Hazard: Tropical cyclones

Tropical cyclones tracks are gathered in the class TCTracks and then provided to the hazard TropCyclone which computes the wind gusts at each centroid. TropCyclone inherits from Hazard and has an associated hazard type TC.

TCTracks class

TCTracks reads and handles historical tropical cyclone tracks of the IBTrACS repository. It also generates synthetic tracks from the historical ones using Wiener processes.

The tracks are stored in the attribute data, which is a list of xarray’s Dataset (see xarray.Dataset). Each Dataset contains the following variables:

- time (coords)
- lat (coords)
- lon (coords)
- time_step
- radius_max_wind
- max_sustained_wind
- central_pressure
- environmental_pressure
- max_sustained_wind_unit (attrs)
- central_pressure_unit (attrs)
- sid (attrs)
- name (attrs)
- orig_event_flag (attrs)
- data_provider (attrs)
- basin (attrs)
- id_no (attrs)
- category (attrs)

The method read_ibtracs_netcdf() generates the Datasets for tracks selected by IBTrACS id, or by basin and year range. To achieve this, it downloads the first time the IBTrACS data v4 in netcdf format and stores it in climada_python/data/system. The tracks can be accessed later either using the attribute data or using get_track(), which allows to select tracks by its name or id. Use the method append() to extend the data list.

If you get an error downloading the IBTrACS data, try to manually access ftp://eclipse.ncdc.noaa.gov/pub/ibtracs//v04r00/provisional/netcdf/, connect as a Guest and copy the file IBTrACS.ALL.v04r00.nc to climada_python/data/system.

To visualize the tracks use plot().

%matplotlib inline
from climada.hazard import TCTracks

tr_irma = TCTracks()
tr_irma.read_ibtracs_netcdf(provider='usa', storm_id='2017242N16333') # IRMA 2017
fig, ax = tr_irma.plot()
ax.set_title('IRMA') # set title

# other ibtracs selection options
from climada.hazard import TCTracks
sel_ibtracs = TCTracks()
sel_ibtracs.read_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), basin='EP') # years 1993 and 1994 in basin EP
print('Number of tracks:', sel_ibtracs.size)
fig, ax = sel_ibtracs.plot()
ax.get_legend()._loc = 2 # correct legend location
ax.set_title('1993-1994, EP') # set title

track1 = TCTracks()
track1.read_ibtracs_netcdf(provider='usa', storm_id='2007314N10093') # SIDR 2007
track2 = TCTracks()
track2.read_ibtracs_netcdf(provider='usa', storm_id='2016138N10081') # ROANU 2016
track1.append(track2.data) # put both tracks together
fig, ax = track1.plot()
ax.get_legend()._loc = 2 # correct legend location
ax.set_title('SIDR and ROANU'); # set title

2019-05-16 22:07:25,528 - climada - DEBUG - Loading default config file: /Users/aznarsig/Documents/Python/climada_python/climada/conf/defaults.conf
2019-05-16 22:07:30,009 - climada.hazard.tc_tracks - INFO - Reading 2017242N16333: IRMA

/Users/aznarsig/anaconda3/envs/climada_up/lib/python3.7/site-packages/matplotlib/tight_layout.py:176: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations.
  warnings.warn('Tight layout not applied. The left and right margins '

2019-05-16 22:07:34,240 - climada.hazard.tc_tracks - INFO - Reading 1993162N10245: ADRIAN
2019-05-16 22:07:34,464 - climada.hazard.tc_tracks - INFO - Reading 1993169N13265: BEATRIZ
2019-05-16 22:07:34,664 - climada.hazard.tc_tracks - INFO - Reading 1993178N14265: NOT_NAMED
2019-05-16 22:07:34,809 - climada.hazard.tc_tracks - WARNING - Skipping 1993178N14265. No usable data.
2019-05-16 22:07:34,811 - climada.hazard.tc_tracks - INFO - Reading 1993186N13262: CALVIN
2019-05-16 22:07:35,022 - climada.hazard.tc_tracks - INFO - Reading 1993196N11241: DORA
2019-05-16 22:07:35,234 - climada.hazard.tc_tracks - INFO - Reading 1993197N11247: EUGENE
2019-05-16 22:07:35,449 - climada.hazard.tc_tracks - INFO - Reading 1993221N12216: KEONI
2019-05-16 22:07:35,596 - climada.hazard.tc_tracks - WARNING - Skipping 1993221N12216. No usable data.
2019-05-16 22:07:35,599 - climada.hazard.tc_tracks - INFO - Reading 1993221N13256: FERNANDA
2019-05-16 22:07:35,824 - climada.hazard.tc_tracks - INFO - Reading 1993223N07185: NOT_NAMED
2019-05-16 22:07:35,968 - climada.hazard.tc_tracks - WARNING - Skipping 1993223N07185. No usable data.
2019-05-16 22:07:35,971 - climada.hazard.tc_tracks - INFO - Reading 1993227N15258: GREG
2019-05-16 22:07:36,192 - climada.hazard.tc_tracks - INFO - Reading 1993229N13265: HILARY
2019-05-16 22:07:36,407 - climada.hazard.tc_tracks - INFO - Reading 1993233N13260: IRWIN
2019-05-16 22:07:36,629 - climada.hazard.tc_tracks - INFO - Reading 1993241N14264: JOVA
2019-05-16 22:07:36,905 - climada.hazard.tc_tracks - INFO - Reading 1993249N12253: KENNETH
2019-05-16 22:07:37,117 - climada.hazard.tc_tracks - INFO - Reading 1993252N11265: LIDIA
2019-05-16 22:07:37,338 - climada.hazard.tc_tracks - INFO - Reading 1993273N11245: MAX
2019-05-16 22:07:37,549 - climada.hazard.tc_tracks - INFO - Reading 1993276N13249: NORMA
2019-05-16 22:07:37,762 - climada.hazard.tc_tracks - INFO - Reading 1993285N12192: HATTIE
2019-05-16 22:07:37,907 - climada.hazard.tc_tracks - WARNING - Skipping 1993285N12192. No usable data.
2019-05-16 22:07:37,909 - climada.hazard.tc_tracks - INFO - Reading 1993285N16239: NOT_NAMED
2019-05-16 22:07:38,049 - climada.hazard.tc_tracks - WARNING - Skipping 1993285N16239. No usable data.
2019-05-16 22:07:38,052 - climada.hazard.tc_tracks - INFO - Reading 1994169N13240: ALETTA
2019-05-16 22:07:38,260 - climada.hazard.tc_tracks - INFO - Reading 1994178N16248: BUD
2019-05-16 22:07:38,474 - climada.hazard.tc_tracks - INFO - Reading 1994180N12255: CARLOTTA
2019-05-16 22:07:38,697 - climada.hazard.tc_tracks - INFO - Reading 1994190N13239: DANIEL
2019-05-16 22:07:38,899 - climada.hazard.tc_tracks - INFO - Reading 1994197N09229: EMILIA
2019-05-16 22:07:39,119 - climada.hazard.tc_tracks - INFO - Reading 1994200N13235: FABIO
2019-05-16 22:07:39,337 - climada.hazard.tc_tracks - INFO - Reading 1994202N11238: GILMA
2019-05-16 22:07:39,550 - climada.hazard.tc_tracks - INFO - Reading 1994212N11240: LI
2019-05-16 22:07:39,697 - climada.hazard.tc_tracks - WARNING - Skipping 1994212N11240. No usable data.
2019-05-16 22:07:39,699 - climada.hazard.tc_tracks - INFO - Reading 1994219N19252: HECTOR
2019-05-16 22:07:39,913 - climada.hazard.tc_tracks - INFO - Reading 1994221N15215: NOT_NAMED
2019-05-16 22:07:40,052 - climada.hazard.tc_tracks - WARNING - Skipping 1994221N15215. No usable data.
2019-05-16 22:07:40,055 - climada.hazard.tc_tracks - INFO - Reading 1994222N11267: JOHN
2019-05-16 22:07:40,291 - climada.hazard.tc_tracks - INFO - Reading 1994223N15253: ILEANA
2019-05-16 22:07:40,502 - climada.hazard.tc_tracks - INFO - Reading 1994224N16240: NOT_NAMED
2019-05-16 22:07:40,637 - climada.hazard.tc_tracks - WARNING - Skipping 1994224N16240. No usable data.
2019-05-16 22:07:40,639 - climada.hazard.tc_tracks - INFO - Reading 1994241N14236: KRISTY
2019-05-16 22:07:40,848 - climada.hazard.tc_tracks - INFO - Reading 1994247N18251: LANE
2019-05-16 22:07:41,149 - climada.hazard.tc_tracks - INFO - Reading 1994249N11200: MELE
2019-05-16 22:07:41,285 - climada.hazard.tc_tracks - WARNING - Skipping 1994249N11200. No usable data.
2019-05-16 22:07:41,287 - climada.hazard.tc_tracks - INFO - Reading 1994249N21220: LANE
2019-05-16 22:07:41,499 - climada.hazard.tc_tracks - INFO - Reading 1994259N13244: MIRIAM
2019-05-16 22:07:41,707 - climada.hazard.tc_tracks - INFO - Reading 1994262N16249: NORMAN
2019-05-16 22:07:41,911 - climada.hazard.tc_tracks - INFO - Reading 1994265N13251: OLIVIA
2019-05-16 22:07:42,134 - climada.hazard.tc_tracks - INFO - Reading 1994268N15232: PAUL
2019-05-16 22:07:42,344 - climada.hazard.tc_tracks - INFO - Reading 1994282N17247: ROSA
2019-05-16 22:07:42,562 - climada.hazard.tc_tracks - INFO - Reading 1994294N13203: NONA
2019-05-16 22:07:42,697 - climada.hazard.tc_tracks - WARNING - Skipping 1994294N13203. No usable data.
2019-05-16 22:07:42,699 - climada.hazard.tc_tracks - INFO - Reading 1994295N25188: YURI
2019-05-16 22:07:42,835 - climada.hazard.tc_tracks - WARNING - Skipping 1994295N25188. No usable data.
Number of tracks: 32
2019-05-16 22:07:46,391 - climada.hazard.tc_tracks - INFO - Reading 2007314N10093: SIDR
2019-05-16 22:07:50,507 - climada.hazard.tc_tracks - INFO - Reading 2016138N10081: ROANU

tr_irma.get_track('2017242N16333')

<xarray.Dataset>
Dimensions:                 (time: 123)
Coordinates:
  * time                    (time) datetime64[ns] 2017-08-30 ... 2017-09-13T12:00:00
    lat                     (time) float32 16.1 16.147842 ... 36.495224 36.8
    lon                     (time) float32 -26.9 -27.592503 ... -89.794334 -90.1
Data variables:
    radius_max_wind         (time) float64 0.0 0.0 0.0 20.0 ... 60.0 60.0 60.0
    max_sustained_wind      (time) float64 30.0 32.0 35.0 ... 15.0 15.0 15.0
    central_pressure        (time) float64 1.008e+03 1.007e+03 ... 1.005e+03
    environmental_pressure  (time) float64 1.012e+03 1.012e+03 ... 1.008e+03
    time_step               (time) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0
Attributes:
    max_sustained_wind_unit:  kn
    central_pressure_unit:    mb
    name:                     IRMA
    sid:                      2017242N16333
    orig_event_flag:          True
    data_provider:            usa
    basin:                    NA
    id_no:                    2017242016333.0
    category:                 5

Once tracks are present in TCTracks, one can generate synthetic tracks for each present track using calc_random_walk() and interpolate all the tracks to the same timestep using equal_timestep().

tr_irma.calc_random_walk(ens_size=5) # select number of synthetic tracks to generate per present tracks
tr_irma.plot()

2019-05-16 22:07:54,136 - climada.hazard.tc_tracks - INFO - Computing 5 synthetic tracks.
2019-05-16 22:07:56,393 - climada.hazard.tc_tracks - DEBUG - No historical track of category Tropical Depression with landfall. Decay parameters from category Hurrican Cat. 3 taken.
2019-05-16 22:07:56,393 - climada.hazard.tc_tracks - DEBUG - No historical track of category Tropical Storm with landfall. Decay parameters from category Hurrican Cat. 3 taken.
2019-05-16 22:07:56,394 - climada.hazard.tc_tracks - DEBUG - No historical track of category Hurrican Cat. 1 with landfall. Decay parameters from category Hurrican Cat. 3 taken.
2019-05-16 22:07:56,395 - climada.hazard.tc_tracks - DEBUG - No historical track of category Hurrican Cat. 2 with landfall. Decay parameters from category Hurrican Cat. 3 taken.
2019-05-16 22:07:56,397 - climada.hazard.tc_tracks - DEBUG - No historical track of category Hurrican Cat. 4 with landfall. Decay parameters from category Hurrican Cat. 5 taken.

(<Figure size 648x936 with 1 Axes>,
 <cartopy.mpl.geoaxes.GeoAxesSubplot at 0x1a1f485160>)

tr_irma.data[-1] # last synthetic track. notice the value of orig_event_flag and name

<xarray.Dataset>
Dimensions:                 (time: 123)
Coordinates:
  * time                    (time) datetime64[ns] 2017-08-30 ... 2017-09-13T12:00:00
    lat                     (time) float64 15.39 15.49 15.6 ... 34.59 34.79 35.0
    lon                     (time) float64 -27.64 -28.42 ... -97.51 -97.84
Data variables:
    radius_max_wind         (time) float64 0.0 0.0 0.0 20.0 ... 60.0 60.0 60.0
    max_sustained_wind      (time) float64 30.0 32.0 35.0 ... 12.01 11.31 10.58
    central_pressure        (time) float64 1.008e+03 1.007e+03 ... 999.2 999.8
    environmental_pressure  (time) float64 1.012e+03 1.012e+03 ... 1.008e+03
    time_step               (time) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0
    on_land                 (time) bool False False False ... True True True
    dist_since_lf           (time) float64 nan nan nan nan ... 804.3 838.2 875.9
Attributes:
    max_sustained_wind_unit:  kn
    central_pressure_unit:    mb
    name:                     IRMA_gen5
    sid:                      2017242N16333_gen5
    orig_event_flag:          False
    data_provider:            usa
    basin:                    NA
    id_no:                    2017242016333.05
    category:                 5

tr_irma.equal_timestep(time_step_h=1) # select number of synthetic tracks to generate per present tracks
tr_irma.plot()

2019-05-16 22:07:58,986 - climada.hazard.tc_tracks - INFO - Interpolating 6 tracks to 1h time steps.

(<Figure size 648x936 with 1 Axes>,
 <cartopy.mpl.geoaxes.GeoAxesSubplot at 0x1a20671c18>)

EXERCISE

Using the first synthetic track generated,

1. Which is the time frequency of the data?

2. Compute the maximum sustained wind for each day.

# Put your code here

# SOLUTION:
import numpy as np
# select the track
tc_syn = tr_irma.get_track('2017242N16333_gen1')

# 1. Which is the time frequency of the data?
# The values of a DataArray are numpy.arrays.
# The nummpy.ediff1d computes the different between elements in an array
diff_time_ns = np.ediff1d(tc_syn.time)
diff_time_h = diff_time_ns.astype(int)/1000/1000/1000/60/60
print('Mean time frequency in hours:', diff_time_h.mean())
print('Std time frequency in hours:', diff_time_h.std())
print()

# 2. Compute the maximum sustained wind for each day.
print('Daily max sustained wind:', tc_syn.max_sustained_wind.groupby('time.day').max())

Mean time frequency in hours: 1.0
Std time frequency in hours: 0.0

Daily max sustained wind: <xarray.DataArray 'max_sustained_wind' (day: 15)>
array([100.      , 100.      , 100.      , 123.333333, 155.      , 155.      ,
       150.      , 144.      , 145.      , 130.291495, 113.852824,  92.890059,
        76.949128,  54.      ,  99.      ])
Coordinates:
  * day      (day) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 30 31

TropCyclone class

The TropCyclone class is a derived class of Hazard. As such, it contains all the attributes and methods of a Hazard. Additionally, it contains the method set_from_tracks() to model tropical cyclones from tracks contained in a TCTracks instance.

When setting tropical cyclones from tracks, the centroids where to map the wind gusts (the hazard intensity) can be provided. If no centroids are provided, the global centroids GLB_NatID_grid_0360as_adv_2.mat are used. The method used to calculate the wind gusts can also be configured. The default is the method of Holland 2008.

from climada.hazard import Centroids, TropCyclone

# construct centroids
min_lat, max_lat, min_lon, max_lon = 16.99375, 21.95625, -72.48125, -61.66875
resol = 50
cent = Centroids()
cent.set_raster_from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.12)
cent.check()
cent.plot()

# construct tropical cyclones
tc_irma = TropCyclone()
tc_irma.set_from_tracks(tr_irma, centroids=cent)
#tc_irma.set_from_tracks(tr_irma) # try without given centroids
tc_irma.check()
tc_irma.plot_intensity('2017242N16333')      # IRMA
tc_irma.plot_intensity('2017242N16333_gen2') # IRMA's synthetic track 2

2019-05-16 22:08:03,175 - climada.hazard.centroids.centr - DEBUG - Setting dist_coast 3822 points.
2019-05-16 22:08:03,508 - climada.hazard.trop_cyclone - INFO - Mapping 6 tracks to 3822 centroids.
2019-05-16 22:08:08,918 - climada.hazard.trop_cyclone - DEBUG - Append events.
2019-05-16 22:08:08,933 - climada.hazard.trop_cyclone - DEBUG - Compute frequency.

(<Figure size 648x936 with 2 Axes>,
 array([[<cartopy.mpl.geoaxes.GeoAxesSubplot object at 0x1a21e89eb8>]],
       dtype=object))

Multiprocessing is implemented in the tropical cyclones. When dealing with a big amount of data, you might consider using it as follows:

# execute these lines in a console, outside Jupyter Notebook

from climada.hazard import TCTracks, Centroids, TropCyclone
from pathos.pools import ProcessPool as Pool

pool = Pool() # start a pathos pool

tc_track = TCTracks(pool)  # provide the pool in the constructor
tc_track.read_ibtracs_netcdf(provider='usa', year_range=(1992, 1994), basin='EP')
tc_track.calc_random_walk()
tc_track.equal_timestep()

lon_min, lat_min, lon_max, lat_max = -160, 10, -100, 36
centr = Centroids()
centr.set_raster_from_pnt_bounds((lon_min, lat_min, lon_max, lat_max), 0.1)

tc_haz = TropCyclone(pool) # provide the pool in the constructor
tc_haz.set_from_tracks(tc_track, centr)
tc_haz.check()

pool.close()
pool.join()