Usage¶

SNOTEL¶

Use metloom to find data for a SNOTEL station:

from datetime import datetime
from metloom.pointdata import SnotelPointData

snotel_point = SnotelPointData("713:CO:SNTL", "MyStation")
df = snotel_point.get_daily_data(
    datetime(2020, 1, 2), datetime(2020, 1, 20),
    [snotel_point.ALLOWED_VARIABLES.SWE]
)
print(df)

CDEC¶

Use metloom to find snow courses within a geometry from CDEC:

from metloom.pointdata import CDECPointData
import geopandas as gpd

fp = <path to shape file>
obj = gpd.read_file(fp)

vrs = [
    CdecStationVariables.SWE,
    CdecStationVariables.SNOWDEPTH
]
points = CDECPointData.points_from_geometry(obj, vrs, snow_courses=True)
df = points.to_dataframe()
print(df)

USGS¶

You can use point.get_daily_data() similarly to the SNOTEL example above to get daily data, and USGSPointData.points_from_geometry() similarly to the CDEC example above. To use metloom to find 15-minute streamflow from USGS:

from metloom.pointdata import USGSPointData
from datetime import datetime

stn_code = "13206000"
stn_name = "BOISE RIVER AT GLENWOOD BRIDGE NR BOISE ID"

point = USGSPointData(stn_code, stn_name)

df = point.get_instantaneous_data(
    datetime(2020, 1, 2), datetime(2020, 1, 20),
    [point.ALLOWED_VARIABLES.DISCHARGE]
)
print(df)

National Weather Service (NWS) Forecast¶

The NWS forecast pulls the current forecast starting from today. When defining a point, give it your own name and id, and make sure to provide the latitude and longitude as a shapely point for the initial metadata.

Then you can use get_daily_forecast or get_hourly_forecast to retrive data.

Note: the data will be aggregated to hourly or daily using mean or sum depending on ``accumulated=True`` on the variable description

Also - the point metadata is the center of the NWS pixel containing your initial input point.

Example of pulling the daily forecast:

from metloom.pointdata import NWSForecastPointData
from metloom.variables import NWSForecastVariables
from shapely.geometry import Point

inintial_point = Point(-119, 43)
pt = NWSForecastPointData(
    "my_point_id", "my_point_name", initial_metadata=inintial_point
)

df = pt.get_daily_forecast([
    NWSForecastVariables.TEMP,
    NWSForecastVariables.PRECIPITATIONACCUM,
]

Mesowest¶

You can also use the Mesowest network if you sign up for an API token which is free!

Create/Copy token from synoptics labs
Create ~/.synoptic_token.json
Copy your token and place it in the file like {"token":"<MY_TOKEN_HERE>"} and save it.
Protect the file using chmod 600 ~/.synoptic_token.json

To pull stations using Mesowest:

from datetime import datetime
from metloom.pointdata import MesowestPointData

meso_point = MesowestPointData("ITD48", "IDAHOME")
df = snotel_point.get_daily_data(
    datetime(2021, 12, 25), datetime(2021, 12, 26),
    [meso_point.ALLOWED_VARIABLES.TEMP]
)
print(df)

Center for Snow and Avalanche Studies (CSAS)¶

There are 4 stations of interest maintained by the CSAS. Senator Beck Study plot, Swamp Angel Study Plot, Senator Beck Stream Gauge and Putney Study plot. These four stations contain a wealth of data useful for studying and validating snow processes. The files exist as a flat csv file so requests using this will simply download the file, interpret the datetime index and crop according to your request. Since it is a CSV the file will be stored in a local cache in the same directory you ran your code. This way the download times are reduced.

Additionally, the CSAS data is not available in realtime (at least as of June 2024). Data is updated annually and stored on the website. Metloom will try to stay as up to date as possible when the files are updated. Please feel free to submit a PR if you know the data has been updated. Checkout the facilities page on CSAS to see more about the stations.

To pull stations using CSAS:

from metloom.pointdata import CSASMet
from metloom.variables import CSASVariables
from datetime import datetime

start = datetime(2023, 1, 1)
end = datetime(2023, 6, 1)
sbsp = CSASMet('SBSP')
df_sbsp = sbsp.get_daily_data(start, end, [CSASVariables.SNOWDEPTH])

If you use these data, please use the appropriate citations and give credit to the institution.

SnowEx¶

During the NASA SnowEx campaign there were a handful of met stations deployed which are now published on the NSIDC. These stations have been mapped into metloom to increase the utility/convenience of these data. The SnowEx data is in a csv file format and thus any queries will download the appropriate files to a local cache to reduce download times. For this to work you need to have a .netrc and an account with the NSIDC. See the access guide for more help.

To pull stations using SnowEx:

from metloom.pointdata import SnowExMet
from metloom.variables import SnowExVariables
from datetime import datetime

start = datetime(2020, 1, 1)
end = datetime(2020, 6, 1)

# Grand Mesa Study Plot
gmsp = SnowExMet('GMSP')
df_gmsp = gmsp.get_daily_data(start, end, [SnowExVariables.SNOWDEPTH])

My variables aren’t here¶

Not all of the available variables for each datasource are implemented within this package. It is easy to extend the classes to add more variables Below is an example on how to add more variables. This is also a great to get started contributing to metloom!

from datetime import datetime
from metloom.variables import CdecStationVariables, SensorDescription
from metloom.pointdata import CDECPointData


class MyVariables(CdecStationVariables):
    """
    SensorDescription("<variable code>", "Desired Name", "Description")
    CDEC variable codes are available with CDEC station metadata
    """
    RH = SensorDescription("12", "Relative Humidity", "RELATIVE HUMIDITY [%]")
    WINDSP = SensorDescription("9", "Wind Speed", "WIND SPEED [mph]")


class MyCDECPointData(CDECPointData):
    ALLOWED_VARIABLES = MyVariables


variables = [MyVariables.RH, MyVariables.WINDSP]
stn = MyCDECPointData("TNY", "Tenaya Lake")
df = stn.get_daily_data(datetime(2021, 12, 25), datetime(2021, 12, 26), variables)

print(df[['Relative Humidity', 'Wind Speed']])