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Feature/tc holland vtrans #833

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Jan 11, 2024
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Feature/tc holland vtrans #833

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f03a61f
Change how vtrans is considered in the implementation of Holland 2008…
Dec 5, 2023
1bd5131
Update CHANGELOG.md
ThomasRoosli Jan 5, 2024
67a3dd8
Merge remote-tracking branch 'origin/develop' into feature/tc_holland…
Jan 5, 2024
529dcbb
trop_cyclone: broadcast_arrays -> broadcast_to
Jan 10, 2024
7522771
Correct typo in CHANGELOG.md
ThomasRoosli Jan 10, 2024
fd5f1cb
remove whitespace in climada/hazard/trop_cyclone.py
ThomasRoosli Jan 10, 2024
d3d9f0c
Update CHANGELOG.md
peanutfun Jan 11, 2024
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@ Code freeze date: YYYY-MM-DD
- `Hazard.from_xarray_raster` now stores strings as default values for `Hazard.event_name` [#795](https://github.com/CLIMADA-project/climada_python/pull/795)
- Fix the dist_approx util function when used with method="geosphere" and log=True and points that are very close. [#792](https://github.com/CLIMADA-project/climada_python/pull/792)
- `climada.util.yearsets.sample_from_poisson`: fix a bug ([#819](https://github.com/CLIMADA-project/climada_python/issues/819)) and inconsistency that occurs when lambda events per year (`lam`) are set to 1. [[#823](https://github.com/CLIMADA-project/climada_python/pull/823)]
- In the TropCyclone class in the Holland model 2008 and 2010 implementation, a doublecounting of translational velocity is removed [#833](https://github.com/CLIMADA-project/climada_python/pull/833)

### Deprecated

Expand Down
33 changes: 20 additions & 13 deletions climada/hazard/test/test_trop_cyclone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,9 +60,10 @@ def test_memory_limit(self):
tc_haz = TropCyclone.from_tracks(tc_track, centroids=CENTR_TEST_BRB, max_memory_gb=0.001)
intensity_idx = [0, 1, 2, 3, 80, 100, 120, 200, 220, 250, 260, 295]
intensity_values = [
25.60778909, 26.90887264, 28.26624642, 25.54092386, 31.21941738, 36.16596567,
21.11399856, 28.01452136, 32.65076804, 31.33884098, 0, 40.27002104,
22.74903, 23.784691, 24.82255, 22.67403, 27.218706, 30.593959,
18.980878, 24.540069, 27.826407, 26.846293, 0., 34.568898,
]

np.testing.assert_array_almost_equal(
tc_haz.intensity[0, intensity_idx].toarray()[0],
intensity_values,
Expand All @@ -72,12 +73,14 @@ def test_set_one_pass(self):
"""Test _tc_from_track function."""
intensity_idx = [0, 1, 2, 3, 80, 100, 120, 200, 220, 250, 260, 295]
intensity_values = {
"geosphere": [25.60794285, 26.90906280, 28.26649026, 25.54076797, 31.21986961,
36.17171808, 21.11408573, 28.01457948, 32.65349378, 31.34027741, 0,
40.27362679],
"equirect": [25.60778909, 26.90887264, 28.26624642, 25.54092386, 31.21941738,
36.16596567, 21.11399856, 28.01452136, 32.65076804, 31.33884098, 0,
40.27002104]
"geosphere": [
22.74927, 23.78498, 24.822908, 22.674202, 27.220042, 30.602122,
18.981022, 24.540138, 27.830925, 26.8489, 0., 34.572391,
],
"equirect": [
22.74903, 23.784691, 24.82255, 22.67403, 27.218706, 30.593959,
18.980878, 24.540069, 27.826407, 26.846293, 0., 34.568898,
]
}
# the values for the two metrics should agree up to first digit at least
for i, val in enumerate(intensity_values["geosphere"]):
Expand Down Expand Up @@ -108,7 +111,7 @@ def test_set_one_pass(self):

self.assertTrue(isinstance(tc_haz.intensity, sparse.csr_matrix))
self.assertEqual(tc_haz.intensity.shape, (1, 296))
self.assertEqual(np.nonzero(tc_haz.intensity)[0].size, 280)
self.assertEqual(np.nonzero(tc_haz.intensity)[0].size, 255)

np.testing.assert_array_almost_equal(
tc_haz.intensity[0, intensity_idx].toarray()[0], intensity_values[metric])
Expand All @@ -127,10 +130,14 @@ def test_windfield_models(self):
"""Test _tc_from_track function with different wind field models."""
intensity_idx = [0, 1, 2, 3, 80, 100, 120, 200, 220, 250, 260, 295]
intensity_values = {
"H08": [25.60778909, 26.90887264, 28.26624642, 25.54092386, 31.21941738, 36.16596567,
21.11399856, 28.01452136, 32.65076804, 31.33884098, 0, 40.27002104],
"H10": [27.604317, 28.720708, 29.894993, 27.52234 , 32.512395, 37.114355,
23.848917, 29.614752, 33.775593, 32.545347, 19.957627, 41.014578],
"H08": [
22.74903, 23.784691, 24.82255, 22.67403, 27.218706, 30.593959,
18.980878, 24.540069, 27.826407, 26.846293, 0., 34.568898,
],
"H10": [
24.745521, 25.596484, 26.475329, 24.690914, 28.650107, 31.584395,
21.723546, 26.140293, 28.94964, 28.051915, 18.49378, 35.312152,
],
# Holland 1980 and Emanuel & Rotunno 2011 use recorded wind speeds, while the above use
# pressure values only. That's why the results are so different:
"H1980": [21.376807, 21.957217, 22.569568, 21.284351, 24.254226, 26.971303,
Expand Down
76 changes: 50 additions & 26 deletions climada/hazard/trop_cyclone.py
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Expand Up @@ -881,7 +881,7 @@
windfields = sparse.vstack(windfields, format="csr")
return intensity, windfields

def _compute_windfields(

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Used when a function or method has too many local variables.
si_track: xr.Dataset,
centroids: np.ndarray,
model: int,
Expand Down Expand Up @@ -955,11 +955,6 @@
si_track, d_centr, close_centr_msk, model, cyclostrophic=False,
)

# vectorial angular velocity
windfields = (
si_track.attrs["latsign"] * np.array([1.0, -1.0])[..., :] * v_centr_normed[:, :, ::-1]
)
windfields[close_centr_msk] *= v_ang_norm[close_centr_msk, None]

# Influence of translational speed decreases with distance from eye.
# The "absorbing factor" is according to the following paper (see Fig. 7):
Expand All @@ -969,11 +964,26 @@
# wind speed profiles in a GIS. UNED/GRID-Geneva.
# https://unepgrid.ch/en/resource/19B7D302
#
t_rad_bc = np.broadcast_arrays(si_track["rad"].values[:, None], d_centr)[0]
t_rad_bc = np.broadcast_to(si_track["rad"].values[:, None], d_centr.shape)
v_trans_corr = np.zeros_like(d_centr)
v_trans_corr[close_centr_msk] = np.fmin(
1, t_rad_bc[close_centr_msk] / d_centr[close_centr_msk])

if model in [MODEL_VANG['H08'], MODEL_VANG['H10']]:
# In these models, v_ang_norm already contains vtrans_norm, so subtract it first, before
# converting to vectors and then adding (vectorial) vtrans again. Make sure to apply the
# "absorbing factor" in both steps:
vtrans_norm_bc = np.broadcast_to(si_track["vtrans_norm"].values[:, None], d_centr.shape)
v_ang_norm[close_centr_msk] -= (
vtrans_norm_bc[close_centr_msk] * v_trans_corr[close_centr_msk]
)

# vectorial angular velocity
windfields = (
si_track.attrs["latsign"] * np.array([1.0, -1.0])[..., :] * v_centr_normed[:, :, ::-1]
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)
windfields[close_centr_msk] *= v_ang_norm[close_centr_msk, None]

# add angular and corrected translational velocity vectors
windfields[1:] += si_track["vtrans"].values[1:, None, :] * v_trans_corr[1:, :, None]
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windfields[np.isnan(windfields)] = 0
Expand Down Expand Up @@ -1407,11 +1417,15 @@
"""
hol_x = np.zeros_like(d_centr)
r_max, v_max_s, hol_b, d_centr, v_n, r_n = [
ar[close_centr] for ar in np.broadcast_arrays(
si_track["rad"].values[:, None], si_track["vmax"].values[:, None],
si_track["hol_b"].values[:, None], d_centr,
np.atleast_1d(v_n)[:, None], np.atleast_1d(r_n_km)[:, None],
)
np.broadcast_to(ar, d_centr.shape)[close_centr]
for ar in [
si_track["rad"].values[:, None],
si_track["vmax"].values[:, None],
si_track["hol_b"].values[:, None],
d_centr,
np.atleast_1d(v_n)[:, None],
np.atleast_1d(r_n_km)[:, None],
]
]

# convert to SI units
Expand Down Expand Up @@ -1470,11 +1484,15 @@
Absolute values of wind speeds (in m/s) in angular direction.
"""
v_ang = np.zeros_like(d_centr)
d_centr, v_max_s, r_max, hol_b, hol_x = [
ar[close_centr] for ar in np.broadcast_arrays(
d_centr, si_track["vmax"].values[:, None], si_track["rad"].values[:, None],
si_track["hol_b"].values[:, None], hol_x,
)
v_max_s, r_max, hol_b, d_centr, hol_x = [
np.broadcast_to(ar, d_centr.shape)[close_centr]
for ar in [
si_track["vmax"].values[:, None],
si_track["rad"].values[:, None],
si_track["hol_b"].values[:, None],
d_centr,
hol_x,
]
]

r_max_norm = (r_max / np.fmax(1, d_centr))**hol_b
Expand Down Expand Up @@ -1526,12 +1544,16 @@
Absolute values of wind speeds (m/s) in angular direction.
"""
v_ang = np.zeros_like(d_centr)
d_centr, r_max, hol_b, penv, pcen, coriolis_p = [
ar[close_centr] for ar in np.broadcast_arrays(
d_centr, si_track["rad"].values[:, None], si_track["hol_b"].values[:, None],
si_track["env"].values[:, None], si_track["cen"].values[:, None],
si_track["cp"].values[:, None]
)
r_max, hol_b, penv, pcen, coriolis_p, d_centr = [
np.broadcast_to(ar, d_centr.shape)[close_centr]
for ar in [
si_track["rad"].values[:, None],
si_track["hol_b"].values[:, None],
si_track["env"].values[:, None],
si_track["cen"].values[:, None],
si_track["cp"].values[:, None],
d_centr,
]
]

r_coriolis = 0
Expand Down Expand Up @@ -1587,12 +1609,14 @@
Absolute values of wind speeds (m/s) in angular direction.
"""
v_ang = np.zeros_like(d_centr)
d_centr, r_max, v_max, coriolis_p = [
ar[close_centr] for ar in np.broadcast_arrays(
d_centr, si_track["rad"].values[:, None],
r_max, v_max, coriolis_p, d_centr = [
np.broadcast_to(ar, d_centr.shape)[close_centr]
for ar in [
si_track["rad"].values[:, None],
si_track["vmax"].values[:, None],
si_track["cp"].values[:, None],
)
d_centr,
]
]

# compute the momentum at the maximum
Expand Down
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