CalibratorCryotempo#

class dtcg.integration.calibration.CalibratorCryotempo( model_matrix=None, datacube_l1=None, gdir=None, )[source]#

Bases: Calibrator

Methods

`__init__`([model_matrix, datacube_l1, gdir])
`add_annual_cumulative`(ds, var[, year_coord, ...])
`add_snowline_attrs`(ds[, inf_values])
`add_specific_mb`(ds, mb_model, fls, ...)	Get specific mass-balance in mm w.e.
`add_specific_mb_attrs`(ds, unit)
`calculate_and_add_total_runoff`(ds)	Get total annual and monthly runoff from runoff components.
`calibrate`(gdir[, model_matrix, datacube_l1, ...])	Calibrate the mass balance model and create L2 datacubes using a model matrix.
`get_calibrated_datacubes`(mb_model_class, ...)	Calibrate models and generate datacubes.
`get_eolis_dates`(ds)	Get time index from CryoTEMPO-EOLIS dataset.
`get_eolis_mean_dh`(ds)	Get mean elevation change from EOLIS data.
`get_geodetic_mb_from_dataset`(dataset[, ...])	Get the geodetic mass balance in kg m-2 from enhanced gridded data.
`get_mcs_parameter_sample`(gdir, ...[, ...])	Defines the distribution of the observation and model parameters and create a sample for the Monte Carlo simulation.
`get_nearest_datetime`(dates, target_date)	Get the nearest datetime.
`get_ref_mb`([gdir, ref_mb_period, ...])	Get observed mass balance for a specific reference period.
`get_temporal_bounds`(dates, date_start, date_end)	Get start and end dates of geodetic and observational periods.
`set_model_matrix`([name, model, geo_period, ...])	Set model parameters for calibration.

Parameters:: model_matrix (dict)

__init__( model_matrix=None, datacube_l1=None, gdir=None, )[source]#

Parameters:: model_matrix (dict)

add_annual_cumulative( ds, var, year_coord='calendar_year', cumulative_suffix='calendar_cum', )#

Parameters:

ds
var
year_coord
cumulative_suffix

add_snowline_attrs(ds, inf_values=None)#

Parameters:

ds
inf_values

add_specific_mb( ds, mb_model, fls, time_resolution, unit, )#

Get specific mass-balance in mm w.e. from a volume change time series.

Parameters:

ds
mb_model
fls
time_resolution
unit

add_specific_mb_attrs(ds, unit)#

Parameters:

ds
unit

calculate_and_add_total_runoff(ds)#

Get total annual and monthly runoff from runoff components.

Parameters:: ds

calibrate(

gdir,

model_matrix=None,

datacube_l1=None,

ignore_errors=False,

**kwargs,

)#

Calibrate the mass balance model and create L2 datacubes using a model matrix.

Parameters:

gdir (GlacierDirectory) – Glacier directory.
model_matrix (dict) – Model parameters for various calibration runs.
datacube_l1 (xr.Dataset) – If the reference mass balance should be derived from an observation data, which is available in a L1 datacube.
ignore_errors (bool) – If True any exception raised during the calibration is ignored.
kwargs (dict) – kwargs passed on to Calibrator.get_calibrated_datacubes

Returns:

Containing the created L2 datacubes for each set of configuration parameters in model_matrix

Return type:

dict

get_calibrated_datacubes(

mb_model_class,

ref_mb,

ref_mb_err,

ref_mb_unit='kg m-2 yr-1',

ref_mb_period='',

gdir=None,

first_guess_settings='',

datacube_types='monthly',

calibration_filesuffix='',

calibration_strategy='',

mcs_sampling_settings=None,

calibration_parameters_control=None,

calibration_parameters_mcs=None,

quantiles=None,

climate_input_filesuffix=None,

show_log=False,

multiprocessing_during_datacube_creation=True,

**kwargs,

)#

Calibrate models and generate datacubes.

Uses the provided reference mass balance to calibrate models and generate model datacubes. These also include an uncertainty propagation through a Monte Carlo simulation.

Parameters:

gdir (GlacierDirectory) – OGGM glacier directory.
mb_model_class (oggm.core.massbalance.MassBalanceModel) – OGGM mass balance model class.
ref_mb (float) – Reference mass balance.
ref_mb_err (float) – Reference mass balance uncertainty.
ref_mb_unit (str, default "kg m-2 yr-1") – Unit used for the reference mass balance.
ref_mb_period (str, optional) – Measurement period for the reference mass balance.
first_guess_settings (str, optional) – the mb parameters which should be used as a first guess
datacube_types (str or list[str], default "monthly") –
Desired datacube types. Select one or provide a list. Options are:
- monthly: monthly volume, area, length and mass balance.
- annual_hydro: annual volume, area, length and mass balance, and annual and monthly runoff components.
- daily_smb: daily mass balance.
calibration_filesuffix (str) – The filesuffix used for all outputs of this method.
calibration_strategy (str) – Text describing the current calibration strategy, e.g. “OGGM model X calibrated with data from Y over the period Z.”
mcs_sampling_settings (dict, default None) – Settings for Monte Carlo simulation parameter sampling. For default values look at signature of Calibrator.get_mcs_parameter_sample.

calibration_parameters_control (dict, default None) –

The order of calibration parameters for the control run. If None, defaults to:

{
    "calibrate_param1": "melt_f",
    "calibrate_param2": "prcp_fac",
    "calibrate_param3": "temp_bias",
}

calibration_parameters_mcs (dict, default None) –
The order of calibration parameters for the Monte Carlo simulation runs. If None, defaults to:
```
{
    "calibrate_param1": "prcp_fac",
    "calibrate_param2": "melt_f",
    "calibrate_param3": "temp_bias",
}
```
quantiles (list, default None) – The quantiles used for the aggregation of the Monte Carlo simulation results, including labels for the final datacube. If None, defaults to [0.05, 0.15, 0.25, 0.50, 0.75, 0.85, 0.95].
climate_input_filesuffix (str, optional) – The filesuffix of the climate input file.
show_log (bool, default False) – Show some basic logs during execution if True.
kwargs (dict) – kwargs to pass to the mb_model_class instance.
multiprocessing_during_datacube_creation (bool)

Returns:

The model datacubes generated by using the provided data for calibration, including uncertainties.

Return type:

dict

get_eolis_dates(ds)[source]#

Get time index from CryoTEMPO-EOLIS dataset.

Parameters:: ds (xr.Dataset) – CryoTEMPO-EOLIS dataset.
Returns:: Time index adjusted to UTC.
Return type:: np.ndarray

get_eolis_mean_dh(ds)[source]#

Get mean elevation change from EOLIS data.

Parameters:: ds (xr.Dataset) – CryoTEMPO-EOLIS dataset.
Returns:: Time series of spatial mean of elevation change.
Return type:: np.ndarray

get_geodetic_mb_from_dataset( dataset, date_start='2011-01-01', date_end='2020-01-01', )[source]#

Get the geodetic mass balance in kg m-2 from enhanced gridded data.

Parameters:

dataset (xr.Dataset) – CryoTEMPO-EOLIS dataset.
date_start (str, default "2011-01-01") – Start of reference period.
date_end (str, default "2020-01-01") – End of reference period.

Returns:

ref_mb, ref_mb_unit, ref_mb_err: the reference mass balance to match with unit and associated uncertainty.

Return type:

tuple

get_mcs_parameter_sample( gdir, control_filesuffix, nr_samples=16, ref_mb_err_sample_interval=1.0, prcp_fac_sample_interval=0.5, temp_bias_sample_interval=0.5, melt_f_sample_interval=0.25, )#

Defines the distribution of the observation and model parameters and create a sample for the Monte Carlo simulation.

Parameters:

gdir (GlacierDirectory)
control_filesuffix (str) – The filesuffix for the control run’s settings, which used only the most likely values of the default calibration.
nr_samples (int, default 2^4.) – Number of samples. Must be a multiple of 2. The total number of generated samples equals nr_samples * (4 + 2), where 4 is the number of variables used for sampling (ref_mb, prcp_fac, melt_f, temp_bias).
ref_mb_err_sample_interval (float, default 1.0) – Multiplication factor applied to ref_mb_err. It assumes ref_mb has a normal distribution.
prcp_fac_sample_interval (float, default 0.5) –
The width of the bounds which are added and subtracted from the prcp_fac of the control run. A uniform distribution

is used for sampling.
temp_bias_sample_interval (float, default 0.5) –
The width of the bounds which are added and subtracted from the temp_bias of the control run. A uniform distribution

is used for sampling.
melt_f_sample_interval (float, default 0.5) –
The width of the bounds which are added and subtracted from the melt_f of the control run. A uniform distribution

is used for sampling.

Return type:

tuple

get_nearest_datetime(dates, target_date)#

Get the nearest datetime.

Parameters:

dates (array_like) – A range of dates that can be converted to datetime64.
target_date (str) – Date in the ISO-8601 format “YYYY-MM-DD”.

get_ref_mb( gdir=None, ref_mb_period=None, datacube_l1=None, source='Hugonnet', )[source]#

Get observed mass balance for a specific reference period.

Parameters:

gdir (GlacierDirectory)
datacube_l1 (xr.Dataset) – If the reference mass balance should be derived from an observation data, which is available in a L1 datacube.
ref_mb_period (str) – Reference calibration period in the format <YYYY-MM-DD_YYYY-MM-DD>.
source (str) – Define the observation source

Returns:

ref_mb, ref_mb_unit, ref_mb_err, ref_mb_period: the reference mass balance to match with unit, associated uncertainty and valid period.

Return type:

tuple

get_temporal_bounds( dates, date_start, date_end, )[source]#

Get start and end dates of geodetic and observational periods.

This matches the nearest available dates for irregular time indices.

Parameters:

dates (array_like) – Time index. This may be irregularly spaced.
date_start (str) – Start year of a desired period.
date_end (str) – End year of a desired period.

Returns:

Nearest available start and end dates for observations.

Return type:

tuple[datetime]

set_model_matrix(

name='SfcType_Cryosat',

model=<class 'oggm.core.massbalance.SfcTypeTIModel'>,

geo_period='2011-01-01_2020-01-01',

daily=True,

source='CryoTEMPO-EOLIS',

**kwargs,

)[source]#

Set model parameters for calibration.

Parameters:

name (str) – Name of configuration parameters. Ideally this should match the settings filesuffix.
model (oggm.core.massbalance.MassBalanceModel) – OGGM mass balance model class.
geo_period (str) – Reference mass balance period in the format YYYY-MM-DD_YYYY-MM-DD.
kwargs – Additional arguments passed to the mass balance model class.
daily (bool)
source (str)