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)[source]

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)[source]

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.