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Hazard: Tropical cyclones

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.

What do tropical cyclones look like in CLIMADA?#

TCTracks reads and handles historical tropical cyclone tracks of the IBTrACS repository or synthetic tropical cyclone tracks simulated using fully statistical or coupled statistical-dynamical modeling approaches. 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:

Coordinates

time

latitude

longitude

Descriptive variables

time_step

radius_max_wind

max_sustained_wind

central_pressure

environmental_pressure

Attributes

max_sustained_wind_unit

central_pressure_unit

sid

name

orig_event_flag

data_provider

basin

id_no

category


How is this tutorial structured?#

Part 1: Load TC tracks

    a) Load TC tracks from historical records

    b) Generate probabilistic events

    c) ECMWF Forecast Tracks

    d) Load TC tracks from other sources

Part 2: TropCyclone() class

    a) Default hazard generation for tropical cyclones

    b) Implementing climate change

    c) Multiprocessing - improving performance for big computations

    d) Making videos

Part 1: Load TC tracks#

Records of historical TCs are very limited and therefore the database to study this natural hazard remains sparse. Only a small fraction of the TCs make landfall every year and reliable documentation of past TC landfalling events has just started in the 1950s (1980s - satellite era). The generation of synthetic storm tracks is an important tool to overcome this spatial and temporal limitation. Synthetic dataset are much larger and thus allow to estimate the risk of much rarer events. Here we show the most prominent tools in CLIMADA to load TC tracks from historical records a), generate a probabilistic dataset thereof b), and work with model simulations c).

a) Load TC tracks from historical records#

The best-track historical data from the International Best Track Archive for Climate Stewardship (IBTrACS) can easily be loaded into CLIMADA to study the historical records of TC events. The constructor from_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/data/. 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 https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/, click on the file IBTrACS.ALL.v04r00.nc and copy it to ~/climada/data/.

To visualize the tracks use plot().

%matplotlib inline
from climada.hazard import TCTracks

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

# other ibtracs selection options
from climada.hazard import TCTracks
# years 1993 and 1994 in basin EP.
# correct_pres ignores tracks with not enough data. For statistics (frequency of events), these should be considered as well
sel_ibtracs = TCTracks.from_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), basin='EP', correct_pres=False)
print('Number of tracks:', sel_ibtracs.size)
ax = sel_ibtracs.plot();
ax.get_legend()._loc = 2 # correct legend location
ax.set_title('1993-1994, EP') # set title

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

2021-06-04 17:07:33,515 - climada.hazard.tc_tracks - INFO - Progress: 100%
2021-06-04 17:07:35,833 - climada.hazard.tc_tracks - WARNING - 19 storm events are discarded because no valid wind/pressure values have been found: 1993178N14265, 1993221N12216, 1993223N07185, 1993246N16129, 1993263N11168, ...
2021-06-04 17:07:35,940 - climada.hazard.tc_tracks - INFO - Progress: 11%
2021-06-04 17:07:36,028 - climada.hazard.tc_tracks - INFO - Progress: 23%
2021-06-04 17:07:36,119 - climada.hazard.tc_tracks - INFO - Progress: 35%
2021-06-04 17:07:36,218 - climada.hazard.tc_tracks - INFO - Progress: 47%
2021-06-04 17:07:36,312 - climada.hazard.tc_tracks - INFO - Progress: 58%
2021-06-04 17:07:36,399 - climada.hazard.tc_tracks - INFO - Progress: 70%
2021-06-04 17:07:36,493 - climada.hazard.tc_tracks - INFO - Progress: 82%
2021-06-04 17:07:36,585 - climada.hazard.tc_tracks - INFO - Progress: 94%
2021-06-04 17:07:36,612 - climada.hazard.tc_tracks - INFO - Progress: 100%
Number of tracks: 33
2021-06-04 17:07:38,825 - climada.hazard.tc_tracks - INFO - Progress: 100%
2021-06-04 17:07:39,974 - climada.hazard.tc_tracks - INFO - Progress: 100%
../_images/8a364e4fd4c6155db3f17c75b8b57ec32bca402db97babf0b5b7516b32273259.png ../_images/ca6abdee4fb5bad000aac3cbb6616b089c6ffd77f2dbbb6c5d8505b96c9abb1a.png ../_images/534cc429d89bcbd5e624e77d07625035aa8e7a0472e5307a139437160cfdc2fa.png 
tr_irma.get_track('2017242N16333')

<xarray.Dataset>
Dimensions:                 (time: 123)
Coordinates:
  * time                    (time) datetime64[ns] 2017-08-30 ... 2017-09-13T1...
    lat                     (time) float32 16.1 16.15 16.2 ... 36.2 36.5 36.8
    lon                     (time) float32 -26.9 -27.59 -28.3 ... -89.79 -90.1
Data variables:
    time_step               (time) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0
    radius_max_wind         (time) float32 60.0 60.0 60.0 ... 60.0 60.0 60.0
    radius_oci              (time) float32 180.0 180.0 180.0 ... 350.0 350.0
    max_sustained_wind      (time) float32 30.0 32.0 35.0 ... 15.0 15.0 15.0
    central_pressure        (time) float32 1.008e+03 1.007e+03 ... 1.005e+03
    environmental_pressure  (time) float64 1.012e+03 1.012e+03 ... 1.008e+03
    basin                   (time) <U2 'NA' 'NA' 'NA' 'NA' ... 'NA' 'NA' 'NA'
Attributes:
    max_sustained_wind_unit:  kn
    central_pressure_unit:    mb
    name:                     IRMA
    sid:                      2017242N16333
    orig_event_flag:          True
    data_provider:            ibtracs_usa
    id_no:                    2017242016333.0
    category:                 5

b) Generate probabilistic events#

Once tracks are present in TCTracks, one can generate synthetic tracks for each present track based on directed random walk. Note that the tracks should be interpolated to use the same timestep before generation of probabilistic events.

calc_perturbed_trajectories() generates an ensemble of “nb_synth_tracks” numbers of synthetic tracks is computed for every track. The methodology perturbs the tracks locations, and if decay is True it additionally includes decay of wind speed and central pressure drop after landfall. No other track parameter is perturbed.

# here we use tr_irma retrieved from IBTrACS with the function above
# select number of synthetic tracks (nb_synth_tracks) to generate per present tracks.
tr_irma.equal_timestep()
tr_irma.calc_perturbed_trajectories(nb_synth_tracks=5)
tr_irma.plot();
# see more configutration options (e.g. amplitude of max random starting point shift in decimal degree; max_shift_ini)

<GeoAxesSubplot:>
../_images/d0690cd131476f2259598bb11194a16deb64c9b3812208f3c07d048d1706e49c.png 
tr_irma.data[-1] # last synthetic track. notice the value of orig_event_flag and name

<xarray.Dataset>
Dimensions:                 (time: 349)
Coordinates:
  * time                    (time) datetime64[ns] 2017-08-30 ... 2017-09-13T1...
    lon                     (time) float64 -27.64 -27.8 -27.96 ... -97.81 -97.93
    lat                     (time) float64 15.39 15.41 15.42 ... 27.41 27.49
Data variables:
    time_step               (time) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    radius_max_wind         (time) float64 60.0 60.0 60.0 ... 60.0 60.0 60.0
    radius_oci              (time) float64 180.0 180.0 180.0 ... 350.0 350.0
    max_sustained_wind      (time) float64 30.0 30.67 31.33 ... 15.0 14.99 14.96
    central_pressure        (time) float64 1.008e+03 1.008e+03 ... 1.005e+03
    environmental_pressure  (time) float64 1.012e+03 1.012e+03 ... 1.008e+03
    basin                   (time) <U2 'NA' 'NA' 'NA' 'NA' ... 'NA' 'NA' 'NA'
    on_land                 (time) bool False False False ... False True True
    dist_since_lf           (time) float64 nan nan nan nan ... nan 7.605 22.71
Attributes:
    max_sustained_wind_unit:  kn
    central_pressure_unit:    mb
    name:                     IRMA_gen5
    sid:                      2017242N16333_gen5
    orig_event_flag:          False
    data_provider:            ibtracs_usa
    id_no:                    2017242016333.05
    category:                 5

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.        , 123.33333333,
       155.        , 155.        , 150.        , 138.        ,
        51.85384486,  58.03963987,  29.03963987,   3.57342356,
         3.35512013,  54.        ,  99.        ])
Coordinates:
  * day      (day) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 30 31

c) ECMWF Forecast Tracks#

ECMWF publishes tropical cyclone forecast tracks free of charge as part of the WMO essentials. These tracks are detected automatically in the ENS and HRES models. The non-supervised nature of the model may lead to artefacts.

The tc_fcast trackset below inherits from TCTracks, but contains some additional metadata that follows ECMWF’s definitions. Try plotting these tracks and compare them to the official cones of uncertainty! The example track at tc_fcast.data[0] shows the data structure.

# This functionality is part of climada_petals, uncomment to execute
# from climada_petals.hazard import TCForecast
#
# tc_fcast = TCForecast()
# tc_fcast.fetch_ecmwf()
#
# print(tc_fcast.data[0])

d) Load TC tracks from other sources#

In addition to the historical records of TCs (IBTrACS), the probabilistic extension of these tracks, and the ECMWF Forecast tracks, CLIMADA also features functions to read in synthetic TC tracks from other sources. These include synthetic storm tracks from Kerry Emanuel’s coupled statistical-dynamical model (Emanuel et al., 2006 as used in Geiger et al., 2016), synthetic storm tracks from a second coupled statistical-dynamical model (CHAZ) (as described in Lee et al., 2018), and synthetic storm tracks from a fully statistical model (STORM) Bloemendaal et al., 2020). However, these functions are partly under development and/or targeted at advanced users of CLIMADA in the context of very specific use cases. They are thus not covered in this tutorial.

Part 2: 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 comes with the constructor method 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.

From the track properties the 1 min sustained peak gusts are computed in each centroid as the sum of a circular wind field (following Holland, 2008) and the translational wind speed that arises from the storm movement. We incorporate the decline of the translational component from the cyclone centre by multiplying it by an attenuation factor. See CLIMADA v1 and references therein for more information.

a) Default hazard generation for tropical cyclones#

from climada.hazard import Centroids, TropCyclone

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

# construct tropical cyclones
tc_irma = TropCyclone.from_tracks(tr_irma, centroids=cent)
# tc_irma = TropCyclone.from_tracks(tr_irma)  # try without given centroids. It might take too much space of your memory
# and then the kernel will be killed: So, don't use this function without given centroids!
tc_irma.check()
tc_irma.plot_intensity('2017242N16333');  # IRMA
tc_irma.plot_intensity('2017242N16333_gen2');  # IRMA's synthetic track 2

<GeoAxesSubplot:title={'center':'Event ID 3: 2017242N16333_gen2'}>
../_images/7e3521e126502b93f475dd3f5ecad6d35b1854f7751b0c960b9010fdda71d075.png ../_images/fe60ec2517f9d9e22b6f6079faba2a0f7547984de4c3ecb4f8657fd88eff2a15.png ../_images/476ef2608a6c9a092e47a4cfc9ad1474e4925e96d43058df275288df7100a229.png

b) Implementing climate change#

apply_climate_scenario_knu implements the changes on intensity and frequency due to climate change described in Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios of Knutson et al 2015. Other RCP scenarios are approximated from the RCP 4.5 values by interpolating them according to their relative radiative forcing.

# an Irma event-like in 2055 under RCP 4.5:
tc_irma = TropCyclone.from_tracks(tr_irma, centroids=cent)
tc_irma_cc = tc_irma.apply_climate_scenario_knu(ref_year=2055, rcp_scenario=45)
tc_irma_cc.plot_intensity('2017242N16333');

<GeoAxesSubplot:title={'center':'Event ID 1: 2017242N16333'}>
../_images/782fa25a850b25a931049273173ac3979544009fe4d66ceceb81dc83c60ca4ce.png

Note: this method to implement climate change is simplified and does only take into account changes in TC frequency and intensity. However, how hurricane damage changes with climate remains challenging to assess. Records of hurricane damage exhibit widely fluctuating values because they depend on rare, landfalling events which are substantially more volatile than the underlying basin-wide TC characteristics. For more accurate future projections of how a warming climate might shape TC characteristics, there is a two-step process needed. First, the understanding of how climate change affects critical environmental factors (like SST, humidity, etc.) that shape TCs is required. Second, the means of simulating how these changes impact TC characteristics (such as intensity, frequency, etc.) are necessary. Statistical-dynamical models (Emanuel et al., 2006 and Lee et al., 2018) are physics-based and allow for such climate change studies. However, this goes beyond the scope of this tutorial.

c) Multiprocessing - improving performance for big computations#

Multiprocessing is part of the tropical cyclone module. Simply provide a process pool as method argument.
Below is an example of how large amounts of data could be processed.

WARNING:
Running multiprocessing code from Jupyter Notebooks can be cumbersome. It’s suggested to copy the code and paste it into an interactive python console.

from climada.hazard import TCTracks, Centroids, TropCyclone

from pathos.pools import ProcessPool as Pool
pool = Pool() # start a pathos pool

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

tc_track = TCTracks.from_ibtracs_netcdf(provider='usa', year_range=(1992, 1994), basin='EP')
tc_track.equal_timestep(pool=pool)
tc_track.calc_perturbed_trajectories(pool=pool) # OPTIONAL: if you want to generate a probabilistic set of TC tracks.

tc_haz = TropCyclone.from_tracks(tc_track, centroids=centr, pool=pool)
tc_haz.check()

pool.close()
pool.join()

d) Making videos#

Videos of a tropical cyclone hitting specific centroids can be created with the method video_intensity().

WARNING:
Creating an animated gif file may consume a lot of memory, up to the point where the os starts swapping or even an ‘out-of-memory’ exception is thrown.

# Note: execution of this cell will fail unless there is enough memory available (> 10G)

from climada.hazard import Centroids, TropCyclone, TCTracks

track_name = '2017242N16333' #'2016273N13300' #'1992230N11325'

tr_irma = TCTracks.from_ibtracs_netcdf(provider='usa', storm_id='2017242N16333')

lon_min, lat_min, lon_max, lat_max = -83.5, 24.4, -79.8, 29.6
centr_video = Centroids.from_pnt_bounds((lon_min, lat_min, lon_max, lat_max), 0.04)
centr_video.check()

tc_video = TropCyclone()

tc_list, tr_coord = tc_video.video_intensity(track_name, tr_irma, centr_video, file_name='results/irma_tc_fl.gif')

2022-04-08 10:01:29,114 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.
2022-04-08 10:01:31,696 - climada.util.coordinates - INFO - dist_to_coast: UTM 32617 (1/1)
2022-04-08 10:01:38,120 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 11374 coastal centroids.
2022-04-08 10:01:38,135 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,144 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12032 coastal centroids.
2022-04-08 10:01:38,158 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,170 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
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2022-04-08 10:01:38,194 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
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2022-04-08 10:01:38,267 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,285 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,296 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,313 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,323 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,338 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,348 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,364 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,374 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,391 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,400 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,416 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,427 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,441 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,452 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:01:38,470 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:01:38,479 - climada.hazard.trop_cyclone - INFO - Generating video irma_tc_fl.gif

15it [00:53,  3.60s/it]

tc_list contains a list with TropCyclone instances plotted at each time step
tr_coord contains a list with the track path coordinates plotted at each time step

Saving disk space with mp4#

Animated gif images occupy a lot of space. Using mp4 as output format makes the video sequences much smaller! However this requires the package ffmpeg to be installed, which is not part of the ordinary climada environment. It can be installed by executing the following command in a console:

conda install ffmpeg

Creating the same videa as above in mp4 format can be done in this way then:

# Note: execution of this cell will fail unless there is enough memory available (> 12G) and ffmpeg is installed

import shutil
from matplotlib import animation
from matplotlib.pyplot import rcParams

rcParams['animation.ffmpeg_path'] = shutil.which('ffmpeg')
writer = animation.FFMpegWriter(bitrate=500)
tc_list, tr_coord = tc_video.video_intensity(track_name, tr_irma, centr_video, file_name='results/irma_tc_fl.mp4', writer=writer)

2022-04-08 10:03:27,161 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 11374 coastal centroids.
2022-04-08 10:03:27,182 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:03:27,192 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12032 coastal centroids.
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2022-04-08 10:03:27,218 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
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2022-04-08 10:03:27,275 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
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2022-04-08 10:03:27,363 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
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2022-04-08 10:03:27,507 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:03:27,523 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:03:27,533 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.
2022-04-08 10:03:27,548 - climada.hazard.trop_cyclone - INFO - Progress: 100%
2022-04-08 10:03:27,559 - climada.hazard.trop_cyclone - INFO - Generating video irma_tc_fl.mp4

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REFERENCES:#

  • Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0381-2

  • Emanuel, K., S. Ravela, E. Vivant, and C. Risi, 2006: A Statistical Deterministic Approach to Hurricane Risk Assessment. Bull. Amer. Meteor. Soc., 87, 299–314, https://doi.org/10.1175/BAMS-87-3-299.

  • Geiger, T., Frieler, K., & Levermann, A. (2016). High-income does not protect against hurricane losses. Environmental Research Letters, 11(8). https://doi.org/10.1088/1748-9326/11/8/084012

  • Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi, G. A., … Chavas, D. (2015). Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios. Journal of Climate, 28(18), 7203–7224. https://doi.org/10.1175/JCLI-D-15-0129.1

  • Lee, C. Y., Tippett, M. K., Sobel, A. H., & Camargo, S. J. (2018). An environmentally forced tropical cyclone hazard model. Journal of Advances in Modeling Earth Systems, 10(1), 223–241. https://doi.org/10.1002/2017MS001186

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