def test_specific_mb(self):
"""Compare the specific mass balance to the one computed
using the OGGM function of the PastMassBalance model.
"""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# instance mb models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
past_mbmod = massbalance.PastMassBalance(gdir)
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
# define temporal range
ys = 1802
ye = 2003
years = np.arange(ys, ye + 1)
# get flow lines
fls = gdir.read_pickle('inversion_flowlines')
# create empty container
past_mb = np.empty(years.size)
vas_mb = np.empty(years.size)
# get specific mass balance for all years
for i, year in enumerate(years):
past_mb[i] = past_mbmod.get_specific_mb(fls=fls, year=year)
vas_mb[i] = vas_mbmod.get_specific_mb(min_hgt, max_hgt, year)
# compute and check correlation
assert corrcoef(past_mb, vas_mb) >= 0.94
# relative error of average spec mb
# TODO: does this even make any sense?!
assert np.abs(rel_err(past_mb.mean(), vas_mb.mean())) <= 0.36
# check correlation of positive and negative mb years
assert corrcoef(np.sign(past_mb), np.sign(vas_mb)) >= 0.72
# compare to reference mb measurements
mbs = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
assert corrcoef(vas_mb[np.in1d(years, mbs.index)], mbs) >= 0.79
def test_annual_climate(self):
"""Test my routine against the corresponding OGGM routine from
the `PastMassBalance()` model.
"""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# instance the mass balance models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
past_mbmod = massbalance.PastMassBalance(gdir)
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
heights = np.array([min_hgt, (min_hgt + max_hgt) / 2, max_hgt])
# specify an (arbitray) year
year = 1975
# get mass balance relevant climate information
temp_for_melt_vas, prcp_solid_vas = \
vas_mbmod.get_annual_climate(min_hgt, max_hgt, year)
_, temp_for_melt_oggm, _, prcp_solid_oggm = \
past_mbmod.get_annual_climate(heights, year)
# prepare my (monthly) values for comparison
temp_for_melt_vas = temp_for_melt_vas.sum()
prcp_solid_vas = prcp_solid_vas.sum()
# computed positive terminus melting temperature must be equal for both
# used methods, i.e. temp_VAS == temp_OGGM
np.testing.assert_allclose(temp_for_melt_vas,
temp_for_melt_oggm[0],
rtol=1e-3)
# glacier averaged solid precipitation amount must be greater than (or
# equal to) solid precipitation amount at glacier terminus elevation
assert md(prcp_solid_oggm[0], prcp_solid_vas) >= 0
# glacier averaged solid precipitation amount must be comparable to the
# solid precipitation amount at average glacier surface elevation
assert rel_err(prcp_solid_oggm[1], prcp_solid_vas) <= 0.15
# glacier averaged solid precipitation amount must be less than (or
# equal to) solid precipitation amount at maximum glacier elevation
assert md(prcp_solid_oggm[2], prcp_solid_vas) <= 0
def _find_inital_glacier(final_model,
firstguess_mb,
y0,
y1,
rtol=0.01,
atol=10,
max_ite=100,
init_bias=0.,
equi_rate=0.0005,
ref_area=None):
""" Iterative search for a plausible starting time glacier"""
# Objective
if ref_area is None:
ref_area = final_model.area_m2
log.info(
'find_inital_glacier in year %d. Ref area to catch: %.3f km2. '
'Tolerance: %.2f %%', np.int64(y0), ref_area * 1e-6, rtol * 100)
# are we trying to grow or to shrink the glacier?
prev_model = copy.deepcopy(final_model)
prev_fls = copy.deepcopy(prev_model.fls)
prev_model.reset_y0(y0)
prev_model.run_until(y1)
prev_area = prev_model.area_m2
# Just in case we already hit the correct starting state
if np.allclose(prev_area, ref_area, atol=atol, rtol=rtol):
model = copy.deepcopy(final_model)
model.reset_y0(y0)
log.info(
'find_inital_glacier: inital starting glacier converges '
'to itself with a final dif of %.2f %%',
utils.rel_err(ref_area, prev_area) * 100)
return 0, None, model
if prev_area < ref_area:
sign_mb = 1.
log.info(
'find_inital_glacier, ite: %d. Glacier would be too '
'small of %.2f %%. Continue', 0,
utils.rel_err(ref_area, prev_area) * 100)
else:
log.info(
'find_inital_glacier, ite: %d. Glacier would be too '
'big of %.2f %%. Continue', 0,
utils.rel_err(ref_area, prev_area) * 100)
sign_mb = -1.
# Log prefix
logtxt = 'find_inital_glacier'
# Loop until 100 iterations
c = 0
bias_step = 50.
mb_bias = init_bias - bias_step
reduce_step = 5.
mb = copy.deepcopy(firstguess_mb)
mb.set_bias(sign_mb * mb_bias)
grow_model = FluxBasedModel(copy.deepcopy(final_model.fls),
mb_model=mb,
fs=final_model.fs,
glen_a=final_model.glen_a,
min_dt=final_model.min_dt,
max_dt=final_model.max_dt)
while True and (c < max_ite):
c += 1
# Grow
mb_bias += bias_step
mb.set_bias(sign_mb * mb_bias)
log.info(logtxt + ', ite: %d. New bias: %.0f', c, sign_mb * mb_bias)
grow_model.reset_flowlines(copy.deepcopy(prev_fls))
grow_model.reset_y0(0.)
grow_model.run_until_equilibrium(rate=equi_rate)
log.info(
logtxt + ', ite: %d. Grew to equilibrium for %d years, '
'new area: %.3f km2', c, grow_model.yr, grow_model.area_km2)
# Shrink
new_fls = copy.deepcopy(grow_model.fls)
new_model = copy.deepcopy(final_model)
new_model.reset_flowlines(copy.deepcopy(new_fls))
new_model.reset_y0(y0)
new_model.run_until(y1)
new_area = new_model.area_m2
# Maybe we done?
if np.allclose(new_area, ref_area, atol=atol, rtol=rtol):
new_model.reset_flowlines(new_fls)
new_model.reset_y0(y0)
log.info(
logtxt + ', ite: %d. Converged with a '
'final dif of %.2f %%', c,
utils.rel_err(ref_area, new_area) * 100)
return c, mb_bias, new_model
# See if we did a step to far or if we have to continue growing
do_cont_1 = (sign_mb > 0.) and (new_area < ref_area)
do_cont_2 = (sign_mb < 0.) and (new_area > ref_area)
if do_cont_1 or do_cont_2:
# Reset the previous state and continue
prev_fls = new_fls
log.info(logtxt + ', ite: %d. Dif of %.2f %%. '
'Continue', c,
utils.rel_err(ref_area, new_area) * 100)
continue
# Ok. We went too far. Reduce the bias step but keep previous state
mb_bias -= bias_step
bias_step /= reduce_step
log.info(logtxt + ', ite: %d. Went too far.', c)
if bias_step < 0.1:
break
raise RuntimeError('Did not converge after {} iterations'.format(c))
def _find_inital_glacier(final_model, firstguess_mb, y0, y1,
rtol=0.01, atol=10, max_ite=100,
init_bias=0., equi_rate=0.0005,
ref_area=None):
""" Iterative search for a plausible starting time glacier"""
# Objective
if ref_area is None:
ref_area = final_model.area_m2
log.info('find_inital_glacier in year %d. Ref area to catch: %.3f km2. '
'Tolerance: %.2f %%' ,
np.int64(y0), ref_area*1e-6, rtol*100)
# are we trying to grow or to shrink the glacier?
prev_model = copy.deepcopy(final_model)
prev_fls = copy.deepcopy(prev_model.fls)
prev_model.reset_y0(y0)
prev_model.run_until(y1)
prev_area = prev_model.area_m2
# Just in case we already hit the correct starting state
if np.allclose(prev_area, ref_area, atol=atol, rtol=rtol):
model = copy.deepcopy(final_model)
model.reset_y0(y0)
log.info('find_inital_glacier: inital starting glacier converges '
'to itself with a final dif of %.2f %%',
utils.rel_err(ref_area, prev_area) * 100)
return 0, None, model
if prev_area < ref_area:
sign_mb = 1.
log.info('find_inital_glacier, ite: %d. Glacier would be too '
'small of %.2f %%. Continue', 0,
utils.rel_err(ref_area, prev_area) * 100)
else:
log.info('find_inital_glacier, ite: %d. Glacier would be too '
'big of %.2f %%. Continue', 0,
utils.rel_err(ref_area, prev_area) * 100)
sign_mb = -1.
# Log prefix
logtxt = 'find_inital_glacier'
# Loop until 100 iterations
c = 0
bias_step = 50.
mb_bias = init_bias - bias_step
reduce_step = 5.
mb = copy.deepcopy(firstguess_mb)
mb.set_bias(sign_mb * mb_bias)
grow_model = FluxBasedModel(copy.deepcopy(final_model.fls), mb_model=mb,
fs=final_model.fs,
glen_a=final_model.glen_a,
min_dt=final_model.min_dt,
max_dt=final_model.max_dt)
while True and (c < max_ite):
c += 1
# Grow
mb_bias += bias_step
mb.set_bias(sign_mb * mb_bias)
log.info(logtxt + ', ite: %d. New bias: %.0f', c, sign_mb * mb_bias)
grow_model.reset_flowlines(copy.deepcopy(prev_fls))
grow_model.reset_y0(0.)
grow_model.run_until_equilibrium(rate=equi_rate)
log.info(logtxt + ', ite: %d. Grew to equilibrium for %d years, '
'new area: %.3f km2', c, grow_model.yr,
grow_model.area_km2)
# Shrink
new_fls = copy.deepcopy(grow_model.fls)
new_model = copy.deepcopy(final_model)
new_model.reset_flowlines(copy.deepcopy(new_fls))
new_model.reset_y0(y0)
new_model.run_until(y1)
new_area = new_model.area_m2
# Maybe we done?
if np.allclose(new_area, ref_area, atol=atol, rtol=rtol):
new_model.reset_flowlines(new_fls)
new_model.reset_y0(y0)
log.info(logtxt + ', ite: %d. Converged with a '
'final dif of %.2f %%', c,
utils.rel_err(ref_area, new_area)*100)
return c, mb_bias, new_model
# See if we did a step to far or if we have to continue growing
do_cont_1 = (sign_mb > 0.) and (new_area < ref_area)
do_cont_2 = (sign_mb < 0.) and (new_area > ref_area)
if do_cont_1 or do_cont_2:
# Reset the previous state and continue
prev_fls = new_fls
log.info(logtxt + ', ite: %d. Dif of %.2f %%. '
'Continue', c,
utils.rel_err(ref_area, new_area)*100)
continue
# Ok. We went too far. Reduce the bias step but keep previous state
mb_bias -= bias_step
bias_step /= reduce_step
log.info(logtxt + ', ite: %d. Went too far.', c)
if bias_step < 0.1:
break
raise RuntimeError('Did not converge after {} iterations'.format(c))