pyissm.model.classes.frontalforcings

Frontal Forcings classes for ISSM.

Classes

`default`([other])	Default frontalforcings class for ISSM.
`rignot`([other])	Rignot frontalforcings class for ISSM.
`rignotarma`([other])	RignotARMA frontalforcings class for ISSM.

class pyissm.model.classes.frontalforcings.default(other=None)

Bases: manage_state

Default frontalforcings class for ISSM.

This class contains the default parameters for frontal forcings in the ISSM framework. It defines the main frontal forcing-related parameters.

Parameters:: other (any, optional) – Any other class object that contains common fields to inherit from. If values in other differ from default values, they will override the default values.

meltingrate

Melting rate at given location [m/a].

Type:: numpy.ndarray, default=np.nan

ablationrate

Frontal ablation rate at given location [m/a], it contains both calving and melting.

Type:: numpy.ndarray, default=np.nan

Examples

>>> md.frontalforcings = pyissm.model.classes.frontalforcings.default()

check_consistency(md, solution, analyses)

Check consistency of the [frontalforcings.default] parameters.

Parameters:

md (pyissm.model.Model) – The model object to check.
solution (str) – The solution name to check.
analyses (list of str) – List of analyses to check consistency for.

Returns:

md – The model object with any consistency errors noted.

Return type:

pyissm.model.Model

marshall_class(fid, prefix, md=None)

Marshall [frontalforcings.default] parameters to a binary file.

Parameters:

fid (file object) – The file object to write the binary data to.
prefix (str) – Prefix string used for data identification in the binary file.
md (pyissm.model.Model, optional) – ISSM model object needed in some cases.

Return type:

None

class pyissm.model.classes.frontalforcings.rignot(other=None)

Bases: manage_state

Rignot frontalforcings class for ISSM.

This class contains the parameters for frontal forcings based on the Rignot methodology in the ISSM framework. It defines the main frontal forcing-related parameters specific to the Rignot approach.

Parameters:: other (any, optional) – Any other class object that contains common fields to inherit from. If values in other differ from default values, they will override the default values.

basin_id

Basin ID for elements.

Type:: numpy.ndarray, default=np.nan

num_basins

Number of basins.

Type:: int, default=0

subglacial_discharge

Sum of subglacial discharge for each basin [m/d].

Type:: numpy.ndarray, default=np.nan

thermalforcing

Thermal forcing [°C].

Type:: numpy.ndarray, default=np.nan

Examples

>>> md.frontalforcings = pyissm.model.classes.frontalforcings.rignot()

check_consistency(md, solution, analyses)

Check consistency of the [frontalforcings.rignot] parameters.

Parameters:

md (pyissm.model.Model) – The model object to check.
solution (str) – The solution name to check.
analyses (list of str) – List of analyses to check consistency for.

Returns:

md – The model object with any consistency errors noted.

Return type:

pyissm.model.Model

marshall_class(fid, prefix, md=None)

Marshall [frontalforcings.rignot] parameters to a binary file.

Parameters:

fid (file object) – The file object to write the binary data to.
prefix (str) – Prefix string used for data identification in the binary file.
md (pyissm.model.Model, optional) – ISSM model object needed in some cases.

Return type:

None

class pyissm.model.classes.frontalforcings.rignotarma(other=None)

Bases: manage_state

RignotARMA frontalforcings class for ISSM.

This class contains the parameters for frontal forcings based on the Rignot methodology with ARMA (AutoRegressive Moving Average) modeling in the ISSM framework. It defines the main frontal forcing-related parameters specific to the RignotARMA approach, including polynomial trends, breakpoints, ARMA coefficients, and subglacial discharge modeling.

Parameters:: other (any, optional) – Any other class object that contains common fields to inherit from. If values in other differ from default values, they will override the default values.

num_basins

Number of different basins.

Type:: int, default=0

num_params

Number of different parameters in the piecewise-polynomial (1:intercept only, 2:with linear trend, 3:with quadratic trend, etc.).

Type:: int, default=0

num_breaks

Number of different breakpoints in the piecewise-polynomial (separating num_breaks+1 periods).

Type:: int, default=0

polynomialparams

Coefficients for the polynomial (const, trend, quadratic, etc.), dim1 for basins, dim2 for periods, dim3 for orders.

Type:: numpy.ndarray, default=np.nan

datebreaks

Dates at which the breakpoints in the piecewise polynomial occur (1 row per basin) [yr].

Type:: numpy.ndarray, default=np.nan

ar_order

Order of the autoregressive model.

Type:: int, default=0

ma_order

Order of the moving-average model.

Type:: int, default=0

arma_timestep

Time resolution of the ARMA model [yr].

Type:: int, default=0

arlag_coefs

Basin-specific vectors of AR lag coefficients.

Type:: numpy.ndarray, default=np.nan

malag_coefs

Basin-specific vectors of MA lag coefficients.

Type:: numpy.ndarray, default=np.nan

monthlyvals_intercepts

Monthly intercept values for each basin.

Type:: numpy.ndarray, default=np.nan

monthlyvals_trends

Monthly trend values for each basin.

Type:: numpy.ndarray, default=np.nan

monthlyvals_numbreaks

Number of breakpoints for monthly values.

Type:: int, default=0

monthlyvals_datebreaks

Dates at which the monthly value breakpoints occur.

Type:: numpy.ndarray, default=np.nan

basin_id

Basin number assigned to each element.

Type:: numpy.ndarray, default=np.nan

subglacial_discharge

Sum of subglacial discharge for each basin [m/d].

Type:: numpy.ndarray, default=np.nan

isdischargearma

Whether an ARMA model is also used for the subglacial discharge (if 0: subglacial_discharge is used, if 1: sd_ parameters are used).

Type:: int, default=0

sd_ar_order

Order of the subglacial discharge autoregressive model.

Type:: int, default=0

sd_ma_order

Order of the subglacial discharge moving-average model.

Type:: int, default=0

sd_arma_timestep

Time resolution of the subglacial discharge ARMA model [yr].

Type:: int, default=0

sd_arlag_coefs

Basin-specific vectors of AR lag coefficients for subglacial discharge.

Type:: numpy.ndarray, default=np.nan

sd_malag_coefs

Basin-specific vectors of MA lag coefficients for subglacial discharge.

Type:: numpy.ndarray, default=np.nan

sd_monthlyfrac

Basin-specific vectors of 12 values with fraction of the annual discharge occurring every month.

Type:: numpy.ndarray, default=np.nan

sd_num_breaks

Number of different breakpoints in the subglacial discharge piecewise-polynomial (separating sd_num_breaks+1 periods).

Type:: int, default=0

sd_num_params

Number of different parameters in the subglacial discharge piecewise-polynomial.

Type:: int, default=0

sd_polynomialparams

Coefficients for the subglacial discharge polynomial (const, trend, quadratic, etc.).

Type:: numpy.ndarray, default=np.nan

sd_datebreaks

Dates at which the breakpoints in the subglacial discharge piecewise polynomial occur (1 row per basin) [yr].

Type:: numpy.ndarray, default=np.nan

Examples

>>> md.frontalforcings = pyissm.model.classes.frontalforcings.rignotarma()

check_consistency(md, solution, analyses)

Check consistency of the [frontalforcings.rignotarma] parameters.

Parameters:

md (pyissm.model.Model) – The model object to check.
solution (str) – The solution name to check.
analyses (list of str) – List of analyses to check consistency for.

Returns:

md – The model object with any consistency errors noted.

Return type:

pyissm.model.Model

marshall_class(fid, prefix, md=None)

Marshall [frontalforcings.rignotarma] parameters to a binary file.

Parameters:

fid (file object) – The file object to write the binary data to.
prefix (str) – Prefix string used for data identification in the binary file.
md (pyissm.model.Model, optional) – ISSM model object needed in some cases.

Return type:

None