文件列表:

find_localobs.m (2312, 2010-08-26)
fmain.m (2272, 2010-08-26)
generate.m (3450, 2010-08-26)
genR.m (953, 2013-06-13)
genU.m (891, 2008-11-21)
get_obs.m (957, 2008-02-20)
get_pos.m (3580, 2008-08-25)
get_pos.rar (1172, 2019-06-11)
get_prm.m (2420, 2011-04-25)
get_prmstruct.m (12563, 2011-10-13)
get_version.m (556, 2013-06-13)
LICENSE (1189, 2009-02-27)
main.m (13121, 2011-10-13)
main1 ecg kalman.rar (3556, 2019-06-11)
plot_diag.m (6597, 2013-06-13)
plot_ens.m (1221, 2013-06-13)
plot_prm.m (2227, 2013-06-13)
plot_state.m (1488, 2013-06-13)
prm2info.m (1479, 2008-02-20)
readprmfile.m (2885, 2011-10-13)
setpath.m (3681, 2011-10-13)
calc_r.m (765, 2008-08-25)
calc_stats.m (12466, 2011-05-13)
calc_stats.rar (2723, 2019-06-11)
CHANGELOG (11185, 2013-06-13)

%% Copyright (C) 2008,2009 Pavel Sakov %% %% This file is part of EnKF-Matlab. EnKF-Matlab is a free software. See %% LICENSE for details. EnKF-Matlab Matlab code for Ensemble Kalman Filter, with a number of simple models Version 0.31 Last update: 12 June 2013 Please send comments and bug reports to Pavel Sakov: pavel.sakovatgmail.com. 1. EnKF-Matlab originally was a rather loose code that emerged over a number of years in the course of experiments with Ensemble Kalman Filter (EnKF). In particular, this code or its predecessors have been used in work on the following papers: * P. R. Oke, P. Sakov, and S. P. Corney. Impacts of localisation in the EnKF and EnOI: Experiments with a small model. Ocean Dynamics, 2006, 57, 32-45. http://www.cmar.csiro.au/staff/oke/pubs/Oke_et_al_2006_Ocean_Dynamics.pdf * P. Sakov and P. R. Oke. Implications of the form of the ensemble transformation in the ensemble square root filters. Mon. Wea. Rev., 2008, 136, 1042-1053. http://enkf.nersc.no/Publications/sak08b.pdf * P. Sakov and P. R. Oke. A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters. Tellus, 2008, 60A, 361-371. http://enkf.nersc.no/Publications/sak08a.pdf * Sakov, P., G. Evensen, and L. Bertino Asynchronous data assimilation with the EnKF. Tellus 62A, 2010, 24-29. In the last year or two (2008-2009), this code has been brought to a structurally decent shape. In 2009-2010 it evolved to incorporate assimilation of asynchronous observations. 2. EnKF-Matlab is designed primarily as a research and study tool. As a consequence, speed and memory usage are given a lower priority than simplicity and ease of use. In particular, the code structure has been designed to maximally simplifiy the representation of the analysis schemes in assimilate.m. 3. Available filters (analysis schemes): EnKF -- perturbed observations EnKF (Evensen 1994, Burgers et al. 19***) ETKF -- Ensemble Transform Kalman Filter (Bishop et al. 2001) LESRF* -- Left-multiplied ESRF (Sakov & Oke 2008) (effective when m > n) EnOI -- Ensemble Optimal Interpolation (?) Potter -- serial Ensemble Square Root Filter (Whitaker & Hamill 2002) DEnKF -- "deteministic" EnKF (Sakov & Oke 2008) * The LESRF scheme has been taken out in version 0.28, in which the old (synchronous) code has been deprecated. 4. Available models: - LA -- linear advection model (Evensen 2004) Represents a fixed profile (can be interpreted as tracer concentration) composed from a number of harmonics with random amplitudes, propagated by 1 cell at each time step - LA2 -- linear advection model with two variables (Oke et al. 2006) Same as LA, except that a secondary variable that is (initially) a derivative of the first (primary) variable is added to study dynamic balance between the variables during assimilation. - L3 -- 3-dimensional Lorenz model (Lorenz 1965) - L40 -- 40-dimensional Lorenz model (Lorenz 1996) - L40p -- as above, with varying forcing as a parameter Designed to demonstrate parameter estimation with ensemble Kalman filters. - S -- Sound model (-) Similar to LA, except that two profiles are generated, moving in opposite directions. A linear, periodic in time model, but not static like LA or LA2. - QG -- Quasigeostrophic 1.5-layer reduced gravity model (-) 127 x 127 QG model (excluding the boundary) implemented in Fortran90. model | linear | static | phys. dim. | state vector dim. | model subspace dim. LA Yes Yes 1D 1000 51 LA2 Yes Yes 1D 2000 51 S Yes No 1D 1000 102 L3 No No 1D 3 about 2 L40 No No 1D 40 13+ L40p No No 1D 41 varying, around 13+ L40b No No 1D 41 about 13+ QG No No 2D 16***1/4225/1089 a few hundred (?) All models but QG are implemented in Matlab. For L3 and L40 models there is also C code that, once compiled with the mex compiler, increases the effectiveness of the system by a significant factor. Beware that the compiled code has fewer options that the Matlab code (the model step can only be a single step of the RK4 integrartor.) To run QG model, one has to compile the Fortran code or use the pre-compiled binaries. Another significant factor that may affect the effectiveness is the graphical output, which can be turned off by setting plot_diag 0 plot_state 0 in the parameter file. 5. To run a quick test, in Matlab run one of the following commands: >> [x, x_true, E, stats] = fmain('prm/prm-L3-m=2.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-L3-m=3.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-L40-etkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-L40-etkf-loc.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-L40p.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-L40b.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-LA.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-LA2.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-S.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QG-denkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QG-etkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QG-potter.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QG-enoi.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QG-enkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QGs-denkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QGs-enkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QGt-denkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QGt-etkf.txt'); >> [x, x_true, E, stats] = fmain('prm/prm-QGt-potter.txt'); (See README in prm directory.) Note that you will have to create or download samples for QG and QGs (small QG) models to run these systems; however you must be able to run QGt (tiny QG) model out of the box. 6. The DA system is run by calling fmain() (that reads parameters from a file) or main() (that reads parameters from a structure). main() may be more convenient to call when running batch jobs. But see the headers of fmain.m and main.m for more details. 7. A description of the common (among all models) fields in the parameter file can be found in the header of get_prmstruct.m. Model-specific parameters are handled by model-specific code in models//model_getprmstruct.m. 8. The code directory structure is as follows: enkf-matlab - contains generic code, including launching, running, plotting, reading parameters, and calculating some common tasks. enkf-matlab/bin - compiled code (binaries) for some architectures enkf-matlab/models - model-specific code for a number of models enkf-matlab/models/common - code shared by several models enkf-matlab/models/ - model-specific code enkf-matlab/prm - some sample parameter files enkf-matlab/samples - files with collection of model dumps for some model used for the initial enesmble enkf-matlab/tools - some analysis/plotting tools not used directly by EnKF code 9. This software has been created under/for *nux, but should be portable to run under Windows. If the system does not find a compiled version for L40 or L3 models it then tries to run the (slower) Matlab code. 10. If you manage to compile binaries for some model(s) that are not in the package - I would much appreciate if you shared those. 11. If you use EnKF-Matlab and find it useful, please drop me a line. 12. The author gratefully acknowledge funding from the eVITA-EnKF project by the Research Council of Norway while developing the EnKF-Matlab code in the period from December 2007 to September 2009. 13. Please acknowledge use of this software in publications.

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