NDBC Latest Data Request

This example shows how to use siphon’s simplewebswervice support query the most recent observations from all of the NDBC buoys at once.

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

from siphon.simplewebservice.ndbc import NDBC

Get a pandas data frame of all of the observations

df = NDBC.latest_observations()
df.head()

	station	latitude	longitude	wind_direction	wind_speed	wind_gust	wave_height	dominant_wave_period	average_wave_period	dominant_wave_direction	pressure	3hr_pressure_tendency	air_temperature	water_temperature	dewpoint	visibility	water_level_above_mean	time
0	14049	-12.00	65.000	127.0	13.1	15.9	NaN	NaN	NaN	NaN	1018.1	NaN	18.9	26.0	NaN	NaN	NaN	2026-08-31 19:00:00+00:00
1	15001	-10.00	-10.000	118.0	6.9	8.3	NaN	NaN	NaN	NaN	1015.8	NaN	23.2	24.1	NaN	NaN	NaN	2026-08-31 19:00:00+00:00
2	15006	-6.00	-10.000	126.0	7.4	8.3	NaN	NaN	NaN	NaN	1014.3	NaN	24.0	24.5	NaN	NaN	NaN	2026-08-31 19:00:00+00:00
3	15009	0.00	-3.051	178.0	6.5	NaN	NaN	NaN	NaN	NaN	1011.7	NaN	25.0	26.1	NaN	NaN	NaN	2026-08-31 18:00:00+00:00
4	22101	37.24	126.020	140.0	3.0	NaN	0.5	0.0	NaN	NaN	NaN	NaN	24.1	25.9	NaN	NaN	NaN	2026-08-31 19:00:00+00:00

In this case I’m going to drop buoys that do not have water temperature measurements.

df.dropna(subset=['water_temperature'], inplace=True)

Let’s make a simple plot of the buoy positions and color by water temperature

proj = ccrs.LambertConformal(central_latitude=45., central_longitude=-100.,
                             standard_parallels=[30, 60])

fig = plt.figure(figsize=(17., 11.))
ax = plt.axes(projection=proj)
ax.coastlines('50m', edgecolor='black')
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.set_extent([-85, -75, 25, 30], ccrs.PlateCarree())

ax.scatter(df['longitude'], df['latitude'], c=df['water_temperature'],
           transform=ccrs.PlateCarree())

plt.show()