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('46006')
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	290.0	13.0	17.0	2.6	12.0	7.0	289.0	1008.1	8.7	12.2	5.7	NaN	NaN	NaN	2025-03-11 16:20:00+00:00
1	280.0	12.0	17.0	NaN	NaN	NaN	NaN	1008.0	9.8	12.2	5.9	NaN	NaN	NaN	2025-03-11 16:10:00+00:00
2	290.0	10.0	12.0	NaN	NaN	NaN	NaN	1007.9	9.7	12.2	5.7	NaN	-2.1	NaN	2025-03-11 16:00:00+00:00
3	280.0	9.0	12.0	2.7	11.0	6.9	300.0	1007.9	9.4	12.2	6.0	NaN	NaN	NaN	2025-03-11 15:50:00+00:00
4	280.0	11.0	15.0	2.7	NaN	6.9	300.0	1008.2	9.2	12.2	6.1	NaN	NaN	NaN	2025-03-11 15:40:00+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()