def get_mean_temps_2k(rgi, return_prcp):
from oggm import cfg, utils, workflow, tasks
from oggm.core.massbalance import PastMassBalance
# Initialize OGGM
cfg.initialize()
wd = utils.gettempdir(reset=True)
cfg.PATHS['working_dir'] = wd
utils.mkdir(wd, reset=True)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# and set standard histalp values
cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['prcp_scaling_factor'] = 1.75
gdir = workflow.init_glacier_regions(rgidf=rgi.split('_')[0],
from_prepro_level=3,
prepro_border=10)[0]
# run histalp climate on glacier!
tasks.process_histalp_data(gdir)
f = gdir.get_filepath('climate_historical')
with utils.ncDataset(f) as nc:
refhgt = nc.ref_hgt
mb = PastMassBalance(gdir, check_calib_params=False)
df = pd.DataFrame()
df2 = pd.DataFrame()
for y in np.arange(1870, 2015):
for i in np.arange(9, 12):
flyear = utils.date_to_floatyear(y, i)
tmp = mb.get_monthly_climate([refhgt], flyear)[0]
df.loc[y, i] = tmp.mean()
if return_prcp:
for i in np.arange(3, 6):
flyear = utils.date_to_floatyear(y, i)
pcp = mb.get_monthly_climate([refhgt], flyear)[3]
df2.loc[y, i] = tmp.mean()
t99 = df.loc[1984:2014, :].mean().mean()
t85 = df.loc[1870:1900, :].mean().mean()
t2k = df.loc[1900:2000, :].mean().mean()
if return_prcp:
p99 = df2.loc[1984:2014, :].mean().mean()
p85 = df2.loc[1870:1900, :].mean().mean()
p2k = df2.loc[1900:2000, :].mean().mean()
return t85, t99, t2k, p85, p99, p2k
return t85, t99, t2k
def get_monthly_climate(self, heights, year=None):
"""Average climate information at given heights.
Note that prcp is corrected with the precipitation factor and that
all other biases (precipitation, temp) are applied
Returns
-------
(temp, tempformelt, prcp, prcpsol)
"""
_, m = floatyear_to_date(year)
yrs = [date_to_floatyear(y, m) for y in self.years]
heights = np.atleast_1d(heights)
nh = len(heights)
shape = (len(yrs), nh)
temp = np.zeros(shape)
tempformelt = np.zeros(shape)
prcp = np.zeros(shape)
prcpsol = np.zeros(shape)
for i, yr in enumerate(yrs):
t, tm, p, ps = self.mbmod.get_monthly_climate(heights, year=yr)
temp[i, :] = t
tempformelt[i, :] = tm
prcp[i, :] = p
prcpsol[i, :] = ps
return (np.mean(temp, axis=0), np.mean(tempformelt, axis=0),
np.mean(prcp, axis=0), np.mean(prcpsol, axis=0))
def interp_m(self):
# monthly MB
months = np.arange(12) + 1
interp_m = []
for m in months:
mb_on_h = self.hbins * 0.
for yr in self.years:
yr = date_to_floatyear(yr, m)
mb_on_h += self.mbmod.get_monthly_mb(self.hbins, year=yr)
interp_m.append(interp1d(self.hbins, mb_on_h / len(self.years)))
return interp_m
def interp_m(self):
# monthly MB
months = np.arange(12)+1
interp_m = []
for m in months:
mb_on_h = self.hbins*0.
for yr in self.years:
yr = date_to_floatyear(yr, m)
mb_on_h += self.mbmod.get_monthly_mb(self.hbins, year=yr)
interp_m.append(interp1d(self.hbins, mb_on_h / len(self.years)))
return interp_m
def get_monthly_mb(self, heights, year=None, add_climate=False, **kwargs):
_, m = floatyear_to_date(year)
mb_on_h = heights * 0.
for yr in self.years:
yr = date_to_floatyear(yr, m)
mb_on_h += self.mbmod.get_monthly_mb(heights, year=yr)
mb_on_h /= len(self.years)
if add_climate:
t, tmelt, prcp, prcpsol = self.get_monthly_climate(heights,
year=year)
return mb_on_h, t, tmelt, prcp, prcpsol
return mb_on_h
def test_date_to_floatyear(self):
r = utils.date_to_floatyear(0, 1)
self.assertEqual(r, 0)
r = utils.date_to_floatyear(1, 1)
self.assertEqual(r, 1)
r = utils.date_to_floatyear([0, 1], [1, 1])
np.testing.assert_array_equal(r, [0, 1])
yr = utils.date_to_floatyear([1998, 1998], [6, 7])
y, m = utils.floatyear_to_date(yr)
np.testing.assert_array_equal(y, [1998, 1998])
np.testing.assert_array_equal(m, [6, 7])
yr = utils.date_to_floatyear([1998, 1998], [2, 3])
y, m = utils.floatyear_to_date(yr)
np.testing.assert_array_equal(y, [1998, 1998])
np.testing.assert_array_equal(m, [2, 3])
time = pd.date_range('1/1/1800', periods=300 * 12 - 11, freq='MS')
yr = utils.date_to_floatyear(time.year, time.month)
y, m = utils.floatyear_to_date(yr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
myr = utils.monthly_timeseries(1800, 2099)
y, m = utils.floatyear_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
myr = utils.monthly_timeseries(1800, ny=300)
y, m = utils.floatyear_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
time = pd.period_range('0001-01', '6000-1', freq='M')
myr = utils.monthly_timeseries(1, 6000)
y, m = utils.floatyear_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
time = pd.period_range('0001-01', '6000-12', freq='M')
myr = utils.monthly_timeseries(1, 6000, include_last_year=True)
y, m = utils.floatyear_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
with self.assertRaises(ValueError):
utils.monthly_timeseries(1)
def get_monthly_mb(self, heights, year=None):
ryr, m = floatyear_to_date(year)
ryr = date_to_floatyear(self.get_state_yr(ryr), m)
return self.mbmod.get_monthly_mb(heights, year=ryr)
def get_monthly_mb(self, heights, year=None, fl_id=None):
ryr, m = floatyear_to_date(year)
ryr = date_to_floatyear(self.get_state_yr(ryr), m)
return self.mbmod.get_monthly_mb(heights, year=ryr)
def get_mean_temps_eq(rgi, histalp_storage, comit_storage, ensmembers):
from oggm import cfg, utils, GlacierDirectory
from oggm.core.massbalance import MultipleFlowlineMassBalance
from oggm.core.flowline import FileModel
import shutil
# 1. get mean surface heights
df85 = pd.DataFrame([])
df99 = pd.DataFrame([])
for i in range(ensmembers):
fnc1 = os.path.join(comit_storage, rgi,
'model_run_commitment1885_{:02d}.nc'.format(i))
fnc2 = os.path.join(comit_storage, rgi,
'model_run_commitment1999_{:02d}.nc'.format(i))
tmpmod1 = FileModel(fnc1)
tmpmod2 = FileModel(fnc2)
for j in np.arange(270, 301):
tmpmod1.run_until(j)
df85.loc[:, '{}{}'.format(i, j)] = tmpmod1.fls[-1].surface_h
tmpmod2.run_until(j)
df99.loc[:, '{}{}'.format(i, j)] = tmpmod2.fls[-1].surface_h
meanhgt99 = df99.mean(axis=1).values
meanhgt85 = df85.mean(axis=1).values
# 2. get the climate
# Initialize OGGM
cfg.initialize()
wd = utils.gettempdir(reset=True)
cfg.PATHS['working_dir'] = wd
utils.mkdir(wd, reset=True)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# and set standard histalp values
cfg.PARAMS['temp_melt'] = -1.75
i = 0
storage_dir = os.path.join(histalp_storage, rgi, '{:02d}'.format(i),
rgi[:8], rgi[:11], rgi)
new_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier',
rgi[:8], rgi[:11], rgi)
shutil.copytree(storage_dir, new_dir)
gdir = GlacierDirectory(rgi)
mb = MultipleFlowlineMassBalance(gdir, filename='climate_monthly',
check_calib_params=False)
# need to do the above for every ensemble member if I consider PRECIP!
# and set cfg.PARAMS['prcp_scaling_factor'] = pdict['prcp_scaling_factor']
df99_2 = pd.DataFrame()
df85_2 = pd.DataFrame()
for i in np.arange(9, 12):
for y in np.arange(1870, 1901):
flyear = utils.date_to_floatyear(y, i)
tmp = mb.flowline_mb_models[-1].get_monthly_climate(meanhgt85,
flyear)[0]
df85_2.loc[y, i] = tmp.mean()
for y in np.arange(1984, 2015):
tmp = mb.flowline_mb_models[-1].get_monthly_climate(meanhgt99,
flyear)[0]
df99_2.loc[y, i] = tmp.mean()
t99 = df99_2.mean().mean()
t85 = df85_2.mean().mean()
return t85, t99
def test_monthly_glacier_massbalance(gdir):
# TODO: problem with that, monthly and annual MB not exactly the same!!!
# I think there is a problem with SEC_IN_MONTH/SEC_IN_YEAR ...
# do this for all model types
# ONLY TEST it for ERA5dr or ERA5_daily!!!
for climate in ['ERA5dr', 'ERA5_daily']:
for mb_type in ['mb_monthly', 'mb_daily', 'mb_real_daily']:
for grad_type in ['cte', 'var_an_cycle']:
if grad_type == 'var_an_cycle':
fail_err_4 = ((mb_type == 'mb_monthly')
and (climate == 'CRU'))
mu_star_opt = mu_star_opt_var
else:
fail_err_4 = False
mu_star_opt = mu_star_opt_cte
if climate == 'ERA5dr':
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset="ERA5dr")
elif climate == 'ERA5_daily':
process_era5_daily_data(gdir)
cfg.PARAMS['baseline_climate'] = 'ERA5_daily'
else:
tasks.process_climate_data(gdir)
pass
# mb_type ='mb_daily'
fail_err_1 = (mb_type == 'mb_daily') and (climate != 'ERA5dr')
fail_err_2 = ((mb_type == 'mb_monthly')
and (climate == 'ERA5_daily'))
fail_err_3 = ((mb_type == 'mb_real_daily')
and (climate != 'ERA5_daily'))
if fail_err_1 or fail_err_2 or fail_err_3 or fail_err_4:
with pytest.raises(InvalidParamsError):
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
else:
# but this is just a test for reproducibility!
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
hgts, widths = gdir.get_inversion_flowline_hw()
rho = 900 # ice density
yrp = [1980, 2018]
for i, yr in enumerate(np.arange(yrp[0], yrp[1] + 1)):
my_mon_mb_on_h = 0.
dayofyear = 0
for m in np.arange(12):
yrm = utils.date_to_floatyear(yr, m + 1)
_, dayofmonth = monthrange(yr, m + 1)
dayofyear += dayofmonth
tmp = (mb_mod.get_monthly_mb(hgts, yrm) *
dayofmonth * SEC_IN_DAY * rho)
my_mon_mb_on_h += tmp
my_an_mb_on_h = (mb_mod.get_annual_mb(hgts, yr) *
dayofyear * SEC_IN_DAY * rho)
# these large errors might come from the problematic of
# different amount of days in a year?
# or maybe it just does not fit ...
assert_allclose(np.mean(my_an_mb_on_h -
my_mon_mb_on_h),
0,
atol=100)
