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	13002	21.0	-23.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	21.7	NaN	NaN	NaN	NaN	2026-02-24 19:00:00+00:00
1	13008	15.0	-38.000	51.0	8.8	10.5	NaN	NaN	NaN	NaN	1014.4	NaN	23.5	24.0	NaN	NaN	NaN	2026-02-24 19:00:00+00:00
2	14049	-12.0	65.000	67.0	2.3	3.1	NaN	NaN	NaN	NaN	1010.9	NaN	24.3	29.7	NaN	NaN	NaN	2026-02-24 19:00:00+00:00
3	15001	-10.0	-10.000	119.0	4.6	5.6	NaN	NaN	NaN	NaN	1010.1	NaN	26.3	NaN	NaN	NaN	NaN	2026-02-24 19:00:00+00:00
4	15009	0.0	-3.051	177.0	0.4	NaN	NaN	NaN	NaN	NaN	1007.2	NaN	30.0	29.5	NaN	NaN	NaN	2026-02-24 18:00:00+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()