oggm.core.massbalance.UncertainMassBalance

class oggm.core.massbalance.UncertainMassBalance(basis_model, rdn_temp_bias_seed=None, rdn_temp_bias_sigma=0.1, rdn_prcp_bias_seed=None, rdn_prcp_bias_sigma=0.1, rdn_bias_seed=None, rdn_bias_sigma=100)

Adding uncertainty to a mass balance model.

There are three variables for which you can add uncertainty: - temperature (additive bias) - precipitation (multiplicative factor) - residual (a bias in units of MB)

__init__(basis_model, rdn_temp_bias_seed=None, rdn_temp_bias_sigma=0.1, rdn_prcp_bias_seed=None, rdn_prcp_bias_sigma=0.1, rdn_bias_seed=None, rdn_bias_sigma=100)

Initialize.

Parameters:

basis_model : MassBalanceModel

the model to which you want to add the uncertainty to

rdn_temp_bias_seed : int

the seed of the random number generator

rdn_temp_bias_sigma : float

the standard deviation of the random temperature error

rdn_prcp_bias_seed : int

the seed of the random number generator (to be consistent this should be renamed prcp_fac as well)

rdn_prcp_bias_sigma : float

the standard deviation of the random precipitation error (to be consistent this should be renamed prcp_fac as well)

rdn_bias_seed : int

the seed of the random number generator

rdn_bias_sigma : float

the standard deviation of the random MB error

Methods

__init__(basis_model[, rdn_temp_bias_seed, …])	Initialize.
get_annual_mb(heights[, year, fl_id])	Like self.get_monthly_mb(), but for annual MB.
get_ela([year])	Compute the equilibrium line altitude for this year
get_monthly_mb(heights[, year])	Monthly mass-balance at given altitude(s) for a moment in time.
get_specific_mb([heights, widths, fls, year])	Specific mb for this year and a specific glacier geometry.

Attributes

prcp_bias
prcp_fac	Precipitation factor to apply to the original series.
temp_bias	Temperature bias to add to the original series.