2. Studying a set of tracks#

[1]:

import huracanpy
import numpy as np
import seaborn as sns

2.2. Adding info to the tracks#

HuracanPy has several function to add useful information to the tracks (for full list, see huracanpy.info). Here for example we add basin and Saffir-Simpson hurrican scale category information.]

2.2.1. Add basin#

[3]:

tracks = tracks.hrcn.add_basin()
tracks.basin

[3]:

<xarray.DataArray 'basin' (record: 2274)> Size: 36kB
array(['AUS', 'AUS', 'AUS', ..., 'SP', 'SP', 'AUS'],
      shape=(2274,), dtype='<U4')
Dimensions without coordinates: record

2.2.1.1. Show distribution of TC points among basins#

(calling seaborn function, works better with categorical labels)

[4]:

sns.countplot(tracks.basin)

[4]:

<Axes: xlabel='count'>

../../_images/user_guide_demos_set_of_tracks_7_1.png

2.2.2. Add Saffir-Simpson and pressure-based categories#

Note: in ERA5 data, wind is stored in wind10 in m/s

[5]:

tracks = tracks.hrcn.add_saffir_simpson_category(wind_name="wind10", wind_units="m s-1")
tracks = tracks.hrcn.add_pressure_category(slp_name="slp", slp_units="Pa")
tracks[["saffir_simpson_category", "pressure_category"]]

[5]:

<xarray.Dataset> Size: 36kB
Dimensions:                  (record: 2274)
Dimensions without coordinates: record
Data variables:
    saffir_simpson_category  (record) int64 18kB -1 -1 -1 -1 -1 ... 0 0 0 0 -1
    pressure_category        (record) int64 18kB -1 0 0 0 0 0 0 ... 1 1 1 1 1 -1

2.2.2.1. Show distribution of TC points among categories#

(using xarray’s built-in function)

[6]:

bins = np.arange(-1.5, 5.5 + 1)
tracks.saffir_simpson_category.plot.hist(bins=bins, alpha=0.5)
tracks.pressure_category.plot.hist(bins=bins, alpha=0.5)

[6]:

(array([ 190., 1342.,  534.,  157.,   41.,   10.,    0.]),
 array([-1.5, -0.5,  0.5,  1.5,  2.5,  3.5,  4.5,  5.5]),
 <BarContainer object of 7 artists>)

../../_images/user_guide_demos_set_of_tracks_11_1.png

2.5. Compositing lifecycle#

Add time from apex variable to be able to superimpose all the tracks centered on apex. Here we use minimum pressure as the apex point

2.5.1. Add time from minimum pressure#

[20]:

tracks = tracks.hrcn.add_time_from_apex(intensity_var_name="slp", stat="min")
tracks.time_from_apex

[20]:

<xarray.DataArray 'time_from_apex' (record: 2274)> Size: 18kB
array([-237600, -216000, -194400, ...,  259200,  280800,       0],
      shape=(2274,), dtype='timedelta64[s]')
Dimensions without coordinates: record

[21]:

tracks.time_from_apex / np.timedelta64(1, "h")

[21]:

<xarray.DataArray 'time_from_apex' (record: 2274)> Size: 18kB
array([-66., -60., -54., ...,  72.,  78.,   0.], shape=(2274,))
Dimensions without coordinates: record

2.5.2. Plot composite SLP lifecycle#

[22]:

# Convert time_from_apex to hours
tracks["time_from_apex"] = tracks.time_from_apex / np.timedelta64(1, "h")

# Use xarray's where to mask points too far away from apex (48 hours away)
tracks_close_to_apex = tracks.where(np.abs(tracks.time_from_apex) <= 48, drop=True)

# Seaborn lineplot allows for drawing composites with uncertainty range
# x-axis is time from apex
# y-axis is slp, converted to hPa
sns.lineplot(x=tracks_close_to_apex.time_from_apex, y=tracks_close_to_apex.slp / 100)

[22]:

<Axes: xlabel='time_from_apex', ylabel='slp'>

../../_images/user_guide_demos_set_of_tracks_44_1.png