Tropycal quickstart — one Atlantic season¶

Fetch tropical-cyclone best tracks for the 2005 North Atlantic season from HURDAT2 through the earthlens Tropycal backend and plot them coloured by Saffir-Simpson category.

Tropycal is a vector backend: download() returns a pyramids FeatureCollection (a geopandas.GeoDataFrame). The default geometry is one Point per 6-hourly fix.

First-load cost. The first query of a basin downloads and parses its whole best-track file (a few seconds for HURDAT, longer for IBTrACS). Needs pip install earthlens[tropycal].

Setup¶

Pull the imports together up front: EarthLens is the unified entry point, matplotlib renders the track scatter, and we prepare an output directory for the written best-track file.

In [ ]:

from pathlib import Path

import matplotlib.pyplot as plt

from earthlens import EarthLens

OUT_DIR = Path('tropycal_output')
OUT_DIR.mkdir(exist_ok=True)

from pathlib import Path import matplotlib.pyplot as plt from earthlens import EarthLens OUT_DIR = Path('tropycal_output') OUT_DIR.mkdir(exist_ok=True)

Query¶

variables selects the basin (here north_atlantic). The whole season's storms are filtered to the date window and bbox at the fix level. The result is written to tropycal_output/ and returned in memory.

Build the request¶

Construct the EarthLens request on its own line so the source, basin, date window, and Gulf-of-Mexico bbox are easy to read and tweak.

In [ ]:

tracks = EarthLens(
    variables=['north_atlantic'],
    data_source='tropycal',
    start='2005-08-01',
    end='2005-09-15',
    aoi=[-98.0, 18.0, -80.0, 31.0],
    source='hurdat',
    path=str(OUT_DIR),
)

tracks = EarthLens( variables=['north_atlantic'], data_source='tropycal', start='2005-08-01', end='2005-09-15', aoi=[-98.0, 18.0, -80.0, 31.0], source='hurdat', path=str(OUT_DIR), )

Download the fixes¶

download() is kept on its own line so the live network call is a separate, re-runnable step. It is wrapped in a guard so the notebook stays safe under nbval-lax when run offline or without the [tropycal] extra.

In [ ]:

fixes = None
try:
    fixes = tracks.download(progress_bar=False)
    print(len(fixes), 'fixes')
except Exception as exc:
    print(f'skipped live query: {type(exc).__name__}: {exc}')

fixes = None try: fixes = tracks.download(progress_bar=False) print(len(fixes), 'fixes') except Exception as exc: print(f'skipped live query: {type(exc).__name__}: {exc}')

Inspect the fixes¶

Peek at the first few rows — one per 6-hourly fix — to confirm the storm metadata, intensity (vmax_kt), category, and geometry came through, and check the CRS.

In [ ]:

if fixes is not None and len(fixes):
    cols = ['storm_id', 'name', 'time', 'vmax_kt', 'category', 'geometry']
    display(fixes[cols].head())
    print('CRS:', fixes.crs)

if fixes is not None and len(fixes): cols = ['storm_id', 'name', 'time', 'vmax_kt', 'category', 'geometry'] display(fixes[cols].head()) print('CRS:', fixes.crs)

Plot the track fixes coloured by category¶

Scatter every fix on a lon/lat axis, colouring each point by its Saffir-Simpson category so the storms' intensification across the Gulf stands out.

In [ ]:

if fixes is not None and len(fixes):
    fig, ax = plt.subplots(figsize=(8, 6))
    fixes.plot(
        ax=ax,
        column='category',
        cmap='YlOrRd',
        legend=True,
        markersize=25,
        edgecolor='k',
        linewidth=0.2,
    )
    ax.set_title('North Atlantic best-track fixes, Aug-Sep 2005 (Gulf of Mexico)')
    ax.set_xlabel('Longitude')
    ax.set_ylabel('Latitude')
    plt.tight_layout()
    plt.show()

if fixes is not None and len(fixes): fig, ax = plt.subplots(figsize=(8, 6)) fixes.plot( ax=ax, column='category', cmap='YlOrRd', legend=True, markersize=25, edgecolor='k', linewidth=0.2, ) ax.set_title('North Atlantic best-track fixes, Aug-Sep 2005 (Gulf of Mexico)') ax.set_xlabel('Longitude') ax.set_ylabel('Latitude') plt.tight_layout() plt.show()