NDBC Buoy Meteorological Data Request

The NDBC keeps a 45-day recent rolling file for each buoy. This examples shows how to access the basic meteorological data from a buoy and make a simple plot.

import matplotlib.pyplot as plt

from siphon.simplewebservice.ndbc import NDBC

Get a pandas data frame of all of the observations, meteorological data is the default observation set to query.

df = NDBC.realtime_observations('46001')
df.head()

	wind_direction	wind_speed	wind_gust	wave_height	dominant_wave_period	average_wave_period	dominant_wave_direction	pressure	air_temperature	water_temperature	dewpoint	visibility	3hr_pressure_tendency	water_level_above_mean	time
0	360.0	4.0	6.0	3.3	10.0	7.7	300.0	993.6	0.7	3.2	-1.9	NaN	NaN	NaN	2026-03-18 17:20:00+00:00
1	360.0	4.0	6.0	3.3	NaN	7.7	300.0	993.4	0.6	3.2	-1.9	NaN	NaN	NaN	2026-03-18 17:10:00+00:00
2	350.0	3.0	5.0	NaN	NaN	NaN	NaN	993.3	0.7	3.2	-1.5	NaN	1.2	NaN	2026-03-18 17:00:00+00:00
3	350.0	2.0	4.0	3.4	10.0	7.8	302.0	993.1	0.5	3.2	-1.8	NaN	NaN	NaN	2026-03-18 16:50:00+00:00
4	340.0	2.0	3.0	NaN	NaN	NaN	NaN	993.0	0.5	3.2	-1.7	NaN	NaN	NaN	2026-03-18 16:40:00+00:00

Let’s make a simple time series plot to checkout what the data look like.

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))
ax2b = ax2.twinx()

# Pressure
ax1.plot(df['time'], df['pressure'], color='black')
ax1.set_ylabel('Pressure [hPa]')

# Wind speed, gust, direction
ax2.plot(df['time'], df['wind_speed'], color='tab:orange')
ax2.plot(df['time'], df['wind_gust'], color='tab:olive', linestyle='--')
ax2b.plot(df['time'], df['wind_direction'], color='tab:blue', linestyle='-')
ax2.set_ylabel('Wind Speed [m/s]')
ax2b.set_ylabel('Wind Direction')

# Water temperature
ax3.plot(df['time'], df['water_temperature'], color='tab:brown')
ax3.set_ylabel('Water Temperature [degC]')

plt.show()