MPAS OCN
Attention
mpas_ocn was being developed with versions of DART before Manhattan (9.x.x) and has yet to be updated. If you are interested in
using mpas_ocn with more recent versions of DART, contact DAReS staff to assess the feasibility of an update.
Until that time, you should consider this documentation as out-of-date.
Overview
&mpas_vars_nml
mpas_state_variables = 'uReconstructZonal', 'QTY_U_WIND_COMPONENT',
'uReconstructMeridional', 'QTY_V_WIND_COMPONENT',
'w', 'QTY_VERTICAL_VELOCITY',
'theta', 'QTY_POTENTIAL_TEMPERATURE',
'qv', 'QTY_VAPOR_MIXING_RATIO',
'qc', 'QTY_CLOUDWATER_MIXING_RATIO',
'qr', 'QTY_RAINWATER_MIXING_RATIO',
'qi', 'QTY_ICE_MIXING_RATIO',
'qs', 'QTY_SNOW_MIXING_RATIO',
'qg', 'QTY_GRAUPEL_MIXING_RATIO',
'surface_pressure', 'QTY_SURFACE_PRESSURE'
/
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
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.
The DART interface for MPAS (atm)
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)
Converting between DART files and MPAS analysis files
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.converts an MPAS analysis file (nominally named |
|
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). |
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) ;
Namelist
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 |
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
|
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. |
Example namelist
&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. |
Example
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', 'QTY_POTENTIAL_TEMPERATURE',
'uReconstructZonal', 'QTY_U_WIND_COMPONENT',
'uReconstructMeridional','QTY_V_WIND_COMPONENT',
'w', 'QTY_VERTICAL_VELOCITY',
'qv', 'QTY_VAPOR_MIXING_RATIO',
'qc', 'QTY_CLOUDWATER_MIXING_RATIO',
'qr', 'QTY_RAINWATER_MIXING_RATIO',
'qi', 'QTY_ICE_MIXING_RATIO',
'qs', 'QTY_SNOW_MIXING_RATIO',
'qg', 'QTY_GRAUPEL_MIXING_RATIO'
'surface_pressure', 'QTY_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.
Other modules used
types_mod
time_manager_mod
threed_sphere/location_mod
utilities_mod
obs_kind_mod
mpi_utilities_mod
random_seq_mod
Warning
DAReS staff began creating the MPAS_OCN interface to DART in preparation for the model’s inclusion as the ocean component of the Community Earth System Model (CESM). The plans for including MPAS_OCN in CESM were abandoned and the Modular Ocean Model version 6 (MOM6) was included instead. Thus, the documentation on this page after this point describes an incomplete interface. Please contact DAReS staff by emailing dart@ucar.edu if you want to use DART with MPAS_OCN.
Public interfaces
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 |
adv_1step |
|
get_state_meta_data |
|
model_interpolate |
|
get_model_time_step |
|
static_init_model |
|
end_model |
|
init_time |
|
init_conditions |
|
nc_write_model_atts |
|
nc_write_model_vars |
|
pert_model_state |
|
get_close_maxdist_init |
|
get_close_obs_init |
|
get_close_obs |
|
ens_mean_for_model |
Unique interface 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 : |
A note about documentation style. Optional arguments are enclosed in brackets [like this].
Interface routine descriptions
model_size = get_model_size( )
integer :: get_model_size
Returns the length of the model state vector. Required.
|
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.
|
State vector of length model_size. |
|
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 of state vector element about which information is requested. |
|
Returns the 3D location of the indexed state variable. The |
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
|
The list of supported variables in DART/assimilation_code/modules/observations/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
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.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.
|
A model state vector. |
|
Location to which to interpolate. |
|
Integer indexing which type of observation is desired. |
|
The interpolated value from the model. |
|
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.
|
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.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.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, andgitm_in&grid_nml.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.
|
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.
|
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.
|
Integer file descriptor to previously-opened netCDF file. |
|
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, preassim.nc, and analysis.nc.
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.
|
file descriptor to previously-opened netCDF file. |
|
A model state vector. |
|
Integer index of copy to be written. |
|
The timestep counter for the given state. |
|
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
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.).input.nml&model_mod_nml:model_perturbation_amplitude and utterly, completely fails.input.nml&filter_nml:start_from_restart = .false.
|
State vector to be perturbed. |
|
The perturbed state vector. |
|
Because of the ‘wet/dry’ issue discussed above, this is always |
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
location_mod because we want to be able to discriminate
against selecting ‘dry land’ locations.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.
|
Structure to allow efficient identification of locations ‘close’ to a given location. |
|
Single given location. |
|
Kind of the single location. |
|
List of candidate locations. |
|
Kind associated with candidate locations. |
|
Number of locations close to the given location. |
|
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.
|
State vector containing the ensemble mean. |
Unique interface routine descriptions
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
gitm_restart.nc.
|
The number of longitudinal gridpoints. |
|
The number of latitudinal gridpoints. |
|
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.
|
The name of the netCDF format GITM restart file. |
|
the 1D array containing the concatenated GITM variables. |
|
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.
|
the netCDF-format GITM restart file to be updated. |
|
the 1D array containing the DART state vector. |
|
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.
|
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.
Files
filename |
purpose |
|---|---|
input.nml |
to read the model_mod namelist |
gitm_vars.nml |
to read the |
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 |
preassim.nc |
the time-history of the model state before assimilation |
analysis.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 |
References
none
Private components
N/A