MODULE model_mod (MPAS OCN)

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$Id: model_mod.html 6336 2013-07-30 23:01:31Z nancy $



The MPAS OCN interface for Data Assimilation Research Testbed (DART) is under development.

Since MPAS OCN uses netcdf files for their restart mechanism, a namelist-controlled set of variables is used to build the DART state vector. Each variable must also correspond to a DART "KIND"; required for the DART interpolate routines. For example:

   mpas_state_variables = 'uReconstructZonal',      'KIND_U_WIND_COMPONENT',
                          'uReconstructMeridional', 'KIND_V_WIND_COMPONENT',
                          'w',                      'KIND_VERTICAL_VELOCITY',
                          'theta',                  'KIND_POTENTIAL_TEMPERATURE',
                          'qv',                     'KIND_VAPOR_MIXING_RATIO',
                          'qc',                     'KIND_CLOUDWATER_MIXING_RATIO',
                          'qr',                     'KIND_RAINWATER_MIXING_RATIO',
                          'qi',                     'KIND_ICE_MIXING_RATIO',
                          'qs',                     'KIND_SNOW_MIXING_RATIO',
                          'qg',                     'KIND_GRAUPEL_MIXING_RATIO',
                          'surface_pressure',       'KIND_SURFACE_PRESSURE'

These variables are then adjusted to be consistent with observations and stuffed back into the same netCDF analysis files. Since DART is an ensemble algorithm, there are multiple analysis files for a single analysis time: one for each ensemble member. Creating the initial ensemble of states is an area of active research.

DART reads grid information from the MPAS OCN 'history' file, I have tried to keep the variable names the same. Internal to the DART code, the following variables exist:

integer :: nCells the number of Cell Centers
integer :: nEdges the number of Cell Edges
integer :: nVertices the number of Cell Vertices
integer :: nVertLevels the number of vertical level midpoints
integer :: nVertLevelsP1 the number of vertical level edges
integer :: nSoilLevels the number of soil level ?midpoints?
real(r8) :: latCell(:) the latitudes of the Cell Centers (-90,90)
real(r8) :: lonCell(:) the longitudes of the Cell Centers [0, 360)
real(r8) :: zgrid(:,:) cell center geometric height at cell centers (ncells,nvert)
integer :: CellsOnVertex(:,:) list of cell centers defining a triangle

model_mod variable storage

input.nml&mpas_vars_nml defines the list of MPAS variables used to build the DART state vector. Combined with an MPAS analysis file, the information is used to determine the size of the DART state vector and derive the metadata. To keep track of what variables are contained in the DART state vector, an array of a user-defined type called "progvar" is available with the following components:

type progvartype
   character(len=NF90_MAX_NAME) :: varname
   character(len=NF90_MAX_NAME) :: long_name
   character(len=NF90_MAX_NAME) :: units
   character(len=NF90_MAX_NAME), dimension(NF90_MAX_VAR_DIMS) :: dimname
   integer, dimension(NF90_MAX_VAR_DIMS) :: dimlens
   integer :: xtype         ! netCDF variable type (NF90_double, etc.) 
   integer :: numdims       ! number of dims - excluding TIME
   integer :: numvertical   ! number of vertical levels in variable
   integer :: numcells      ! number of horizontal locations (typically cell centers)
   logical :: ZonHalf       ! vertical coordinate has dimension nVertLevels
   integer :: varsize       ! prod(dimlens(1:numdims))
   integer :: index1        ! location in dart state vector of first occurrence
   integer :: indexN        ! location in dart state vector of last  occurrence
   integer :: dart_kind
   character(len=paramname_length) :: kind_string
   logical  :: clamping     ! does variable need to be range-restricted before 
   real(r8) :: range(2)     ! being stuffed back into MPAS analysis file.
end type progvartype

type(progvartype), dimension(max_state_variables) :: progvar

The variables are simply read from the MPAS analysis file and stored in the DART state vector such that all quantities for one variable are stored contiguously. Within each variable; they are stored vertically-contiguous for each horizontal location. From a storage standpoint, this would be equivalent to a Fortran variable dimensioned x(nVertical,nHorizontal,nVariables). The fastest-varying dimension is vertical, then horizontal, then variable ... naturally, the DART state vector is 1D. Each variable is also stored this way in the MPAS analysis file.

The DART interface for MPAS (atm)

was compiled with the gfortran 4.2.3 compilers and run on a Mac.

The DART components were built with the following mkmf.template settings:

      FC = gfortran
      LD = gfortran
      NETCDF = /Users/thoar/GNU
      INCS = -I${NETCDF}/include
      LIBS = -L${NETCDF}/lib -lnetcdf -lcurl -lhdf5_hl -lhdf5 -lz  -lm
      FFLAGS = -O0 -fbounds-check -frecord-marker=4 -ffpe-trap=invalid $(INCS)

Converting between DART files and MPAS analysis files

is relatively straighforward. Given the namelist mechanism for determining the state variables and the MPAS history netCDF files exist, - everything that is needed is readily determined.

There are two programs - both require the list of MPAS variables to use in the DART state vector: the mpas_vars_nml namelist in the input.nml file. The MPAS file name being read and/or written is - in all instances - specified by the model_nml:model_analysis_filename variable in the input.nml namelist file.

model_to_dart.f90 converts an MPAS analysis file (nominally named into a DART-compatible file normally called dart_ics . We usually wind up linking the actual analysis file to a static name that is used by DART.
dart_to_model.f90 inserts the DART output into an existing MPAS analysis netCDF file by overwriting the variables in the analysis netCDF file. There are two different types of DART output files, so there is a namelist option to specify if the DART file has two time records or just one (if there are two, the first one is the 'advance_to' time, followed by the 'valid_time' of the ensuing state). dart_to_model updates the MPAS analysis file specified in input.nmlmodel_nml:model_analysis_filename. If the DART file contains an 'advance_to' time, separate control information is written to an auxiliary file that is used by the advance_model.csh script.

The header of an MPAS analysis file is presented below - simply for context. Keep in mind that many variables have been removed for clarity. Also keep in mind that the multi-dimensional arrays listed below have the dimensions reversed from the Fortran convention.

366 mirage2:thoar% ncdump -h
netcdf mpas_analysis {
        StrLen = 64 ;
        Time = UNLIMITED ; // (1 currently)
        nCells = 10242 ;                                  available in DART
        nEdges = 30720 ;                                  available in DART
        maxEdges = 10 ;
        maxEdges2 = 20 ;
        nVertices = 20480 ;                               available in DART
        TWO = 2 ;
        THREE = 3 ;
        vertexDegree = 3 ;                                available in DART
        FIFTEEN = 15 ;
        TWENTYONE = 21 ;
        R3 = 3 ;
        nVertLevels = 41 ;                                available in DART
        nVertLevelsP1 = 42 ;                              available in DART
        nMonths = 12 ;
        nVertLevelsP2 = 43 ;
        nSoilLevels = 4 ;                                 available in DART
        char xtime(Time, StrLen) ;                        available in DART
        double latCell(nCells) ;                          available in DART
        double lonCell(nCells) ;                          available in DART
        double latEdge(nEdges) ;
        double lonEdge(nEdges) ;
        int indexToEdgeID(nEdges) ;
        double latVertex(nVertices) ;
        double lonVertex(nVertices) ;
        int indexToVertexID(nVertices) ;
        int cellsOnEdge(nEdges, TWO) ;
        int nEdgesOnCell(nCells) ;
        int nEdgesOnEdge(nEdges) ;
        int edgesOnCell(nCells, maxEdges) ;
        int edgesOnEdge(nEdges, maxEdges2) ;
        double weightsOnEdge(nEdges, maxEdges2) ;
        double dvEdge(nEdges) ;
        double dcEdge(nEdges) ;
        double angleEdge(nEdges) ;
        double edgeNormalVectors(nEdges, R3) ;
        double cellTangentPlane(nEdges, TWO, R3) ;
        int cellsOnCell(nCells, maxEdges) ;
        int verticesOnCell(nCells, maxEdges) ;
        int verticesOnEdge(nEdges, TWO) ;
        int edgesOnVertex(nVertices, vertexDegree) ;
        int cellsOnVertex(nVertices, vertexDegree) ;      available in DART
        double kiteAreasOnVertex(nVertices, vertexDegree) ;
        double rainc(Time, nCells) ;
        double cuprec(Time, nCells) ;
        double cutop(Time, nCells) ;
        double cubot(Time, nCells) ;
        double relhum(Time, nCells, nVertLevels) ;
        double qsat(Time, nCells, nVertLevels) ;
        double graupelnc(Time, nCells) ;
        double snownc(Time, nCells) ;
        double rainnc(Time, nCells) ;
        double graupelncv(Time, nCells) ;
        double snowncv(Time, nCells) ;
        double rainncv(Time, nCells) ;
        double sr(Time, nCells) ;
        double surface_temperature(Time, nCells) ;
        double surface_pressure(Time, nCells) ;
        double coeffs_reconstruct(nCells, maxEdges, R3) ;
        double theta_base(Time, nCells, nVertLevels) ;
        double rho_base(Time, nCells, nVertLevels) ;
        double pressure_base(Time, nCells, nVertLevels) ;
        double exner_base(Time, nCells, nVertLevels) ;
        double exner(Time, nCells, nVertLevels) ;
        double h_divergence(Time, nCells, nVertLevels) ;
        double uReconstructMeridional(Time, nCells, nVertLevels) ;
        double uReconstructZonal(Time, nCells, nVertLevels) ;
        double uReconstructZ(Time, nCells, nVertLevels) ;
        double uReconstructY(Time, nCells, nVertLevels) ;
        double uReconstructX(Time, nCells, nVertLevels) ;
        double pv_cell(Time, nCells, nVertLevels) ;
        double pv_vertex(Time, nVertices, nVertLevels) ;
        double ke(Time, nCells, nVertLevels) ;
        double rho_edge(Time, nEdges, nVertLevels) ;
        double pv_edge(Time, nEdges, nVertLevels) ;
        double vorticity(Time, nVertices, nVertLevels) ;
        double divergence(Time, nCells, nVertLevels) ;
        double v(Time, nEdges, nVertLevels) ;
        double rh(Time, nCells, nVertLevels) ;
        double theta(Time, nCells, nVertLevels) ;
        double rho(Time, nCells, nVertLevels) ;
        double qv_init(nVertLevels) ;
        double t_init(nCells, nVertLevels) ;
        double u_init(nVertLevels) ;
        double pressure_p(Time, nCells, nVertLevels) ;
        double tend_theta(Time, nCells, nVertLevels) ;
        double tend_rho(Time, nCells, nVertLevels) ;
        double tend_w(Time, nCells, nVertLevelsP1) ;
        double tend_u(Time, nEdges, nVertLevels) ;
        double qv(Time, nCells, nVertLevels) ;
        double qc(Time, nCells, nVertLevels) ;
        double qr(Time, nCells, nVertLevels) ;
        double qi(Time, nCells, nVertLevels) ;
        double qs(Time, nCells, nVertLevels) ;
        double qg(Time, nCells, nVertLevels) ;
        double tend_qg(Time, nCells, nVertLevels) ;
        double tend_qs(Time, nCells, nVertLevels) ;
        double tend_qi(Time, nCells, nVertLevels) ;
        double tend_qr(Time, nCells, nVertLevels) ;
        double tend_qc(Time, nCells, nVertLevels) ;
        double tend_qv(Time, nCells, nVertLevels) ;
        double qnr(Time, nCells, nVertLevels) ;
        double qni(Time, nCells, nVertLevels) ;
        double tend_qnr(Time, nCells, nVertLevels) ;
        double tend_qni(Time, nCells, nVertLevels) ;



We adhere to the F90 standard of starting a namelist with an ampersand '&' and terminating with a slash '/' for all our namelist input. Consider yourself forewarned that character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely terminating the namelist.

namelist /model_nml/  model_analysis_filename, &
          assimilation_period_days, assimilation_period_seconds, &
          model_perturbation_amplitude, output_state_vector, calendar, debug

This namelist is read in a file called input.nml. This namelist provides control over the assimilation period for the model. All observations within (+/-) half of the assimilation period are assimilated. The assimilation period is the minimum amount of time the model can be advanced, and checks are performed to ensure that the assimilation window is a multiple of the model dynamical timestep. This also specifies the MPAS analysis file that will be read and/or written by the different program units.

Contents Type Description
model_analysis_filename character(len=256)
[default: '']
Character string specifying the name of the MPAS analysis file to be read and/or written by the different program units.
output_state_vector logical [default: .true.] The switch to determine the form of the state vector in the output netCDF files. If .true. the state vector will be output exactly as DART uses it ... one long array. If .false., the state vector is parsed into prognostic variables and output that way -- much easier to use with 'ncview', for example.
assimilation_period_days integer [default: 1] The number of days to advance the model for each assimilation.
assimilation_period_seconds integer [default: 0] In addition to assimilation_period_days, the number of seconds to advance the model for each assimilation.
model_perturbation_amplitude real(r8) [default: 0.2] Reserved for future use.
calendar character(len=32)
[default: 'Gregorian']
Character string specifying the calendar being used by MPAS.
debug integer [default: 0] The switch to specify the run-time verbosity. 0 is as quiet as it gets. > 1 provides more run-time messages. > 5 provides ALL run-time messages.


   model_analysis_filename      = '';
   assimilation_period_days     = 0,
   assimilation_period_seconds  = 60,
   model_perturbation_amplitude = 0.2,
   output_state_vector          = .true.,
   calendar                     = 'Gregorian',
   debug                        = 0

namelist /mpas_vars_nml/ mpas_state_variables

This namelist is read from input.nml and contains the list of MPAS variables that make up the DART state vector.

Contents Type Description
mpas_vars_nml character(len=NF90_MAX_NAME)::
dimension(160) [default:  see example]
The table that relates the GITM variables to use to build the DART state vector, and the corresponding DART kinds for those variables.


The following mpas_vars_nml is just for demonstration purposes. You application will likely involve a different DART state vector.

   mpas_state_variables = 'theta',                 'KIND_POTENTIAL_TEMPERATURE',
                          'uReconstructZonal',     'KIND_U_WIND_COMPONENT',
                          'w',                     'KIND_VERTICAL_VELOCITY',
                          'qv',                    'KIND_VAPOR_MIXING_RATIO',
                          'qc',                    'KIND_CLOUDWATER_MIXING_RATIO',
                          'qr',                    'KIND_RAINWATER_MIXING_RATIO',
                          'qi',                    'KIND_ICE_MIXING_RATIO',
                          'qs',                    'KIND_SNOW_MIXING_RATIO',
                          'qg',                    'KIND_GRAUPEL_MIXING_RATIO'
                          'surface_pressure',      'KIND_SURFACE_PRESSURE'

The variables are simply read from the MPAS analysis file and stored in the DART state vector such that all quantities for one variable are stored contiguously. Within each variable; they are stored vertically-contiguous for each horizontal location. From a storage standpoint, this would be equivalent to a Fortran variable dimensioned x(nVertical,nHorizontal,nVariables). The fastest-varying dimension is vertical, then horizontal, then variable ... naturally, the DART state vector is 1D. Each variable is also stored this way in the MPAS analysis file.




Everything below here is complete fiction

Everything below here is complete fiction

Everything below here is complete fiction



Only a select number of interfaces used are discussed here. Each module has its own discussion of their routines.

Required Interface Routines

use model_mod, only : get_model_size

Unique Interface Routines

use model_mod, only : get_gridsize
use location_mod, only : get_close_obs

A note about documentation style. Optional arguments are enclosed in brackets [like this].

Required Interface Routines

model_size = get_model_size( )
integer :: get_model_size

Returns the length of the model state vector. Required.

model_size The length of the model state vector.

call adv_1step(x, time)
real(r8), dimension(:), intent(inout) :: x
type(time_type),        intent(in)    :: time

adv_1step is not used for the gitm model. Advancing the model is done through the advance_model script. This is a NULL_INTERFACE, provided only for compatibility with the DART requirements.

x State vector of length model_size.
time    Specifies time of the initial model state.

call get_state_meta_data (index_in, location, [, var_type] )
integer,             intent(in)  :: index_in
type(location_type), intent(out) :: location
integer, optional,   intent(out) ::  var_type 

get_state_meta_data returns metadata about a given element of the DART representation of the model state vector. Since the DART model state vector is a 1D array and the native model grid is multidimensional, get_state_meta_data returns information about the native model state vector representation. Things like the location, or the type of the variable (for instance: temperature, u wind component, ...). The integer values used to indicate different variable types in var_type are themselves defined as public interfaces to model_mod if required.

index_in    Index of state vector element about which information is requested.
location Returns the 3D location of the indexed state variable. The location_ type comes from DART/location/threed_sphere/location_mod.f90. Note that the lat/lon are specified in degrees by the user but are converted to radians internally.
var_type Returns the type of the indexed state variable as an optional argument. The type is one of the list of supported observation types, found in the block of code starting ! Integer definitions for DART TYPES in DART/obs_kind/obs_kind_mod.f90

The list of supported variables in DART/obs_kind/obs_kind_mod.f90 is created by preprocess.

call model_interpolate(x, location, itype, obs_val, istatus)
real(r8), dimension(:), intent(in)  :: x
type(location_type),    intent(in)  :: location
integer,                intent(in)  :: itype
real(r8),               intent(out) :: obs_val
integer,                intent(out) :: istatus

Given a model state, model_interpolate returns the value of the desired observation type (which could be a state variable) that would be observed at the desired location. The interpolation method is either completely specified by the model, or uses some standard 2D or 3D scalar interpolation routines. Put another way, model_interpolate will apply the forward operator H to the model state to create an observation at the desired location.

If the interpolation is valid, istatus = 0. In the case where the observation operator is not defined at the given location (e.g. the observation is below the lowest model level, above the top level, or 'dry'), interp_val is returned as 0.0 and istatus = 1.

x A model state vector.
location    Location to which to interpolate.
itype Integer indexing which type of observation is desired.
obs_val The interpolated value from the model.
istatus Integer flag indicating the success of the interpolation.
success == 0, failure == anything else

var = get_model_time_step()
type(time_type) :: get_model_time_step

get_model_time_step returns the forecast length to be used as the "model base time step" in the filter. This is the minimum amount of time the model can be advanced by filter. This is also the assimilation window. All observations within (+/-) one half of the forecast length are used for the assimilation. In the GITM case, this is set from the namelist values for input.nml&model_nml:assimilation_period_days, assimilation_period_seconds.

var    Smallest time step of model.

call static_init_model()

static_init_model is called for runtime initialization of the model. The namelists are read to determine runtime configuration of the model, the grid coordinates, etc. There are no input arguments and no return values. The routine sets module-local private attributes that can then be queried by the public interface routines.

See the GITM documentation for all namelists in gitm_in . Be aware that DART reads the GITM &grid_nml namelist to get the filenames for the horizontal and vertical grid information as well as the topography information.

The namelists (all mandatory) are:
gitm_in&domain_nml, and

call end_model()

end_model is used to clean up storage for the model, etc. when the model is no longer needed. There are no arguments and no return values. The grid variables are deallocated.

call init_time(time)
type(time_type), intent(out) :: time

init_time returns the time at which the model will start if no input initial conditions are to be used. This is frequently used to spin-up models from rest, but is not meaningfully supported for the GITM model. The only time this routine would get called is if the input.nml&perfect_model_obs_nml:start_from_restart is .false., which is not supported in the GITM model.

time    the starting time for the model if no initial conditions are to be supplied. This is hardwired to 0.0

call init_conditions(x)
real(r8), dimension(:), intent(out) :: x

init_conditions returns default initial conditions for model; generally used for spinning up initial model states. For the GITM model it is just a stub because the initial state is always provided by the input files.

x    Initial conditions for state vector. This is hardwired to 0.0

ierr = nc_write_model_atts(ncFileID)
integer             :: nc_write_model_atts
integer, intent(in) :: ncFileID

nc_write_model_atts writes model-specific attributes to an opened netCDF file: In the GITM case, this includes information like the coordinate variables (the grid arrays: ULON, ULAT, TLON, TLAT, ZG, ZC, KMT, KMU), information from some of the namelists, and either the 1D state vector or the prognostic variables (SALT,TEMP,UVEL,VVEL,PSURF). All the required information (except for the netCDF file identifier) is obtained from the scope of the model_mod module. Both the input.nml and gitm_in files are preserved in the netCDF file as variables inputnml and gitm_in, respectively.

ncFileID    Integer file descriptor to previously-opened netCDF file.
ierr Returns a 0 for successful completion.

nc_write_model_atts is responsible for the model-specific attributes in the following DART-output netCDF files:,, and

ierr = nc_write_model_vars(ncFileID, statevec, copyindex, timeindex)
integer,                intent(in) :: ncFileID
real(r8), dimension(:), intent(in) :: statevec
integer,                intent(in) :: copyindex
integer,                intent(in) :: timeindex
integer                            :: ierr

nc_write_model_vars writes a copy of the state variables to a NetCDF file. Multiple copies of the state for a given time are supported, allowing, for instance, a single file to include multiple ensemble estimates of the state. Whether the state vector is parsed into prognostic variables (SALT, TEMP, UVEL, VVEL, PSURF) or simply written as a 1D array is controlled by input.nml&model_mod_nml:output_state_vector. If output_state_vector = .true. the state vector is written as a 1D array (the simplest case, but hard to explore with the diagnostics). If output_state_vector = .false. the state vector is parsed into prognostic variables before being written.

ncFileID file descriptor to previously-opened netCDF file.
statevec A model state vector.
copyindex    Integer index of copy to be written.
timeindex The timestep counter for the given state.
ierr Returns 0 for normal completion.

call pert_model_state(state, pert_state, interf_provided)
real(r8), dimension(:), intent(in)  :: state
real(r8), dimension(:), intent(out) :: pert_state
logical,                intent(out) :: interf_provided

Given a model state, pert_model_state produces a perturbed model state. This is used to generate ensemble initial conditions perturbed around some control trajectory state when one is preparing to spin-up ensembles. Since the DART state vector for the GITM model contains both 'wet' and 'dry' cells, it is imperative to provide an interface to perturb just the wet cells (interf_provided == .true.).

The magnitude of the perturbation is wholly determined by input.nml&model_mod_nml:model_perturbation_amplitude and utterly, completely fails.

A more robust perturbation mechanism is needed. Until then, avoid using this routine by using your own ensemble of initial conditions. This is determined by setting input.nml&filter_nml:start_from_restart = .false.

state State vector to be perturbed.
pert_state The perturbed state vector.
interf_provided    Because of the 'wet/dry' issue discussed above, this is always .true., indicating a model-specific perturbation is available.

call get_close_maxdist_init(gc, maxdist)
type(get_close_type), intent(inout) :: gc
real(r8),             intent(in)    :: maxdist

Pass-through to the 3-D sphere locations module. See get_close_maxdist_init() for the documentation of this subroutine.

call get_close_obs_init(gc, num, obs)
type(get_close_type), intent(inout) :: gc
integer,              intent(in)    :: num
type(location_type),  intent(in)    :: obs(num)

Pass-through to the 3-D sphere locations module. See get_close_obs_init() for the documentation of this subroutine.

call get_close_obs(gc, base_obs_loc, base_obs_kind, obs, obs_kind, &
          num_close, close_ind [, dist])
type(get_close_type),              intent(in ) :: gc
type(location_type),               intent(in ) :: base_obs_loc
integer,                           intent(in ) :: base_obs_kind
type(location_type), dimension(:), intent(in ) :: obs
integer,             dimension(:), intent(in ) :: obs_kind
integer,                           intent(out) :: num_close
integer,             dimension(:), intent(out) :: close_ind
real(r8), optional,  dimension(:), intent(out) :: dist

Given a DART location (referred to as "base") and a set of locations, and a definition of 'close' - return a subset of locations that are 'close', as well as their distances to the DART location and their indices. This routine intentionally masks a routine of the same name in location_mod because we want to be able to discriminate against selecting 'dry land' locations.

Given a single location and a list of other locations, returns the indices of all the locations close to the single one along with the number of these and the distances for the close ones. The list of locations passed in via the obs argument must be identical to the list of obs passed into the most recent call to get_close_obs_init(). If the list of locations of interest changes, get_close_obs_destroy() must be called and then the two initialization routines must be called before using get_close_obs() again.

For vertical distance computations, the general philosophy is to convert all vertical coordinates to a common coordinate. This coordinate type is defined in the namelist with the variable "vert_localization_coord".

gc Structure to allow efficient identification of locations 'close' to a given location.
base_obs_loc Single given location.
base_obs_kind Kind of the single location.
obs List of candidate locations.
obs_kind Kind associated with candidate locations.
num_close Number of locations close to the given location.
close_ind Indices of those locations that are close.
dist Distance between given location and the close ones identified in close_ind.

call ens_mean_for_model(ens_mean)
real(r8), dimension(:), intent(in) :: ens_mean

ens_mean_for_model normally saves a copy of the ensemble mean to module-local storage. This is a NULL_INTERFACE for the GITM model. At present there is no application which requires module-local storage of the ensemble mean. No storage is allocated.

ens_mean State vector containing the ensemble mean.


Unique Interface Routines

call get_gridsize( num_x, num_y, num_z )
integer, intent(out) :: num_x, num_y, num_z

get_gridsize returns the dimensions of the compute domain. The horizontal gridsize is determined from

num_x The number of longitudinal gridpoints.
num_y The number of latitudinal gridpoints.
num_z The number of vertical gridpoints.

call restart_file_to_sv(filename, state_vector, model_time)
character(len=*),       intent(in)    :: filename
real(r8), dimension(:), intent(inout) :: state_vector
type(time_type),        intent(out)   :: model_time

restart_file_to_sv Reads a GITM netCDF format restart file and packs the desired variables into a DART state vector. The desired variables are specified in the gitm_vars_nml namelist.

filename The name of the netCDF format GITM restart file.
state_vector the 1D array containing the concatenated GITM variables.
model_time the time of the model state. The last time in the netCDF restart file.

call sv_to_restart_file(state_vector, filename, statedate)
real(r8), dimension(:), intent(in) :: state_vector
character(len=*),       intent(in) :: filename
type(time_type),        intent(in) :: statedate

sv_to_restart_file updates the variables in the GITM restart file with values from the DART vector state_vector. The last time in the file must match the statedate.

filename the netCDF-format GITM restart file to be updated.
state_vector the 1D array containing the DART state vector.
statedate the 'valid_time' of the DART state vector.

call get_gitm_restart_filename( filename )
character(len=*), intent(out) :: filename

get_gitm_restart_filename returns the name of the gitm restart file - the filename itself is in private module storage.

filename    The name of the GITM restart file.

time = get_base_time( filehandle )
integer,          intent(in) :: filehandle -OR-
character(len=*), intent(in) :: filehandle
type(time_type),  intent(out) :: time

get_base_time extracts the start time of the experiment as contained in the netCDF restart file. The file may be specified by either a character string or the integer netCDF fid.

time = get_state_time( filehandle )
integer,          intent(in) :: filehandle -OR-
character(len=*), intent(in) :: filehandle
type(time_type),  intent(out) :: time

get_state_time extracts the time of the model state as contained in the netCDF restart file. In the case of multiple times in the file, the last time is the time returned. The file may be specified by either a character string or the integer netCDF fid.



filename purpose
input.nml to read the model_mod namelist
gitm_vars.nml to read the gitm_vars_nml namelist provides grid dimensions, model state, and 'valid_time' of the model state the time-history of the "true" model state from an OSSE the time-history of the model state before assimilation  the time-history of the model state after assimilation
dart_log.out [default name] the run-time diagnostic output
dart_log.nml [default name] the record of all the namelists actually USED - contains the default values





restart_file_to_sv cannot open file "xxxx" for reading The GITM restart file "xxxx" does not exist.
restart_file_to_sv 'WARNING!!! year 0 not supported; setting to year 1 year 0 ... is not supported in a Gregorian calendar. Our intent here is to do data assimilation, normally 'real' observations have 'real' dates.
sv_to_restart_file current time /= model time. FATAL error. The DART time does not match the time of the GITM restart file. This message is preceeded by several lines indicating the expected times of both DART and GITM.


none at this time



Provide a better mechanism for generating a set of perturbed initial conditions - pert_model_state()





Terms of Use

DART software - Copyright 2004 - 2013 UCAR.
This open source software is provided by UCAR, "as is",
without charge, subject to all terms of use at

Contact: DART core group
Revision: $Revision: 6336 $
Source: $URL: $
Change Date: $Date: 2013-07-30 17:01:31 -0600 (Tue, 30 Jul 2013) $
Change history:  try "svn log" or "svn diff"