National Hurricane Center Cyclone Tutorial¶
This tutorial will explain how to go about retrieving a past hurricane from the National Hurricane Center. In addition, it will demonstrate how to plot the outlooks using Cartopy.
Specifically, this tutorial explains how to use get_nhc_past_cyclone. Throughout this entire tutorial, Hurricane Dorian’s data set will be used from the National Hurricane Center.
Summary¶
This function retrieves a specified hurricane and saves the polygon, line, and points in an object as an attribute. It utilizes PyNimbus geometries, which are Shapely polygons built for PyNimbus purposes.
Required arguments:
name (string) - The name of the cyclone. Capitalization doesn’t matter.
year (integer) - The year of the cyclone specified in YYYY form (i.e. 2018). Records only go back to 2008. The current year may or may not be updated as data gets updated in the NHC server.
advisory_num (integer) - The advisory number for the specified cyclone. Note that intermediate advisories are not considered as of the latest release (v0.0.5)
Optional arguments:
clean_files (boolean) - Defaulted to True. Will clean up the files downloaded.
extract_dir (string) - The directory where to extract the files. By default, the files get downloaded to the current working directory.
Retrieving the data¶
First, we will call the function to retrieve the information:
import pynimbus as pyn
dorian = pyn.get_nhc_past_cyclone("dorian", 2019, 24)
This will return an object containing the polygon, line, and points as attributes:
>>> dorian.polygon
PyNimbus Polygon Geometry object.
Number of Points: 1876
Max Latitude: -85.1301
Max Longitude: 23.46636
Min Latitude: -68.7397
Min Longitude: 31.3947
Center Point: (-79.24563562858884, 27.354134571268858)
>>> dorian.line
PyNimbus Line Geometry object.
Number of Points: 8
Max Latitude: -81.4
Max Longitude: 23.8
Min Latitude: -69.1
Min Longitude: 28.1
Center Point: (-75.23006110816756, 26.12241608285046)
>>> dorian.points
PyNimbus Scatter Points Geometry object.
Number of Points: 8
Max Latitude: -81.4
Max Longitude: 23.8
Min Latitude: -69.1
Min Longitude: 28.1
Plotting Using Cartopy¶
# import cartopy, matplotlib, and PyNimbus
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import pynimbus as pyn
# pynimbus stuff
# download the shapefiles (and delete after)
dorian = pyn.get_nhc_past_cyclone("dorian", 2019, 24)
# get line, polygon, and points ready to plot
poly_lat, poly_lon = dorian.polygon.lat_lon_to_plot()
line_lat, line_lon = dorian.line.lat_lon_to_plot()
point_lat, point_lon = dorian.points.lat_lon_to_plot()
# cartopy stuff
plt.close('all')
ax = plt.axes(projection=ccrs.PlateCarree())
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAND, edgecolor='black')
ax.add_feature(cfeature.LAKES, edgecolor='black')
ax.add_feature(cfeature.RIVERS)
ax.add_feature(cfeature.BORDERS)
ax.add_feature(cfeature.COASTLINE)
plt.plot(poly_lat, poly_lon, color = 'blue', zorder = 0)
plt.plot(line_lat, line_lon, color = 'red', zorder = 1)
plt.scatter(point_lat, point_lon, color = 'green', zorder = 2)
plt.title("Hurricane Dorian Advisory 24 - 2019")
plt.show()
The above code will get you this map:
Save to CSV¶
Simply call the pandas to_csv method:
import pandas as pd
import pynimbus as pyn
link = "https://www.spc.noaa.gov/climo/reports/160524_rpts_filtered.csv"
df = pyn.get_spc_storm_reports_df(link, type_of_df = 'tornado')
df.to_csv("/path/to/save/csv")
It was decided to not integrate this directly into PyNimbus, as the above is easier on the user-end. However, a future version may have integration.