Individual Season Analysis

This sample script illustrates how to retrieve, visualize and analyze seasons from the HURDAT2 dataset.

import tropycal.tracks as tracks

HURTDAT2 Dataset

Let’s start by creating an instance of a TrackDataset object. By default, this reads in the HURDAT2 dataset from the National Hurricane Center (NHC) website. For this example we’ll be using the HURDAT2 dataset over the North Atlantic basin.

HURDAT data is not available for the most recent hurricane seasons. To include the latest data up through today, the “include_btk” flag would need to be set to True, which reads in preliminary best track data from the NHC website.

basin = tracks.TrackDataset(basin='north_atlantic',source='hurdat',include_btk=False)

--> Starting to read in HURDAT2 data
--> Completed reading in HURDAT2 data (4.12 seconds)

Individual season analysis

Individual seasons can be retrieved from the dataset by calling the get_season() function, which returns an instance of a Season object.

Let’s retrieve an instance of the 2017 Atlantic Hurricane Season:

season = basin.get_season(2017)

We can quickly visualize what the Season object contains by printing it:

print(season)

<tropycal.tracks.Season>
Season Summary:
    Total Storms:      18
    Named Storms:      17
    Hurricanes:        10
    Major Hurricanes:  6
    Season ACE:        224.8

More Information:
    year:          2017
    basin:         north_atlantic
    source_basin:  north_atlantic
    source:        hurdat
    source_info:   NHC Hurricane Database

The Season object can be converted to a Pandas DataFrame, which lists a summary of storms during the season:

season.to_dataframe()

	id	name	vmax	mslp	category	ace	start_time	end_time	start_lat	start_lon
0	AL012017	ARLENE	45	990	0	0.8	2017-04-19 00:00:00	2017-04-21 06:00:00	31.1	-42.6
1	AL022017	BRET	45	1007	0	0.5	2017-06-19 18:00:00	2017-06-20 09:00:00	9.2	-58.9
2	AL032017	CINDY	50	991	0	1.8	2017-06-20 18:00:00	2017-06-23 12:00:00	25.6	-90.6
3	AL042017	FOUR	25	1009	-1	0.0	2017-07-05 18:00:00	2017-07-07 12:00:00	12.0	-37.1
4	AL052017	DON	45	1005	0	0.8	2017-07-17 06:00:00	2017-07-18 12:00:00	10.4	-49.1
5	AL062017	EMILY	50	1001	0	0.6	2017-07-30 18:00:00	2017-08-01 18:00:00	28.4	-85.4
6	AL072017	FRANKLIN	75	981	1	3.7	2017-08-07 00:00:00	2017-08-10 12:00:00	16.1	-82.7
7	AL082017	GERT	95	962	2	7.8	2017-08-12 18:00:00	2017-08-17 12:00:00	24.0	-69.4
8	AL092017	HARVEY	115	937	4	11.4	2017-08-17 06:00:00	2017-09-01 00:00:00	13.4	-52.0
9	AL112017	IRMA	155	914	5	64.9	2017-08-30 00:00:00	2017-09-12 00:00:00	16.1	-26.9
10	AL122017	JOSE	135	938	4	43.3	2017-09-05 06:00:00	2017-09-22 12:00:00	11.6	-37.9
11	AL132017	KATIA	90	972	2	6.1	2017-09-05 12:00:00	2017-09-09 12:00:00	22.2	-97.2
12	AL142017	LEE	100	962	3	17.9	2017-09-14 18:00:00	2017-09-30 00:00:00	10.4	-23.1
13	AL152017	MARIA	150	908	5	44.8	2017-09-16 12:00:00	2017-09-30 12:00:00	12.2	-49.7
14	AL162017	NATE	80	981	1	4.1	2017-10-04 12:00:00	2017-10-08 18:00:00	12.0	-81.8
15	AL172017	OPHELIA	100	959	3	14.5	2017-10-09 06:00:00	2017-10-15 18:00:00	30.9	-40.0
16	AL182017	PHILIPPE	35	1000	0	0.1	2017-10-28 12:00:00	2017-10-29 00:00:00	20.1	-83.5
17	AL192017	RINA	50	991	0	1.7	2017-11-05 18:00:00	2017-11-09 00:00:00	29.2	-51.7

A more detailed summary of the season can be retrieved using the summary() method:

season.summary()

{'id': ['AL012017', 'AL022017', 'AL032017', 'AL042017', 'AL052017', 'AL062017', 'AL072017', 'AL082017', 'AL092017', 'AL112017', 'AL122017', 'AL132017', 'AL142017', 'AL152017', 'AL162017', 'AL172017', 'AL182017', 'AL192017'], 'operational_id': ['AL012017', 'AL022017', 'AL032017', 'AL042017', 'AL052017', 'AL062017', 'AL072017', 'AL082017', 'AL092017', 'AL112017', 'AL122017', 'AL132017', 'AL142017', 'AL152017', 'AL162017', 'AL172017', 'AL182017', 'AL192017'], 'name': ['ARLENE', 'BRET', 'CINDY', 'FOUR', 'DON', 'EMILY', 'FRANKLIN', 'GERT', 'HARVEY', 'IRMA', 'JOSE', 'KATIA', 'LEE', 'MARIA', 'NATE', 'OPHELIA', 'PHILIPPE', 'RINA'], 'max_wspd': [45, 45, 50, 25, 45, 50, 75, 95, 115, 155, 135, 90, 100, 150, 80, 100, 35, 50], 'min_mslp': [990, 1007, 991, 1009, 1005, 1001, 981, 962, 937, 914, 938, 972, 962, 908, 981, 959, 1000, 991], 'category': [0, 0, 0, -1, 0, 0, 1, 2, 4, 5, 4, 2, 3, 5, 1, 3, 0, 0], 'ace': [0.8, 0.5, 1.8, 0.0, 0.8, 0.6, 3.7, 7.8, 11.4, 64.9, 43.3, 6.1, 17.9, 44.8, 4.1, 14.5, 0.1, 1.7], 'season_start': datetime.datetime(2017, 4, 19, 0, 0), 'season_end': datetime.datetime(2017, 11, 9, 0, 0), 'season_storms': 18, 'season_named': 17, 'season_hurricane': 10, 'season_major': 6, 'season_ace': 224.8, 'season_subtrop_pure': 0, 'season_subtrop_partial': 0}

Plot Season

Plotting a Season object can be quickly done using the plot() method.

Note that you can pass various arguments to the plot function, such as customizing the map and track aspects. The “Customizing Storm Plots” example script has more examples on how to customize such plots. Read through the documentation for more customization options.

season.plot()

<GeoAxes: title={'left': '2017 Atlantic Hurricane Season', 'right': '17 named • 10 hurricanes • 6 major\n224.8 Cumulative ACE'}>

Multiple Season Analysis

Seasons can also be combined for multi-season analyses by simply adding multiple season objects together.

season1 = basin.get_season(2017)
season2 = basin.get_season(2018)
season3 = basin.get_season(2021)

combined = season1 + season2 + season3

print(combined)

<tropycal.tracks.Season>
Season Summary:
    Total Storms:      [18, 16, 21]
    Named Storms:      [17, 15, 21]
    Hurricanes:        [10, 8, 7]
    Major Hurricanes:  [6, 2, 4]
    Season ACE:        [224.8, 132.5, 145.3]

More Information:
    year:          [2017, 2018, 2021]
    basin:         north_atlantic
    source_basin:  north_atlantic
    source:        hurdat
    source_info:   NHC Hurricane Database

The combined seasons can then be plotted on the same map:

combined.plot()

<GeoAxes: title={'left': '[2017, 2018, 2021] Atlantic Hurricane Season', 'right': '53 named • 25 hurricanes • 12 major\n502.6 Cumulative ACE'}>

The summary method also generates summaries for all seasons in this object:

combined.summary()

{'id': [['AL012017', 'AL022017', 'AL032017', 'AL042017', 'AL052017', 'AL062017', 'AL072017', 'AL082017', 'AL092017', 'AL112017', 'AL122017', 'AL132017', 'AL142017', 'AL152017', 'AL162017', 'AL172017', 'AL182017', 'AL192017'], ['AL012018', 'AL022018', 'AL032018', 'AL042018', 'AL052018', 'AL062018', 'AL072018', 'AL082018', 'AL092018', 'AL102018', 'AL112018', 'AL122018', 'AL132018', 'AL142018', 'AL152018', 'AL162018'], ['AL012021', 'AL022021', 'AL032021', 'AL042021', 'AL052021', 'AL062021', 'AL072021', 'AL082021', 'AL092021', 'AL102021', 'AL112021', 'AL122021', 'AL132021', 'AL142021', 'AL152021', 'AL162021', 'AL172021', 'AL182021', 'AL192021', 'AL202021', 'AL212021']], 'operational_id': [['AL012017', 'AL022017', 'AL032017', 'AL042017', 'AL052017', 'AL062017', 'AL072017', 'AL082017', 'AL092017', 'AL112017', 'AL122017', 'AL132017', 'AL142017', 'AL152017', 'AL162017', 'AL172017', 'AL182017', 'AL192017'], ['AL012018', 'AL022018', 'AL032018', 'AL042018', 'AL052018', 'AL062018', 'AL072018', 'AL082018', 'AL092018', 'AL102018', 'AL112018', 'AL122018', 'AL132018', 'AL142018', 'AL152018', 'AL162018'], ['AL012021', 'AL022021', 'AL032021', 'AL042021', 'AL052021', 'AL062021', 'AL072021', 'AL082021', 'AL092021', 'AL102021', 'AL112021', 'AL122021', 'AL132021', 'AL142021', 'AL152021', 'AL162021', 'AL172021', 'AL182021', 'AL192021', 'AL202021', 'AL212021']], 'name': [['ARLENE', 'BRET', 'CINDY', 'FOUR', 'DON', 'EMILY', 'FRANKLIN', 'GERT', 'HARVEY', 'IRMA', 'JOSE', 'KATIA', 'LEE', 'MARIA', 'NATE', 'OPHELIA', 'PHILIPPE', 'RINA'], ['ALBERTO', 'BERYL', 'CHRIS', 'DEBBY', 'ERNESTO', 'FLORENCE', 'GORDON', 'HELENE', 'ISAAC', 'JOYCE', 'ELEVEN', 'KIRK', 'LESLIE', 'MICHAEL', 'NADINE', 'OSCAR'], ['ANA', 'BILL', 'CLAUDETTE', 'DANNY', 'ELSA', 'FRED', 'GRACE', 'HENRI', 'IDA', 'KATE', 'JULIAN', 'LARRY', 'MINDY', 'NICHOLAS', 'ODETTE', 'PETER', 'ROSE', 'SAM', 'TERESA', 'VICTOR', 'WANDA']], 'max_wspd': [[45, 45, 50, 25, 45, 50, 75, 95, 115, 155, 135, 90, 100, 150, 80, 100, 35, 50], [55, 70, 90, 45, 40, 130, 60, 95, 65, 45, 30, 55, 80, 140, 55, 95], [40, 55, 40, 40, 75, 55, 105, 65, 130, 40, 50, 110, 50, 65, 40, 45, 50, 135, 40, 55, 50]], 'min_mslp': [[990, 1007, 991, 1009, 1005, 1001, 981, 962, 937, 914, 938, 972, 962, 908, 981, 959, 1000, 991], [990, 991, 969, 998, 1003, 937, 996, 967, 995, 995, 1007, 998, 968, 919, 995, 966], [1004, 992, 1003, 1009, 991, 991, 967, 986, 929, 1004, 993, 953, 1000, 988, 1005, 1005, 1004, 927, 1008, 997, 983]], 'category': [[0, 0, 0, -1, 0, 0, 1, 2, 4, 5, 4, 2, 3, 5, 1, 3, 0, 0], [0, 1, 2, 0, 0, 4, 0, 2, 1, 0, -1, 0, 1, 5, 0, 2], [0, 0, 0, 0, 1, 0, 3, 1, 4, 0, 0, 3, 0, 1, 0, 0, 0, 4, 0, 0, 0]], 'ace': [[0.8, 0.5, 1.8, 0.0, 0.8, 0.6, 3.7, 7.8, 11.4, 64.9, 43.3, 6.1, 17.9, 44.8, 4.1, 14.5, 0.1, 1.7], [1.8, 4.8, 7.4, 1.5, 1.4, 39.7, 2.3, 15.7, 6.8, 2.4, 0.0, 3.2, 21.3, 12.5, 2.7, 9.0], [0.8, 1.0, 1.1, 0.4, 9.9, 2.4, 8.6, 7.8, 10.6, 0.6, 1.1, 31.9, 0.4, 2.4, 0.4, 1.8, 1.4, 54.0, 0.6, 2.4, 5.7]], 'seasons': [2017, 2018, 2021], 'season_start': [datetime.datetime(2017, 4, 19, 0, 0), datetime.datetime(2018, 5, 25, 12, 0), datetime.datetime(2021, 5, 22, 6, 0)], 'season_end': [datetime.datetime(2017, 11, 9, 0, 0), datetime.datetime(2018, 10, 31, 12, 0), datetime.datetime(2021, 11, 7, 6, 0)], 'season_storms': [18, 16, 21], 'season_named': [17, 15, 21], 'season_hurricane': [10, 8, 7], 'season_major': [6, 2, 4], 'season_ace': [224.8, 132.5, 145.3], 'season_subtrop_pure': [0, 0, 1], 'season_subtrop_partial': [0, 7, 3]}