Wind Speed and Gust from NDBC SOS service

In [20]:

    

from datetime import datetime
import cStringIO
import pandas as pd
from owslib.sos import SensorObservationService


    

In [21]:

    

# pick a buoy, any buoy
#sta_id='44066'  # texas tower
sta_id='44013'  # boston buoy


    

In [22]:

    

# pick a start & stop time
start = '2013-06-12T00:00:00Z'
stop = '2013-06-14T00:00:00Z'


    

In [23]:

    

from IPython.core.display import HTML
HTML('<iframe src=http://www.ndbc.noaa.gov/station_page.php?station=%s width=950 height=400></iframe>' % sta_id)


    

    
Out[23]:

In [24]:

    

ndbc=SensorObservationService('http://sdf.ndbc.noaa.gov/sos/server.php?request=GetCapabilities&service=SOS')


    

In [25]:

    

id=ndbc.identification
id.title


    

    
Out[25]:





'National Data Buoy Center SOS'

In [26]:

    

contents = ndbc.contents
network = contents['network-all']
network.description


    

    
Out[26]:





'All stations on the NDBC SOS server'

In [27]:

    

id.title


    

    
Out[27]:





'National Data Buoy Center SOS'

In [28]:

    

rfs = network.response_formats


    

In [29]:

    

print '\n'.join(rfs)


    

    




text/xml;subtype="om/1.0.0"
text/csv
text/tab-separated-values
application/vnd.google-earth.kml+xml
text/xml;schema="ioos/0.6.1"
application/ioos+xml;version=0.6.1

In [30]:

    

station = contents['station-%s' % sta_id]


    

In [31]:

    

station.name


    

    
Out[31]:





'urn:ioos:station:wmo:44013'

In [32]:

    

station.description


    

    
Out[32]:





'BOSTON 16 NM East of Boston, MA'

In [33]:

    

getob = ndbc.get_operation_by_name('getobservation')


    

In [34]:

    

getob.parameters


    

    
Out[34]:





{'observedProperty': {'values': ['air_temperature',
   'air_pressure_at_sea_level',
   'sea_water_electrical_conductivity',
   'currents',
   'sea_water_salinity',
   'sea_floor_depth_below_sea_surface',
   'sea_water_temperature',
   'waves',
   'winds']},
 'responseFormat': {'values': ['text/xml;subtype="om/1.0.0"',
   'text/csv',
   'text/tab-separated-values',
   'application/vnd.google-earth.kml+xml',
   'text/xml;schema="ioos/0.6.1"',
   'application/ioos+xml;version=0.6.1']}}

In [35]:

    

# issue the SOS get_obs request
response = ndbc.get_observation(offerings=['urn:ioos:station:wmo:%s' % sta_id],
                                 responseFormat='text/csv',
                                 observedProperties=['winds'],
                                 eventTime='%s/%s' % (start,stop))


    

In [36]:

    

df2 = pd.read_csv(cStringIO.StringIO(response.strip()),index_col='date_time',parse_dates=True)  # skip the units row


    

In [37]:

    

df2.head()


    

    
Out[37]:




&ltclass 'pandas.core.frame.DataFrame'>
DatetimeIndex: 5 entries, 2013-06-12 00:50:00 to 2013-06-12 04:50:00
Data columns (total 9 columns):
station_id                      5  non-null values
sensor_id                       5  non-null values
latitude (degree)               5  non-null values
longitude (degree)              5  non-null values
depth (m)                       5  non-null values
wind_from_direction (degree)    5  non-null values
wind_speed (m/s)                5  non-null values
wind_speed_of_gust (m/s)        5  non-null values
upward_air_velocity (m/s)       0  non-null values
dtypes: float64(7), object(2)

In [38]:

    

df2[['wind_speed_of_gust (m/s)','wind_speed (m/s)']].plot(figsize=(12,4),title=station.description,legend=True)


    

    
Out[38]:





<matplotlib.axes.AxesSubplot at 0x44fc350>