You can run this notebook in , in , in or in .

[1]:

!pip install --quiet climetlab matplotlib

NOAA’s hurricane database¶

[2]:

import climetlab as cml

[3]:

atlantic = cml.load_dataset("hurricane-database", bassin="atlantic")

Get the Pandas frame

[4]:

df = atlantic.to_pandas()

Huricanes making landfall at category 5¶

[5]:

df[(df.category == 5) & (df.type == "L")]

[5]:

	bassin	number	year	name	time	type	status	lat	lon	knots	category	pressure
17942	AL	3	1935	unnamed	1935-09-03 02:00:00	L	HU	24.8	-80.8	160.0	5	892.0
24300	AL	10	1955	janet	1955-09-27 17:00:00	L	HU	17.4	-83.9	140.0	5	NaN
24303	AL	10	1955	janet	1955-09-28 05:00:00	L	HU	18.4	-87.8	150.0	5	914.0
28888	AL	9	1969	camille	1969-08-18 04:00:00	L	HU	30.3	-89.4	150.0	5	900.0
36448	AL	8	1988	gilbert	1988-09-14 15:00:00	L	HU	20.7	-87.0	140.0	5	900.0
37967	AL	4	1992	andrew	1992-08-23 21:00:00	L	HU	25.4	-76.6	140.0	5	923.0
37971	AL	4	1992	andrew	1992-08-24 08:40:00	L	HU	25.5	-80.2	145.0	5	926.0
37972	AL	4	1992	andrew	1992-08-24 09:05:00	L	HU	25.5	-80.3	145.0	5	922.0
45134	AL	4	2007	dean	2007-08-21 08:30:00	L	HU	18.7	-87.7	150.0	5	905.0
45182	AL	6	2007	felix	2007-09-04 12:00:00	L	HU	14.3	-83.2	140.0	5	934.0
49925	AL	11	2017	irma	2017-09-06 05:45:00	L	HU	17.7	-61.8	155.0	5	914.0
49927	AL	11	2017	irma	2017-09-06 11:15:00	L	HU	18.1	-63.1	155.0	5	914.0
49929	AL	11	2017	irma	2017-09-06 16:30:00	L	HU	18.5	-64.4	155.0	5	915.0
49941	AL	11	2017	irma	2017-09-09 03:00:00	L	HU	22.3	-77.9	145.0	5	924.0
50139	AL	15	2017	maria	2017-09-19 01:15:00	L	HU	15.4	-61.3	145.0	5	922.0
50835	AL	14	2018	michael	2018-10-10 17:30:00	L	HU	30.0	-85.5	140.0	5	919.0

Get the track for Katrina¶

[6]:

katrina = df[(df.name == "katrina") & (df.year == 2005)]

[7]:

katrina

[7]:

	bassin	number	year	name	time	type	status	lat	lon	knots	category	pressure
44065	AL	12	2005	katrina	2005-08-23 18:00:00		TD	23.1	-75.1	30.0	1	1008.0
44066	AL	12	2005	katrina	2005-08-24 00:00:00		TD	23.4	-75.7	30.0	1	1007.0
44067	AL	12	2005	katrina	2005-08-24 06:00:00		TD	23.8	-76.2	30.0	1	1007.0
44068	AL	12	2005	katrina	2005-08-24 12:00:00		TS	24.5	-76.5	35.0	1	1006.0
44069	AL	12	2005	katrina	2005-08-24 18:00:00		TS	25.4	-76.9	40.0	1	1003.0
44070	AL	12	2005	katrina	2005-08-25 00:00:00		TS	26.0	-77.7	45.0	1	1000.0
44071	AL	12	2005	katrina	2005-08-25 06:00:00		TS	26.1	-78.4	50.0	1	997.0
44072	AL	12	2005	katrina	2005-08-25 12:00:00		TS	26.2	-79.0	55.0	1	994.0
44073	AL	12	2005	katrina	2005-08-25 18:00:00		TS	26.2	-79.6	60.0	1	988.0
44074	AL	12	2005	katrina	2005-08-25 22:30:00	L	HU	26.0	-80.1	70.0	1	984.0
44075	AL	12	2005	katrina	2005-08-26 00:00:00		HU	25.9	-80.3	70.0	1	983.0
44076	AL	12	2005	katrina	2005-08-26 06:00:00		HU	25.4	-81.3	65.0	1	987.0
44077	AL	12	2005	katrina	2005-08-26 12:00:00		HU	25.1	-82.0	75.0	1	979.0
44078	AL	12	2005	katrina	2005-08-26 18:00:00		HU	24.9	-82.6	85.0	2	968.0
44079	AL	12	2005	katrina	2005-08-27 00:00:00		HU	24.6	-83.3	90.0	2	959.0
44080	AL	12	2005	katrina	2005-08-27 06:00:00		HU	24.4	-84.0	95.0	2	950.0
44081	AL	12	2005	katrina	2005-08-27 12:00:00		HU	24.4	-84.7	100.0	3	942.0
44082	AL	12	2005	katrina	2005-08-27 18:00:00		HU	24.5	-85.3	100.0	3	948.0
44083	AL	12	2005	katrina	2005-08-28 00:00:00		HU	24.8	-85.9	100.0	3	941.0
44084	AL	12	2005	katrina	2005-08-28 06:00:00		HU	25.2	-86.7	125.0	4	930.0
44085	AL	12	2005	katrina	2005-08-28 12:00:00		HU	25.7	-87.7	145.0	5	909.0
44086	AL	12	2005	katrina	2005-08-28 18:00:00		HU	26.3	-88.6	150.0	5	902.0
44087	AL	12	2005	katrina	2005-08-29 00:00:00		HU	27.2	-89.2	140.0	5	905.0
44088	AL	12	2005	katrina	2005-08-29 06:00:00		HU	28.2	-89.6	125.0	4	913.0
44089	AL	12	2005	katrina	2005-08-29 11:10:00	L	HU	29.3	-89.6	110.0	3	920.0
44090	AL	12	2005	katrina	2005-08-29 12:00:00		HU	29.5	-89.6	110.0	3	923.0
44091	AL	12	2005	katrina	2005-08-29 14:45:00	L	HU	30.2	-89.6	105.0	3	928.0
44092	AL	12	2005	katrina	2005-08-29 18:00:00		HU	31.1	-89.6	80.0	1	948.0
44093	AL	12	2005	katrina	2005-08-30 00:00:00		TS	32.6	-89.1	50.0	1	961.0
44094	AL	12	2005	katrina	2005-08-30 06:00:00		TS	34.1	-88.6	40.0	1	978.0
44095	AL	12	2005	katrina	2005-08-30 12:00:00		TD	35.6	-88.0	30.0	1	985.0
44096	AL	12	2005	katrina	2005-08-30 18:00:00		TD	37.0	-87.0	30.0	1	990.0
44097	AL	12	2005	katrina	2005-08-31 00:00:00		EX	38.6	-85.3	30.0	1	994.0
44098	AL	12	2005	katrina	2005-08-31 06:00:00		EX	40.1	-82.9	25.0	1	996.0

Plot the track¶

[8]:

cml.plot_map(katrina, margins=0.25)

../_images/examples_12-hurricane-database_12_0.png

Retrieve matching fields from ERA5¶

[9]:

hurricane = katrina[katrina.status == "HU"]
hurricane

[9]:

	bassin	number	year	name	time	type	status	lat	lon	knots	category	pressure
44074	AL	12	2005	katrina	2005-08-25 22:30:00	L	HU	26.0	-80.1	70.0	1	984.0
44075	AL	12	2005	katrina	2005-08-26 00:00:00		HU	25.9	-80.3	70.0	1	983.0
44076	AL	12	2005	katrina	2005-08-26 06:00:00		HU	25.4	-81.3	65.0	1	987.0
44077	AL	12	2005	katrina	2005-08-26 12:00:00		HU	25.1	-82.0	75.0	1	979.0
44078	AL	12	2005	katrina	2005-08-26 18:00:00		HU	24.9	-82.6	85.0	2	968.0
44079	AL	12	2005	katrina	2005-08-27 00:00:00		HU	24.6	-83.3	90.0	2	959.0
44080	AL	12	2005	katrina	2005-08-27 06:00:00		HU	24.4	-84.0	95.0	2	950.0
44081	AL	12	2005	katrina	2005-08-27 12:00:00		HU	24.4	-84.7	100.0	3	942.0
44082	AL	12	2005	katrina	2005-08-27 18:00:00		HU	24.5	-85.3	100.0	3	948.0
44083	AL	12	2005	katrina	2005-08-28 00:00:00		HU	24.8	-85.9	100.0	3	941.0
44084	AL	12	2005	katrina	2005-08-28 06:00:00		HU	25.2	-86.7	125.0	4	930.0
44085	AL	12	2005	katrina	2005-08-28 12:00:00		HU	25.7	-87.7	145.0	5	909.0
44086	AL	12	2005	katrina	2005-08-28 18:00:00		HU	26.3	-88.6	150.0	5	902.0
44087	AL	12	2005	katrina	2005-08-29 00:00:00		HU	27.2	-89.2	140.0	5	905.0
44088	AL	12	2005	katrina	2005-08-29 06:00:00		HU	28.2	-89.6	125.0	4	913.0
44089	AL	12	2005	katrina	2005-08-29 11:10:00	L	HU	29.3	-89.6	110.0	3	920.0
44090	AL	12	2005	katrina	2005-08-29 12:00:00		HU	29.5	-89.6	110.0	3	923.0
44091	AL	12	2005	katrina	2005-08-29 14:45:00	L	HU	30.2	-89.6	105.0	3	928.0
44092	AL	12	2005	katrina	2005-08-29 18:00:00		HU	31.1	-89.6	80.0	1	948.0

We can use the Pandas frame to specify the date and area of the request to the CDS.

[10]:

source = cml.load_source(
    "cds",
    "reanalysis-era5-pressure-levels",
    variable="z",
    level=500,
    product_type="reanalysis",
    time=[0, 6, 12, 18],
    date=hurricane,
    area=hurricane,
)

for s in source:
    cml.plot_map(s, width=400, grid=True)

../_images/examples_12-hurricane-database_16_0.png

../_images/examples_12-hurricane-database_16_1.png

../_images/examples_12-hurricane-database_16_2.png

../_images/examples_12-hurricane-database_16_3.png

../_images/examples_12-hurricane-database_16_4.png

../_images/examples_12-hurricane-database_16_5.png

../_images/examples_12-hurricane-database_16_6.png

../_images/examples_12-hurricane-database_16_7.png

../_images/examples_12-hurricane-database_16_8.png

../_images/examples_12-hurricane-database_16_9.png

../_images/examples_12-hurricane-database_16_10.png

../_images/examples_12-hurricane-database_16_11.png

../_images/examples_12-hurricane-database_16_12.png

../_images/examples_12-hurricane-database_16_13.png

../_images/examples_12-hurricane-database_16_14.png

../_images/examples_12-hurricane-database_16_15.png

../_images/examples_12-hurricane-database_16_16.png

../_images/examples_12-hurricane-database_16_17.png

../_images/examples_12-hurricane-database_16_18.png

../_images/examples_12-hurricane-database_16_19.png

Pacific bassin¶

[11]:

pacific = cml.load_dataset("hurricane-database", bassin="pacific")

Get the Pandas frame

[12]:

df = pacific.to_pandas()

Plot a few years

[13]:

for year in range(2010, 2020):
    cml.plot_map(
        df[df.year == year], column="number", title=year, style="rainbow-markers"
    )

../_images/examples_12-hurricane-database_22_0.png

../_images/examples_12-hurricane-database_22_1.png

../_images/examples_12-hurricane-database_22_2.png

../_images/examples_12-hurricane-database_22_3.png

../_images/examples_12-hurricane-database_22_4.png

../_images/examples_12-hurricane-database_22_5.png

../_images/examples_12-hurricane-database_22_6.png

../_images/examples_12-hurricane-database_22_7.png

../_images/examples_12-hurricane-database_22_8.png

../_images/examples_12-hurricane-database_22_9.png

Plot each track independently¶

[14]:

import matplotlib.pyplot as plt

[15]:

cmap = plt.get_cmap("prism")

[16]:

p = cml.new_plot(margins="5%")
i = 0
for year in range(2010, 2020):
    cp = df[(df.year == year) & (df.number == 1) & (df.bassin == "CP")]
    if len(cp):
        p.plot_map(cp, style={"+symbol_colour": cmap((i * 20) % cmap.N)})
        i += 1
p.show()

../_images/examples_12-hurricane-database_26_0.png