Accessing Surface Data

There are a number of different methods for accessing current (and archived) surface observations. The first first access point is only accessible within the Valparaiso University domain. The second two are accessible to anyone on the web.

Valpo Local Data Access

Local Access Current Data: /data/ldmdata/surface/sao

The current data are roughly the past two weeks worth of surface data.

from metpy.io import metar

data = '/data/ldmdata/surface/sao/20220114_sao.wmo'

df = metar.parse_metar_file(data, year=2021, month=1)

Valpo Remote Data Access

Remote Access Current Data (Valpo: http://bergeron.valpo.edu/current_surface_data)

The current data are roughly the past two weeks worth of surface data.

from io import StringIO
from urllib.request import urlopen

from metpy.io import metar

data = StringIO(urlopen('http://bergeron.valpo.edu/current_surface_data/'
                        '20201221_sao.wmo').read().decode('utf-8', 'backslashreplace'))

df = metar.parse_metar_file(data, year=2020, month=12)

Iowa State Current and Archive Remote Data Access

Remote Access Current and Archived Data (Iowa State Archive: 1950-current):

from io import StringIO
from urllib.request import urlopen

from metpy.io import metar
import pandas as pd

# Set a date and time for the observations you want
date = datetime(1993, 3, 13, 12)

# Set a time window to look for observations
dt = timedelta(minutes=15)

# Use datetime and time window to set final start/end times
sdate = date - dt
edate = date + dt

# Use Pandas remote csv reading capability to grab desired data
# Make Missing values (\'M\') into NaNs
# Replace Trace precip with very small float value

data = pd.read_csv('http://mesonet.agron.iastate.edu/'
                   'cgi-bin/request/asos.py?data=all&tz=Etc/UTC&format=comma&'
                   f'latlon=yes&elev=yes&year1={sdate.year}&month1={sdate.month}&'
                   f'day1={sdate.day}&hour1={sdate.hour}&minute1={sdate.minute}&'
                   f'year2={edate.year}&month2={edate.month}&day2={edate.day}&'
                   f'hour2={edate.hour}&minute2={edate.minute}', skiprows=5,
                   na_values=['M'],
                   low_memory=False).replace('T', 0.00001).groupby('station').tail(1)

# Parse METAR observations using MetPy
df = metar.parse_metar_file(StringIO('\n'.join(val for val in data.metar)),
                            year=date.year, month=date.month)