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	90.0	3.0	5.0	1.5	12.0	8.4	234.0	1023.3	3.0	3.9	3.0	NaN	NaN	NaN	2026-01-21 23:20:00+00:00
1	90.0	3.0	4.0	NaN	NaN	NaN	NaN	1023.3	3.0	3.9	3.0	NaN	NaN	NaN	2026-01-21 23:10:00+00:00
2	60.0	3.0	4.0	NaN	NaN	NaN	NaN	1023.4	2.9	3.9	2.9	NaN	-0.8	NaN	2026-01-21 23:00:00+00:00
3	90.0	3.0	4.0	1.5	11.0	8.1	229.0	1023.5	2.9	3.9	2.9	NaN	NaN	NaN	2026-01-21 22:50:00+00:00
4	110.0	3.0	4.0	1.5	NaN	8.1	229.0	1023.6	2.9	3.9	2.9	NaN	NaN	NaN	2026-01-21 22:40:00+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()