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Bug fix/storm speed #104

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merged 7 commits into from
Jul 25, 2024
Merged

Bug fix/storm speed #104

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5e7b4ca
correction for speed and bearing calculation, previously applied comp…
WPringle Jun 25, 2024
d43edea
updated the modified reference files. best-track is unchanged except …
WPringle Jun 25, 2024
90679be
handling special case where no previous times available for new forecast
WPringle Jul 11, 2024
de477ed
make sure ^Cw is not larger than the maximum radii of the strongest i…
WPringle Jul 11, 2024
90a1eaa
reformat
WPringle Jul 11, 2024
d67e3d5
Fix tests
SorooshMani-NOAA Jul 22, 2024
0f93840
check for negative time intervals and use abs for computing speed and…
WPringle Jul 25, 2024
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76 changes: 54 additions & 22 deletions stormevents/nhc/track.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1072,17 +1072,25 @@ def __compute_velocity(data: DataFrame) -> DataFrame:
for advisory in pandas.unique(data["advisory"]):
advisory_data = data.loc[data["advisory"] == advisory]

indices = numpy.array(
[
numpy.where(advisory_data["datetime"] == unique_datetime)[0][0]
for unique_datetime in pandas.unique(advisory_data["datetime"])
]
)
indices = advisory_data.index
shifted_indices = numpy.roll(indices, 1)
shifted_indices[0] = 0

indices = advisory_data.index[indices]
shifted_indices = advisory_data.index[shifted_indices]
shifted_indices[0] = indices[0]

# check for negative time shifts which indicate new forecasts
# and update this with the last previously available time
for counter, ind in enumerate(zip(indices, shifted_indices)):
this_time = advisory_data.loc[ind[0], "datetime"]
shift_time = advisory_data.loc[ind[1], "datetime"]
if shift_time > this_time:
# update shift index
if (advisory_data["datetime"] < this_time).sum() == 0:
shifted_indices[counter] = advisory_data["datetime"][
advisory_data["datetime"] > this_time
].index[0]
Comment on lines +1086 to +1089
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@WPringle can you please explain what this does? It says that if there's no time in the dataframe smaller than the current row we're processing, then the shifted time ("previous time") should be the first time that is larger than the current time. Why?! This gives us opposite bearing, right?

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That's a good point about the bearing! This could be a bug that needs fixing.

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It seems all the tests are passing. The coverage test runs into a timeout issue. Do you want me to go ahead with this merge and then later explore the bearing issue in a separate ticket or do you want me to wait for you to test the potential bearing issue?

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Thanks for this! Please wait until I double check everything. I will let you know once happy.

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@WPringle WPringle Jul 25, 2024 •
edited
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OK for the bearing when the shifted time is larger than the current time, we can use the forward_azimuth instead of inverse_azimuth. The change has been added in latest commit. Note this is overall a rare case.

else:
shifted_indices[counter] = advisory_data["datetime"][
advisory_data["datetime"] < this_time
].index[-1]

_, inverse_azimuths, distances = geodetic.inv(
advisory_data.loc[indices, "longitude"],
Expand All @@ -1091,21 +1099,31 @@ def __compute_velocity(data: DataFrame) -> DataFrame:
advisory_data.loc[shifted_indices, "latitude"],
)

intervals = advisory_data.loc[indices, "datetime"].diff()
speeds = distances / (intervals / pandas.to_timedelta(1, "s"))
bearings = pandas.Series(inverse_azimuths % 360, index=speeds.index)

for index in indices:
cluster_index = (
advisory_data["datetime"] == advisory_data.loc[index, "datetime"]
intervals = (
(
abs(
advisory_data.loc[indices, "datetime"].values
- advisory_data.loc[shifted_indices, "datetime"].values
)
)
advisory_data.loc[cluster_index, "speed"] = speeds[index]
advisory_data.loc[cluster_index, "direction"] = bearings[index]
.astype("timedelta64[s]")
.astype(float)
)
speeds = pandas.Series(distances / intervals, index=indices)
bearings = pandas.Series(inverse_azimuths % 360, index=indices)
bearings[pandas.isna(speeds)] = numpy.nan
# fill in nans carrying forward, because it is same valid time
# and forecast but different isotach.
# then fill nans backwards to handle the first time
speeds.ffill(inplace=True)
bearings.ffill(inplace=True)
Comment on lines +1120 to +1121
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@SorooshMani-NOAA SorooshMani-NOAA Jul 22, 2024 •
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@WPringle where would we get nan to carry forward? We already are getting all indices, we shouldn't run into a case of nan that needs to be filled forward for different isotachs. If possible can you share an example where this happens?

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Does geodetic.inv return nans when locations are same?

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@WPringle WPringle Jul 22, 2024 •
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For the same forecast time but different isotachs the result will be nan because the time difference is zero. So we just want to repeat the result from the first line of the same forecast time.

speeds.bfill(inplace=True)
bearings.bfill(inplace=True)
advisory_data["speed"] = speeds
advisory_data["direction"] = bearings

data.loc[data["advisory"] == advisory] = advisory_data

data.loc[pandas.isna(data["speed"]), "speed"] = 0

return data

@property
Expand Down Expand Up @@ -1352,7 +1370,21 @@ def clamp(n, minn, maxn):
or forecast.loc[valid_index, "forecast_hours"].iloc[0] == 0
):
continue
forecast.loc[valid_index, "radius_of_maximum_winds"] = rmw
# make sure rolling rmw is not larger than the maximum radii of the strongest isotach
# this problem usually comes from the rolling average
max_isotach_radii = isotach_radii.loc[valid_index].iloc[-1].max()
if rmw < max_isotach_radii or numpy.isnan(max_isotach_radii):
forecast.loc[valid_index, "radius_of_maximum_winds"] = rmw
# in case it does not come from rolling average just set to be Vr/Vmax ratio of max_isotach_radii
if (
forecast.loc[valid_index, "radius_of_maximum_winds"].iloc[-1]
> max_isotach_radii
):
forecast.loc[valid_index, "radius_of_maximum_winds"] = (
max_isotach_radii
* forecast.loc[valid_index, "isotach_radius"].iloc[-1]
/ forecast.loc[valid_index, "max_sustained_wind_speed"].iloc[-1]
)

# fill OFCL background pressure with the first entry from 0-hr CARQ background pressure (at sea level)
forecast.loc[radp_missing, "background_pressure"] = carq_ref[
Expand Down
2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/florence2018.fort.22
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@@ -1,4 +1,4 @@
AL, 06, 2018083006, , BEST, 0, 128N, 169W, 20, 1008, LO, 0, , 0, 0, 0, 0, 1010, 150, 50, 0, 0, L, 0, , 0, 0, INVEST, 1
AL, 06, 2018083006, , BEST, 0, 128N, 169W, 20, 1008, LO, 0, , 0, 0, 0, 0, 1010, 150, 50, 0, 0, L, 0, , 270, 5, INVEST, 1
AL, 06, 2018083012, , BEST, 0, 128N, 179W, 25, 1007, LO, 0, , 0, 0, 0, 0, 1010, 150, 50, 0, 0, L, 0, , 270, 5, SIX, 2
AL, 06, 2018083018, , BEST, 0, 128N, 190W, 25, 1007, LO, 0, , 0, 0, 0, 0, 1010, 150, 50, 35, 0, L, 0, , 270, 6, SIX, 3
AL, 06, 2018083100, , BEST, 0, 131N, 202W, 30, 1006, LO, 0, , 0, 0, 0, 0, 1010, 150, 40, 40, 0, L, 0, , 284, 6, SIX, 4
Expand Down
2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/harvey2017.fort.22
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@@ -1,4 +1,4 @@
AL, 09, 2017081606, , BEST, 0, 137N, 458W, 25, 1013, LO, 0, , 0, 0, 0, 0, 1014, 150, 80, 0, 0, ,,, 0, 0,, 1
AL, 09, 2017081606, , BEST, 0, 137N, 458W, 25, 1013, LO, 0, , 0, 0, 0, 0, 1014, 150, 80, 0, 0, ,,, 270, 8,, 1
AL, 09, 2017081612, , BEST, 0, 137N, 474W, 25, 1010, LO, 0, , 0, 0, 0, 0, 1013, 150, 80, 0, 0, L, 0, , 270, 8, INVEST, 2
AL, 09, 2017081618, , BEST, 0, 136N, 490W, 25, 1009, LO, 0, , 0, 0, 0, 0, 1013, 150, 80, 0, 0, L, 0, , 267, 8, INVEST, 3
AL, 09, 2017081700, , BEST, 0, 136N, 506W, 25, 1010, LO, 0, , 0, 0, 0, 0, 1012, 120, 80, 0, 0, L, 0, , 270, 8, INVEST, 4
Expand Down
2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/ike2008.fort.22
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@@ -1,4 +1,4 @@
AL, 09, 2008090106, , BEST, 0, 172N, 370W, 30, 1006, TD, 0, , 0, 0, 0, 0, 1011, 250, 90, 0, 0, L, 0, , 0, 0, INVEST, 1
AL, 09, 2008090106, , BEST, 0, 172N, 370W, 30, 1006, TD, 0, , 0, 0, 0, 0, 1011, 250, 90, 0, 0, L, 0, , 274, 7, INVEST, 1
AL, 09, 2008090112, , BEST, 0, 173N, 384W, 35, 1005, TS, 34, NEQ, 120, 75, 0, 60, 1011, 250, 90, 40, 0, L, 0, , 274, 7, NINE, 2
AL, 09, 2008090118, , BEST, 0, 175N, 399W, 45, 1003, TS, 34, NEQ, 130, 110, 0, 75, 1011, 250, 90, 55, 0, L, 0, , 278, 7, IKE, 3
AL, 09, 2008090200, , BEST, 0, 178N, 413W, 45, 1002, TS, 34, NEQ, 140, 120, 0, 90, 1011, 250, 20, 55, 0, L, 0, , 283, 7, IKE, 4
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2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/irene2011.fort.22
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@@ -1,4 +1,4 @@
AL, 09, 2011082100, , BEST, 0, 150N, 590W, 45, 1006, TS, 34, NEQ, 105, 0, 0, 45, 1010, 175, 60, 55, 0, L, 0, , 0, 0, IRENE, 1
AL, 09, 2011082100, , BEST, 0, 150N, 590W, 45, 1006, TS, 34, NEQ, 105, 0, 0, 45, 1010, 175, 60, 55, 0, L, 0, , 303, 9, IRENE, 1
AL, 09, 2011082106, , BEST, 0, 160N, 606W, 45, 1006, TS, 34, NEQ, 130, 0, 0, 80, 1010, 175, 50, 55, 0, L, 0, , 303, 9, IRENE, 2
AL, 09, 2011082112, , BEST, 0, 168N, 622W, 45, 1005, TS, 34, NEQ, 130, 0, 0, 70, 1010, 175, 50, 55, 0, L, 0, , 298, 9, IRENE, 3
AL, 09, 2011082118, , BEST, 0, 175N, 637W, 50, 999, TS, 34, NEQ, 130, 20, 0, 70, 1010, 175, 50, 55, 0, L, 0, , 296, 8, IRENE, 4
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2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/irma2017.fort.22
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@@ -1,4 +1,4 @@
AL, 11, 2017083000, , BEST, 0, 161N, 269W, 30, 1008, TD, 0, , 0, 0, 0, 0, 1012, 180, 60, 0, 0, L, 0, , 0, 0, INVEST, 1
AL, 11, 2017083000, , BEST, 0, 161N, 269W, 30, 1008, TD, 0, , 0, 0, 0, 0, 1012, 180, 60, 0, 0, L, 0, , 274, 7, INVEST, 1
AL, 11, 2017083006, , BEST, 0, 162N, 283W, 35, 1007, TS, 34, NEQ, 30, 0, 0, 0, 1012, 180, 60, 0, 0, L, 0, , 274, 7, INVEST, 2
AL, 11, 2017083012, , BEST, 0, 163N, 297W, 45, 1006, TS, 34, NEQ, 30, 0, 0, 30, 1012, 180, 20, 50, 0, L, 0, , 274, 7, IRMA, 3
AL, 11, 2017083018, , BEST, 0, 163N, 308W, 50, 1004, TS, 34, NEQ, 30, 30, 0, 30, 1011, 200, 15, 0, 0, L, 0, , 270, 5, IRMA, 4
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2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/isabel2003.fort.22
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@@ -1,4 +1,4 @@
AL, 13, 2003090600, , BEST, 0, 138N, 314W, 30, 1009, TD, 34, NEQ, 0, 0, 0, 0, 1012, 150, 40, 0, 0, ,,, 0, 0,, 1
AL, 13, 2003090600, , BEST, 0, 138N, 314W, 30, 1009, TD, 34, NEQ, 0, 0, 0, 0, 1012, 150, 40, 0, 0, ,,, 275, 7,, 1
AL, 13, 2003090606, , BEST, 0, 139N, 327W, 35, 1005, TS, 34, NEQ, 0, 0, 0, 0, 1012, 150, 40, 0, 0, ,,, 275, 7,, 2
AL, 13, 2003090612, , BEST, 0, 136N, 339W, 40, 1003, TS, 34, NEQ, 75, 75, 25, 75, 1012, 150, 25, 0, 0, ,,, 256, 6,, 3
AL, 13, 2003090618, , BEST, 0, 134N, 349W, 45, 1000, TS, 34, NEQ, 75, 75, 25, 75, 1012, 150, 25, 0, 0, ,,, 259, 5,, 4
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2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/maria2017.fort.22
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@@ -1,4 +1,4 @@
AL, 15, 2017091612, , BEST, 0, 122N, 497W, 30, 1006, TD, 0, , 0, 0, 0, 0, 1012, 150, 40, 40, 0, L, 0, , 0, 0, INVEST, 1
AL, 15, 2017091612, , BEST, 0, 122N, 497W, 30, 1006, TD, 0, , 0, 0, 0, 0, 1012, 150, 40, 40, 0, L, 0, , 270, 10, INVEST, 1
AL, 15, 2017091618, , BEST, 0, 122N, 517W, 40, 1004, TS, 34, NEQ, 40, 0, 0, 40, 1012, 150, 40, 50, 0, L, 0, , 270, 10, FIFTEEN, 2
AL, 15, 2017091700, , BEST, 0, 124N, 531W, 45, 1002, TS, 34, NEQ, 40, 30, 0, 40, 1012, 150, 30, 55, 0, L, 0, , 278, 7, MARIA, 3
AL, 15, 2017091706, , BEST, 0, 128N, 544W, 55, 994, TS, 34, NEQ, 50, 40, 0, 50, 1010, 150, 20, 65, 0, L, 0, , 288, 7, MARIA, 4
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2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/michael2018.fort.22
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@@ -1,4 +1,4 @@
AL, 14, 2018100618, , BEST, 0, 178N, 866W, 25, 1006, LO, 0, , 0, 0, 0, 0, 1009, 180, 90, 35, 0, L, 0, , 0, 0, INVEST, 1
AL, 14, 2018100618, , BEST, 0, 178N, 866W, 25, 1006, LO, 0, , 0, 0, 0, 0, 1009, 180, 90, 35, 0, L, 0, , 316, 2, INVEST, 1
AL, 14, 2018100700, , BEST, 0, 181N, 869W, 25, 1004, LO, 0, , 0, 0, 0, 0, 1009, 180, 90, 35, 0, L, 0, , 316, 2, FOURTEEN, 2
AL, 14, 2018100706, , BEST, 0, 184N, 868W, 30, 1004, TD, 0, , 0, 0, 0, 0, 1010, 240, 120, 40, 0, L, 0, , 18, 2, FOURTEEN, 3
AL, 14, 2018100712, , BEST, 0, 188N, 864W, 35, 1003, TS, 34, NEQ, 120, 180, 0, 0, 1009, 270, 120, 40, 0, L, 0, , 44, 3, FOURTEEN, 4
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2 changes: 1 addition & 1 deletion tests/data/reference/test_vortex_track/sandy2012.fort.22
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@@ -1,4 +1,4 @@
AL, 18, 2012102118, , BEST, 0, 143N, 774W, 25, 1006, LO, 0, , 0, 0, 0, 0, 1008, 180, 150, 35, 0, L, 0, , 0, 0, INVEST, 1
AL, 18, 2012102118, , BEST, 0, 143N, 774W, 25, 1006, LO, 0, , 0, 0, 0, 0, 1008, 180, 150, 35, 0, L, 0, , 224, 3, INVEST, 1
AL, 18, 2012102200, , BEST, 0, 139N, 778W, 25, 1005, LO, 0, , 0, 0, 0, 0, 1008, 180, 150, 35, 0, L, 0, , 224, 3, INVEST, 2
AL, 18, 2012102206, , BEST, 0, 135N, 782W, 25, 1003, LO, 0, , 0, 0, 0, 0, 1008, 225, 75, 35, 0, L, 0, , 224, 3, INVEST, 3
AL, 18, 2012102212, , BEST, 0, 131N, 786W, 30, 1002, TD, 0, , 0, 0, 0, 0, 1007, 250, 75, 0, 0, L, 0, , 224, 3, EIGHTEEN, 4
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