DART Documentation Main Index |
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_vars_nml 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 |
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 private 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.
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) LDFLAGS = $(FFLAGS) $(LIBS)
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 mpas_analysis.nc) 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 mpas_analysis.nc netcdf mpas_analysis { dimensions: 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 variables: 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: 'mpas_analysis.nc'] |
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_nml model_analysis_filename = 'mpas_restart.nc'; 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_vars_nml mpas_state_variables = 'theta', 'KIND_POTENTIAL_TEMPERATURE', 'uReconstructZonal', 'KIND_U_WIND_COMPONENT', 'uReconstructMeridional','KIND_V_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.
types_mod time_manager_mod threed_sphere/location_mod utilities_mod obs_kind_mod mpi_utilities_mod random_seq_mod
Only a select number of interfaces used are discussed here. Each module has its own discussion of their routines.
use model_mod, only : | get_gridsize |
restart_file_to_sv | |
sv_to_restart_file | |
get_gitm_restart_filename | |
get_base_time | |
get_state_time |
use location_mod, only : | get_close_obs |
A note about documentation style. Optional arguments are enclosed in brackets [like this].
integer :: get_model_size
Returns the length of the model state vector. Required.
model_size | The length of the model state vector. |
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. |
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.
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 |
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. |
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:
input.nml&model_mod_nml,
gitm_in&time_manager_nml,
gitm_in&io_nml,
gitm_in&init_ts_nml,
gitm_in&restart_nml,
gitm_in&domain_nml, and
gitm_in&grid_nml.
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.
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 |
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 |
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: True_State.nc, Prior_Diag.nc, and Posterior_Diag.nc.
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. |
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. |
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.
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.
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. |
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. |
integer, intent(out) :: num_x, num_y, num_z
get_gridsize returns the dimensions of the compute domain. The horizontal gridsize is determined from gitm_restart.nc.
num_x | The number of longitudinal gridpoints. |
num_y | The number of latitudinal gridpoints. |
num_z | The number of vertical gridpoints. |
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. |
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. |
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. |
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.
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 |
gitm_restart.nc | provides grid dimensions, model state, and 'valid_time' of the model state |
True_State.nc | the time-history of the "true" model state from an OSSE |
Prior_Diag.nc | the time-history of the model state before assimilation |
Posterior_Diag.nc | 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 |
Routine | Message | Comment |
---|---|---|
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()
N/A
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
http://www.image.ucar.edu/DAReS/DART/DART_download
Contact: | DART core group |
Revision: | $Revision: 6336 $ |
Source: | $URL: https://svn-dares-dart.cgd.ucar.edu/DART/releases/Lanai/models/mpas_ocn/model_mod.html $ |
Change Date: | $Date: 2013-07-30 17:01:31 -0600 (Tue, 30 Jul 2013) $ |
Change history: | try "svn log" or "svn diff" |