def run_constant_climate_with_bias(gdir,
temp_bias_ts=None,
prcp_fac_ts=None,
ys=None,
ye=None,
y0=2014,
halfsize=5,
climate_filename='climate_historical',
climate_input_filesuffix='',
output_filesuffix='',
init_model_fls=None,
init_model_filesuffix=None,
init_model_yr=None,
bias=None,
**kwargs):
"""Runs a glacier with temperature and precipitation correction timeseries.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
temp_bias_ts : pandas DataFrame
the temperature bias timeseries (in °C) (index: time as years)
prcp_fac_ts : pandas DataFrame
the precipitaion bias timeseries (in % change, positive or negative)
(index: time as years)
y0 : int
central year of the constant climate period
halfsize : int
half-size of the constant climate period
climate_filename : str
name of the climate file, e.g. 'climate_historical' (default) or 'gcm_data'
climate_input_filesuffix: str
filesuffix for the input climate file
output_filesuffix : str
for the output file
init_model_filesuffix : str
if you want to start from a previous model run state. Can be
combined with `init_model_yr`
init_model_yr : int
the year of the initial run you want to start from. The default
is to take the last year available
bias : float
bias of the mb model. Default is to use the calibrated one, which
is often a better idea. For t* experiments it can be useful to set it
to zero
kwargs : dict
kwargs to pass to the FluxBasedModel instance
"""
if init_model_filesuffix is not None:
fp = gdir.get_filepath('model_geometry',
filesuffix=init_model_filesuffix)
fmod = FileModel(fp)
if init_model_yr is None:
init_model_yr = fmod.last_yr
# Avoid issues here
if init_model_yr > fmod.y0:
fmod.run_until(init_model_yr)
else:
fmod.run_until(fmod.y0)
init_model_fls = fmod.fls
# Final crop
mb = BiasedConstantMassBalance(gdir,
temp_bias_ts=temp_bias_ts,
prcp_fac_ts=prcp_fac_ts,
y0=y0,
bias=bias,
halfsize=halfsize,
filename=climate_filename,
input_filesuffix=climate_input_filesuffix)
# Decide from climate
if ye is None:
ye = mb.ye
if ys is None:
ys = mb.ys
return flowline_model_run(gdir,
output_filesuffix=output_filesuffix,
mb_model=mb,
ys=ys,
ye=ye,
init_model_fls=init_model_fls,
**kwargs)
def run_uncertain_random_climate(gdir,
nyears=700,
output_filesuffix='',
sigma_t=None,
sigma_p=None,
sigma_smb=None,
rdn_temp_bias_seed=None,
rdn_prcp_fac_seed=None,
rdn_bias_seed=None,
orig_mb_tbias=0,
orig_mb_pbias=1,
orig_mb_sbias=0,
**kwargs):
"""Test stuff
Parameters
----------
gdir
nyears
output_filesuffix
rdn_temp_bias_seed
rdn_prcp_bias_seed
rdn_bias_seed
kwargs
Returns
-------
"""
# Find the optimal bias for a balanced MB
mbref = mbmods.PastMassBalance(gdir)
h, w = gdir.get_inversion_flowline_hw()
cyrs = np.unique(mbref.years)
def to_minimize(x):
mbref.bias = x
return np.mean(mbref.get_specific_mb(h, w, cyrs))
opti_bias = optimization.brentq(to_minimize, -10000, 10000, xtol=0.01)
mbref.bias = opti_bias
assert np.allclose(np.mean(mbref.get_specific_mb(h, w, cyrs)),
0,
atol=0.01)
rdf = pd.DataFrame(index=cyrs)
for y in cyrs:
rdf.loc[y, 'SMB'] = mbref.get_specific_mb(h, w, year=y)
t, _, p, _ = mbref.get_annual_climate([np.mean(h)], year=y)
rdf.loc[y, 'TEMP'] = t
rdf.loc[y, 'PRCP'] = p
if sigma_smb is None:
sigma_smb = rdf.std()['SMB'] / 2
if sigma_t is None:
sigma_t = rdf.std()['TEMP'] / 2
if sigma_p is None:
sigma_p = (rdf.std() / rdf.mean())['PRCP'] / 2
mb = mbmods.RandomMassBalance(gdir, all_years=True, seed=0)
mb.mbmod.temp_bias = orig_mb_tbias
mb.mbmod.prcp_bias = orig_mb_pbias
mb.mbmod.bias = orig_mb_sbias + opti_bias
rmb = mbmods.UncertainMassBalance(mb,
rdn_temp_bias_seed=rdn_temp_bias_seed,
rdn_temp_bias_sigma=sigma_t,
rdn_prcp_fac_seed=rdn_prcp_fac_seed,
rdn_prcp_fac_sigma=sigma_p,
rdn_bias_seed=rdn_bias_seed,
rdn_bias_sigma=sigma_smb)
return flowline_model_run(gdir,
output_filesuffix=output_filesuffix,
mb_model=rmb,
ys=0,
ye=nyears,
**kwargs)
def run_from_magicc_data(gdir,
magicc_ts=None,
ys=None,
ye=None,
y0=2014,
halfsize=5,
use_dt_per_dt=True,
use_dp_per_dt=True,
climate_filename='climate_historical',
climate_input_filesuffix='',
output_filesuffix='',
init_model_filesuffix=None,
init_model_yr=None,
bias=None,
**kwargs):
"""Runs a glacier with climate input from MAGICC.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
ys : int
start year of the model run (default: from the glacier geometry
date if init_model_filesuffix is None, else init_model_yr)
ye : int
end year of the model run (default: last year of the magicc file)
y0 : int
central year where to apply the MAGICC anomaly method
halfsize : int
half-size of the MAGICC anomaly window
climate_filename : str
name of the climate file, e.g. 'climate_historical' (default) or
'gcm_data'
climate_input_filesuffix: str
filesuffix for the input climate file
output_filesuffix : str
for the output file
init_model_filesuffix : str
if you want to start from a previous model run state. Can be
combined with `init_model_yr`
init_model_yr : int
the year of the initial run you want to start from. The default
is to take the y0 - halfsize
bias : float
bias of the mb model. Default is to use the calibrated one, which
is often a better idea. For t* experiments it can be useful to set it
to zero
kwargs : dict
kwargs to pass to the FluxBasedModel instance
"""
if init_model_yr is None:
init_model_yr = y0 - halfsize
if init_model_filesuffix is not None:
fp = gdir.get_filepath('model_geometry',
filesuffix=init_model_filesuffix)
fmod = FileModel(fp)
if init_model_yr is None:
init_model_yr = fmod.last_yr
# Avoid issues here
if init_model_yr > fmod.y0:
fmod.run_until(init_model_yr)
else:
fmod.run_until(fmod.y0)
init_model_fls = fmod.fls
if ys is None:
ys = init_model_yr
else:
raise InvalidParamsError('We strongly recommend to start from '
'prepro for this task.')
# Take from rgi date if not set yet
if ys is None:
raise InvalidParamsError('ys should not be guessed at this point')
dt_per_dt = 1.
dp_per_dt = 0.
if use_dt_per_dt:
dt_per_dt = np.asarray(gdir.get_diagnostics()['magicc_dt_per_dt'])
if use_dp_per_dt:
dp_per_dt = np.asarray(gdir.get_diagnostics()['magicc_dp_per_dt'])
# Final crop
mb = MagiccMassBalance(gdir,
magicc_ts=magicc_ts,
y0=y0,
halfsize=halfsize,
dt_per_dt=dt_per_dt,
dp_per_dt=dp_per_dt,
filename=climate_filename,
bias=bias,
input_filesuffix=climate_input_filesuffix)
if ye is None:
# Decide from climate
ye = mb.ye
return flowline_model_run(gdir,
output_filesuffix=output_filesuffix,
mb_model=mb,
ys=ys,
ye=ye,
init_model_fls=init_model_fls,
**kwargs)