NEDAS.models.topaz.v5 package

class NEDAS.models.topaz.v5.Topaz5Model(**kwargs)[source]

Bases: Model[RegularGrid]

TOPAZ5 model class.

io_mode: Literal['online', 'offline'] = 'offline'
nhc_root: str
basedir: str
model_env: str | None
reanalysis_code: str
V: str
X: str
T: str
R: str
E: str
idm: int
jdm: int
kdm: int
nproc: int
use_job_array: bool
stagnant_log_timeout: int
meanssh_file: str
forcing_file: str
restart_dt: int
output_dt: int
forcing_dt: int
z_units: str
thflag: int
thref: float
thbase: float
kapref: int
yrflag: int
ONEM: float
MIN_SEAICE_CONC: float
MAX_OCEAN_TEMP: float
MIN_OCEAN_SALN: float
MAX_OCEAN_SALN: float
ncat: int
Nilayer: int
saltmax: float
min_salin: float
depressT: float
nsal: float
msal: float
aice_thresh: float
fice_thresh: float
hice_impact: float
is_ncat(name)[source]
get_cat_id(name)[source]
filename(**kwargs)[source]
read_grid(**kwargs)[source]

Read the grid information from the model output.

Parameters:

**kwargs – Keyword arguments for reading the grid.

read_mask(**kwargs)[source]
read_var(**kwargs)[source]

Read a variable from the model output.

Parameters:

**kwargs – Keyword arguments for reading the variable.

Returns:

The read variable.

Return type:

np.ndarray

write_var(var, **kwargs)[source]

Write a variable to the model output.

Parameters:

  • var (np.ndarray) – The variable to write.

  • **kwargs – Keyword arguments for writing the variable.

z_coords(**kwargs)[source]

Get the vertical coordinates of the model.

Parameters:

**kwargs – Keyword arguments for getting the vertical coordinates.

Returns:

The vertical coordinates.

Return type:

np.ndarray

get_ocean_surf_height(**kwargs)[source]

Get ocean surface height from restart variables Adapted from p_ssh_from_state.F90 in ReanalysisTP5/SSHFromState_HYCOMICE

get_ocean_surf_height_anomaly(**kwargs)[source]
get_ocean_surf_temp(**kwargs)[source]
get_seaice_conc(**kwargs)[source]

Get total seaice concentration from multicategory ice concentration (aicen) adapted from ReanalysisTP5/SSHFromState_HYCOMICE/mod_read_icednc by J. Xie

get_seaice_thick(**kwargs)[source]

Get total seaice thickness from the multicategory ice volume (vicen)

get_snow_thick(**kwargs)[source]

Get total snow thickness from the multi-category snow volume (vsnon)

preprocess(*args, **kwargs)[source]

Preprocess the model data.

Parameters:

**kwargs – Keyword arguments for preprocessing.

postprocess(*args, **kwargs)[source]

Postprocess the model data.

Parameters:

**kwargs – Keyword arguments for postprocessing.

postprocess_native(*args, **kwargs)[source]

Post processing the restart variables for next forecast

run(*args, **kwargs)[source]

Run the model forward in time.

Parameters:

  • *args – Arguments

  • **kwargs – Keyword arguments

Keyword Arguments:

  • time (datetime) – current time when forecast starts

  • restart_dir (str) – directory where restart files are located

  • forecast_period (int) – forecast period in hours

If self.ens_run_strategy == ‘batch’, the method will run all ensemble members in one go, expect additional kwargs[‘nens’] to be the ensemble size. If self.ens_run_strategy == ‘scheduler’, the method runs a single member indexed by kwargs[‘member’], and kwargs[‘worker_id’] is the pid assigned by the scheduler to run this method.

generate_truth(*args, **kwargs)[source]

Generate truth (nature run) model states. Use for running synthetic observation experiments.

generate_init_ensemble(*args, **kwargs)[source]

Generate initial perturbed model states for ensemble forecasts.

Parameters:

  • nens (int) – ensemble size

  • **kwargs

Submodules