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Until that time, you should consider this documentation as out-of-date.
&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 |
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.:doc:`./model_to_dart` | 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.nml model_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. |
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. |
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.
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.
Only a select number of interfaces used are discussed here. Each module has its own discussion of their 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 |
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_o bs |
A note about documentation style. Optional arguments are enclosed in brackets [like this].
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/assimilation_code/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/assimilation_code/modules/observations/obs_kind_mod.f90 |
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.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. |
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
.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:
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.
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
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 |
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
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)
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.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. |
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
.
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 |
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 |
- none
N/A