DART Documentation Main Index 
This document describes the DART interface module for the MPASAtmosphere (or briefly, MPASATM) global model, which uses an unstructured Voronoi grid mesh, formally Spherical Centriodal Voronoi Tesselations (SCVTs). This allows for both quasiuniform discretization of the sphere and local refinement. The MPAS/DART interface was built on the SCVTdual mesh and does not regrid to regular lat/lon grids. In the Cgrid discretization, the normal component of velocity on cell edges is prognosed; zonal and meridional wind components are diagnosed on the cell centers. We provide several options to choose from in the assimilation of wind observations as shown below.
The grid terminology used in MPAS is as shown in the figure below:
The wind options during a DART assimilation are controlled by combinations of 4 different namelist values. The values determine which fields the forward operator uses to compute expected observation values; how the horizontal interpolation is computed in that forward operator; and how the assimilation increments are applied to update the wind quantities in the state vector. Preliminary results based on real data assimilation experiments indicate that performance is better when the zonal and meridional winds are used as input to the forward operator that uses Barycentric interpolation, and when the prognostic u wind is updated by the incremental method described in the figure below. However there remain scientific questions about how best to handle the wind fields under different situations. Thus we have kept all implemented options available for use in experimental comparisons. See the figure below for a flowchart representation of how the 4 namelist items interact:
Cycling of MPAS/DART is run in a restart mode. As for all DART experiments, the overall design for an experiment is this: the DART program filter will read the initial condition file, the observation sequence file, and the DART namelist to decide whether or not to advance the MPASATM model. All of the control of the execution of the MPAS model is done by DART directly. If the model needs to be advanced, filter makes a call to the shell to execute the script advance_model.csh, which is ENTIRELY responsible for getting all the input files, data files, namelists, etc. into a temporary directory, running the model, and copying the results back to the parent directory (which we call CENTRALDIR). The whole process hinges on setting the MPASATM model namelist values such that it is doing a restart for every model advance. Unlike MPASATM free forecast runs, the forecast step in MPAS/DART requires to set up one more namelist parameter called "config_do_DAcycling = .true." in &restart section of namelist.input to recouple the state vectors (updated by filter) with the mass field for the restart mode. For more information, check the advance_model.csh script.
Since DART is an ensemble algorithm, there are multiple analysis files for a single analysis time: one for each ensemble member. Because MPAS/DART is run in a restart mode, each member should keep its own MPAS restart file from the previous cycle (rather than having a single template file in CENTRALDIR). Creating the initial ensemble of states is an area of active research.
This namelist is read from the file input.nml. Namelists start with an ampersand '&' and terminate with a slash '/'. Character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely terminating the namelist.
&model_nml init_template_filename = 'mpas_init.nc', vert_localization_coord = 3, assimilation_period_days = 0, assimilation_period_seconds = 21600, model_perturbation_amplitude = 0.0001, log_p_vert_interp = .true., calendar = 'Gregorian', use_u_for_wind = .false., use_rbf_option = 2, update_u_from_reconstruct = .true., use_increments_for_u_update = .true., highest_obs_pressure_mb = 100.0, sfc_elev_max_diff = 1.0, outside_grid_level_tolerance = 1.0, extrapolate = .false., debug = 0, /
Item  Type  Description 

init_template_filename  character(len=256) [default: 'mpas_init.nc'] 
The name of the MPAS analysis file to be read and/or written by the DART programs for the state data. 
highest_obs_pressure_mb  real(r8) [default: 100.0] 
Observations higher than this pressure are ignored. Set to 1.0 to ignore this test. For models with a prescribed top boundary layer, trying to assimilate very high observations results in problems because the model damps out any changes the assimilation tries to make. With adaptive algorithms this results in larger and larger coefficients as the assimilation tries to effect state vector change. 
assimilation_period_days  integer [default: 0]  The number of days to advance the model for each assimilation. Even if the model is being advanced outside of the DART filter program, the assimilation period should be set correctly. Only observations with a time within +/ 1/2 this window size will be assimilated. 
assimilation_period_seconds  integer [default: 21600]  In addition to assimilation_period_days, the number of seconds to advance the model for each assimilation. 
vert_localization_coord  integer [default: 3]  Vertical coordinate for vertical localization.

sfc_elev_max_diff  real(r8)[default: 1.0]  If > 0, the maximum difference, in meters, between an observation marked as a 'surface obs' as the vertical type (with the surface elevation, in meters, as the numerical vertical location), and the surface elevation as defined by the model. Observations further away from the surface than this threshold are rejected and not assimilated. If the value is negative, this test is skipped. 
log_p_vert_interp  logical [default: .true.]  If .true., vertical interpolation is done in logpressure. Otherwise, linear. 
use_u_for_wind  logical [default: .false.]  If .false., zonal and meridional winds at cell centers are used for the wind observation operator [default]. In that case, triangular meshes are used for the barycentric (e.g., areaweighted) interpolation. If .true., wind vectors at an arbitrary (e.g., observation) point are reconstructed from the normal component of velocity on cell edges (u) using radial basis functions (RBFs) provided by the MPAS model. 
use_rbf_option  integer [default: 2]  If use_u_for_wind is .true., this option controls how many points will be used in the RBF interpolation. Options are available as 0, 1, 2, and 3. All the edges available in N (= 0,1,2, or 3) neighboring cells go into the RBF reconstruction. 
update_u_from_reconstruct  logical [default: .true.]  When zonal and meridional winds at cell centers are used for the wind observation operator (use_u_for_wind = .false.), this option decides if the normal component of velocity on cell edges (which is the only wind prognostic variable in MPASATM) should be updated from the winds at cell centers. If .true., use_increments_for_u_update should be also decided. If use_u_for_wind = .true. and the normal component of velocity on cell edges is defined as a state vector, this option should be .false. so the edge winds can be directly updated by filter. 
use_increments_for_u_update  logical [default: .true.]  Only if update_u_from_reconstruct is .true., this option is used to decide if the edge winds are replaced by averaging from the analysis winds at cell centers (.false.), or just updated by the analysis increments at cell centers (.true.). If .true., all the wind components (e.g., both at cell centers and edges) are read from prior and used to compute the increments [Recommended]. 
model_perturbation_amplitude  real(r8) [default: 0.0001]  The amplitude of random noise to add when trying to perturb a single state vector to create an ensemble. Only used when input.nml::&filter_nml:start_from_restart = .false. Multiplied by the state vector, it produces standard deviation of a gaussian distribution with the mean at the value of the state vector element. 
calendar  character(len=32) [default: 'Gregorian'] 
Character string specifying the calendar being used by MPAS. 
outside_grid_level_tolerance  real(r8) [default: 1.0]  If greater than 0.0, amount of distance in fractional model levels that a vertical location can be above or below the top or bottom of the grid and still be evaluated without error. Since extrapolate is not implemented yet, the value of .false. will be assumed. In this case, vertical locations equivalent to level 1 or level N will be used. Eventually, if extrapolate is .true., extrapolate from the first or last model level. If extrapolate is .false., simply use the value at level 1 for low vertical locations, or at level N for high vertical locations. 
extrapolate  logical [default: .false.]  NOT IMPLEMENTED YET. Vertical locations equivalant to level 1
or level N will be used. When this is implemented, it will do: If outside_grid_level_tolerance is greater than 0.0, then control how values are assigned to locations where the vertical is exterior to the grid. If this is .true., then extrapolate low locations from levels 1 and 2, and high locations from levels N1 and N. If this is .false., then simply use the corresponding values at level 1 or N. This item is ignored if outside_grid_level_tolerance is less than or equal to 0.0. 
debug  integer [default: 0]  The switch to specify the runtime verbosity. 0 is as quiet as it gets. > 1 provides more runtime messages. > 5 provides ALL runtime messages. 
The &mpas_vars_nml namelist contains the list of MPAS variables that make up the DART state vector. The order the items are specified controls the order of the data in the state vector, so it should not be changed without regenerating all DART initial condition or restart files. These variables are directly updated by the filter assimilation.
Any variables whose values cannot exceed a given minimum or maximum can be listed in mpas_state_bounds. When the data is written back into the MPAS NetCDF files values outside the allowed range will be detected and changed. Data inside the DART state vector and data written to the DART diagnostic files will not go through this test and values may exceed the allowed limits. Note that changing values at the edges of the distribution means it is no longer completely gaussian. In practice this technique has worked effectively, but if the assimilation is continually trying to move the values outside the permitted range the results may be of poor quality. Examine the diagnostics for these fields carefully when using bounds to restrict their values.
&mpas_vars_nml mpas_state_variables = 'theta', 'QTY_POTENTIAL_TEMPERATURE', 'uReconstructZonal', 'QTY_U_WIND_COMPONENT', 'uReconstructMeridional','QTY_V_WIND_COMPONENT', 'qv', 'QTY_VAPOR_MIXING_RATIO', 'qc', 'QTY_CLOUDWATER_MIXING_RATIO', 'surface_pressure', 'QTY_SURFACE_PRESSURE' mpas_state_bounds = 'qv','0.0','NULL','CLAMP', 'qc','0.0','NULL','CLAMP', /
Item  Type  Description 

mpas_vars_nml  character(len=NF90_MAX_NAME):: dimension(160)  The table that both specifies which MPASATM variables will be placed in the state vector, and also relates those variables to the corresponding DART kinds. The first column in each pair must be the exact NetCDF name of a field in the MPAS file. The second column in each pair must be a KIND known to the DART system. See assimilation_code/modules/observations/obs_kind_mod.f90 for known names. This file is autogenerated when DART builds filter for a particular model, so run quickbuild.csh in the work directory first before examining this file. Use the generic kind list in the obs_kind_mod tables, not the specific type list. 
mpas_state_bounds  character(len=NF90_MAX_NAME):: dimension(160)  List only MPASATM variables that must restrict their values to remain between given lower and/or upper bounds. Columns are: NetCDF variable name, min value, max value, and action to take for outofrange values. Either min or max can have the string 'NULL' to indicate no limiting will be done. If the action is 'CLAMP' out of range values will be changed to the corresponding bound and execution continues; 'FAIL' stops the executable if out of range values are detected. 
As the forward operators use the unstructured grid meshes in MPASATM, the DART/MPAS interface needs to read static variables related to the grid structure from the MPAS ATM 'history' file (specified in model_analysis_filename). These variables are used to find the closest cell to an observation point in the cartesian coordinate (to avoid the polar issues).
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 levels for mass fields 
integer :: nVertLevelsP1  the number of vertical levels for vertical velocity 
integer :: nSoilLevels  the number of soil levels 
real(r8) :: latCell(:)  the latitudes of the cell centers [90,90] 
real(r8) :: lonCell(:)  the longitudes of the cell centers [0, 360] 
real(r8) :: latEdge(:)  the latitudes of the edges [90,90], if edge winds are used. 
real(r8) :: lonEdge(:)  the longitudes of the edges [0, 360], if edge winds are used. 
real(r8) :: xVertex(:)  The cartesian location in xaxis of the vertex 
real(r8) :: yVertex(:)  The cartesian location in yaxis of the vertex 
real(r8) :: zVertex(:)  The cartesian location in zaxis of the vertex 
real(r8) :: xEdge(:)  The cartesian location in xaxis of the edge, if edge winds are used. 
real(r8) :: yEdge(:)  The cartesian location in yaxis of the edge, if edge winds are used. 
real(r8) :: zEdge(:)  The cartesian location in zaxis of the edge, if edge winds are used. 
real(r8) :: zgrid(:,:)  geometric height at cell centers (nCells, nVertLevelsP1) 
integer :: CellsOnVertex(:,:)  list of cell centers defining a triangle 
integer :: edgesOnCell(:,:)  list of edges on each cell 
integer :: verticesOnCell(:,:)  list of vertices on each cell 
integer :: edgeNormalVectors(:,:)  unit direction vectors on the edges (only used if use_u_for_wind = .true.) 
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 userdefined 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 dimensions  excluding TIME integer :: numvertical ! number of vertical levels in variable integer :: numcells ! number of cell locations (typically cell centers) integer :: numedges ! number of edge locations (edges for normal velocity) logical :: ZonHalf ! vertical coordinate for mass fields (nVertLevels) integer :: varsize ! variable size (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 rangerestricted before real(r8) :: range(2) ! lower and upper bounds for the data range. logical :: out_of_range_fail ! is out of range fatal if rangechecking? 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 verticallycontiguous for each horizontal location. From a storage
standpoint, this would be equivalent to a Fortran variable dimensioned
x(nVertical,nHorizontal,nVariables). The fastestvarying 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 MPASATM can be compiled with various fortran compilers such as (but not limited to) gfortran, pgf90, and intel. It has been tested on a Mac and NCAR IBM supercomputer (yellowstone).
NOTE: While MPAS requires the PIO (Parallel IO) and pNetCDF (Parallel NetCDF) libraries, DART uses only the plain NetCDF libraries. If an altered NetCDF library is required by the parallel versions, there may be incompatibilities between the runtime requirements of DART and MPAS. Static linking of one or the other executable, or swapping of modules between executions may be necessary.
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 multidimensional arrays listed below have the dimensions reversed from the Fortran convention.
366 mirage2:thoar% ncdump h mpas_init.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 ; 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) ; available in DART double lonEdge(nEdges) ; available in DART int indexToEdgeID(nEdges) ; double latVertex(nVertices) ; double lonVertex(nVertices) ; double xVertex(nVertices) ; available in DART double yVertex(nVertices) ; available in DART double zVertex(nVertices) ; available in DART double xEdge(nVertices) ; available in DART double yEdge(nVertices) ; available in DART double zEdge(nVertices) ; available in DART int indexToVertexID(nVertices) ; int cellsOnEdge(nEdges, TWO) ; int nEdgesOnCell(nCells) ; int nEdgesOnEdge(nEdges) ; int edgesOnCell(nCells, maxEdges) ; available in DART int edgesOnEdge(nEdges, maxEdges2) ; double weightsOnEdge(nEdges, maxEdges2) ; double dvEdge(nEdges) ; double dcEdge(nEdges) ; double angleEdge(nEdges) ; double edgeNormalVectors(nEdges, R3) ; available in DART double cellTangentPlane(nEdges, TWO, R3) ; int cellsOnCell(nCells, maxEdges) ; int verticesOnCell(nCells, maxEdges) ; available in DART 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) ;
types_mod time_manager_mod threed_sphere/location_mod threed_cartesian/xyz_location_mod utilities_mod obs_kind_mod mpi_utilities_mod random_seq_mod mpas_atm/get_geometry_mod mpas_atm/get_reconstruct_mod
Only a select number of interfaces used are discussed here. Each module has its own discussion of their routines.
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 mpas 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/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.
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, a type not found in the state vector), interp_val is returned
as MISSING_R8 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 == any positive values (negative values are reserved for system use) 
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 NOT related to the internal dynamic typestep of the model; it must be a multiple of the minimum amount of model time in which the model can be started and stopped. This is also the assimilation window. All observations within (+/) one half of the forecast length are used for the assimilation. In the MPAS case, this is set from the namelist values for input.nml :: &model_nml :: assimilation_period_days, assimilation_period_seconds.
var  Assimilation window and multiple of some timestep the model can be advanced. 
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 modulelocal private attributes that can then be queried by the
public interface routines.
The namelists (all mandatory) are:
input.nml :: &model_mod_nml, and
input.nml :: &mpas_vars_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. 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 MPAS 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 MPAS 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, intent(in) :: ncFileID integer, intent(in) :: domainID
nc_write_model_atts writes modelspecific attributes to a created netCDF file: In the MPAS case, this includes information like the coordinate variables (the grid arrays: lonCell, latCell, xCell, lonVertex, zgrid, etc.), information from some of the namelists, and the variable attributes of either the 1D state vector or the prognostic variables (potential temperature, dry density, vertical velocity, etc.). All the required information (except for the netCDF file identifier) is obtained from the scope of the model_mod module. The input.nml is preserved in the netCDF file as variables inputnml.
ncFileID  Integer file descriptor to previouslyopened netCDF file. 
domainID  Integer descriptor specifying the domain whose metadata should be written. 
nc_write_model_atts is responsible for the modelspecific attributes in the following DARToutput netCDF files: true_state.nc, preassim.nc, and analysis.nc.
real(r8), dimension(:), intent(in) :: state real(r8), dimension(:), intent(out) :: pert_state logical, intent(out) :: interf_provided
Only called when
input.nml :: &filter_nml:start_from_restart = .false..
A single model state is still required, and gaussian noise with the magnitude
determined by the setting of
input.nml :: &model_mod_nml:model_perturbation_amplitude.
is added to each item in the state vector.
Generally this is not the best way to generate a set of initial conditions,
but can be useful if very small magnitude noise is added and then the
model is run freely for an extended period of time (several model weeks)
to generate realistic features in the model state.
state  State vector to be perturbed. 
pert_state  The perturbed state vector. 
interf_provided  This is always .true., indicating a random perturbation is added to each model state variable independently. 
type(get_close_type), intent(inout) :: gc real(r8), intent(in) :: maxdist
Passthrough to the 3D 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)
Passthrough to the 3D 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
need to be able to convert vertical coordinates that are not already
in the same units as specified by
input.nml::&model_nml:vert_localization_coord.
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 modulelocal storage. This is a NULL_INTERFACE for the MPAS model. At present there is no application which requires modulelocal storage of the ensemble mean. No storage is allocated.
ens_mean  State vector containing the ensemble mean. 
filename  purpose 

input.nml  to read the namelist  model_mod_nml and mpas_vars_nml 
mpas_init.nc  provides model state, and 'valid_time' of the model state 
static.nc  provides grid dimensions 
true_state.nc  the timehistory of the "true" model state from an OSSE 
preassim.nc  the timehistory of the model state before assimilation 
analysis.nc  the timehistory of the model state after assimilation 
dart_log.out [default name]  the runtime diagnostic output 
dart_log.nml [default name]  the record of all the namelists actually USED  contains the default values 
none at this time
DART software  Copyright 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:  $Revision$ 
Source:  $URL$ 
Change Date:  $Date$ 
Change history:  try "svn log" or "svn diff" 