski and Dusanka Zupanski CIRA/CSU. GEOS column model component of the algorithm developed in collaboration with: Arthur Hou and Sara Zhang NASA/GMAO Christian Kummerow CSU/ATM Science Department.

lecea
Download Presentation

Ensemble data assimilation and model error estimation algorithm Developed by

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Ensemble data assimilation and model error estimation algorithm Developed by Milija Zupanski and Dusanka Zupanski CIRA/CSU GEOS column model component of the algorithm developed in collaboration with: Arthur Hou and Sara Zhang NASA/GMAO Christian Kummerow CSU/ATM Science Department Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

  2. Ensemble Data Assimilation (EnsDA) Algorithm prep_ensda.sh WARM start: Copy files from previously completed cycle COLD start: Run randomly-perturbed ensemble forecasts to initialize fcst err cov cycle_ensda.sh icycle < N_cycles_max fcsterr_cov.sh - Prepare first-guess (background) vector - Prepare forecast error covariance (from ensembles) prep_obs.sh Given ‘OBSTYPE’ and ‘delobs’, select and copy available obs files assimilation.sh Iterative minimization of cost function, save current cycle output post.sh (post-processing) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

  3. Ensemble Data Assimilation (EnsDA) Algorithm assimilation.sh script assimilation.sh iter < ioutmax forward.sh: Transformation from model space to observation space Hessian preconditioning (only for iter=1) Gradient calculation (ensembles) Cost function calculation (diagnostic) Minimization (ensemble subspace) Step-length (line-search) Control variable update (transformation from ensemble subspace to model (physical) space) - Analysis error covariance calculation - Save current cycle output files Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

  4. Ensemble Data Assimilation (EnsDA) Algorithm Options – envir.sh MODEL: KdVB, GEOS, WRF, … OBS: OBSTYPE, delobs,OBSFLAG STAT: Mode (max likelihood), Mean (ensemble mean) DA_TYPE: Filter, Smoother COVAR: Localized, Non-localized forecast error covariance N_CYCLES: Number of data assimilation cycles CYCLE_INTERVAL: Time interval between cycles ENS_SIZE: Number of ensemble members START: Cold (from scratch), Warm (from previous cycle) IOUTMAX: Number of minimization iterations MINIM_ALG: Minimization algorithm (C-G, L-BFGS) MPI_RUN: Parallel run (YES, NO) nCPU_ensfcst: No. of CPUs for ensemble fcst nCPU_da: No. of CPUs for data assimilation Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

  5. Ensemble Data Assimilation (EnsDA) Algorithm Control variables Optional control variable components: - initial conditions - model bias - empirical parameters ../include/Cntrl_vrbl_list.h !--------------------------------------- max_num_of_cntrl_vrbls = 5 if(.not.allocated (cvar_list)) then allocate (cvar_list(1:max_num_of_cntrl_vrbls)) end if !--------------------------------------- ncv= 1 cvar_list(ncv)%ndim = 3 cvar_list(ncv)%start_index(1) = 1 cvar_list(ncv)%start_index(2) = 1 cvar_list(ncv)%start_index(3) = 1 cvar_list(ncv)%start_index(4) = 1 cvar_list(ncv)%end_index(1) = NNXP(1) cvar_list(ncv)%end_index(2) = NNYP(1) cvar_list(ncv)%end_index(3) = NNZP(1) cvar_list(ncv)%end_index(4) = 1 cvar_list(ncv)%name = 'T ' cvar_list(ncv)%stddev = 1.5 cvar_list(ncv)%description = 'Temperature ‘ !--------------------------------------- ../namelists/Cntrl_vrbl_’MODEL’.h 6 p T F F 0 t T F T 1 q T F T 3 u T F F 0 v T F F 0 param1 F T F 0 param2 F T F 0 !-- first line number is the number of control variables defined --! !-- vrbl name, ic flag, param flag, bias flag, number of biases ----! !--- recall that vrbl name has 9 characters !!!!! !--- All inputs have to be separated by at least one blank space !!! Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

  6. Tasks • All EnsDA codes and scripts are on PC Linux Cluster • (Ramscluster) • Cp EnsDA codes and scripts to your computer platform • The following tar files will be prepared: • EnsDA.tar and KdVB.tar • Install LAPACK on your computer • (http://www.cs.colorado.edu/~lapack/ • compile with 32 bit object code option) • Use instructions for compiling and running EnsDA Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

  7. Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

More Related