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pygridmet.pygridmet

pygridmet.pygridmet#

Access the GridMET database for both single single pixel and gridded queries.

Module Contents#

pygridmet.pygridmet.get_bycoords(coords, dates, coords_id=None, crs=4326, variables=None, snow=False, snow_params=None, ssl=True, to_xarray=False)#

Get point-data from the GridMET database at 1-km resolution.

Parameters:

  • coords (tuple or list of tuples) – Coordinates of the location(s) of interest as a tuple (x, y)

  • dates (tuple or list, optional) – Start and end dates as a tuple (start, end) or a list of years [2001, 2010, ...].

  • coords_id (list of int or str, optional) – A list of identifiers for the coordinates. This option only applies when to_xarray is set to True. If not provided, the coordinates will be enumerated.

  • crs (str, int, or pyproj.CRS, optional) – The CRS of the input coordinates, defaults to EPSG:4326.

  • variables (str or list) – List of variables to be downloaded. The acceptable variables are: pr, rmax, rmin, sph, srad, th, tmmn, tmmx, vs, bi, fm100, fm1000, erc, etr, pet, and vpd. Descriptions can be found here. Defaults to None, i.e., all the variables are downloaded.

  • snow (bool, optional) – Compute snowfall from precipitation and minimum temperature. Defaults to False.

  • snow_params (dict, optional) – Model-specific parameters as a dictionary that is passed to the snowfall function. These parameters are only used if snow is True. Two parameters are required: t_rain (deg C) which is the threshold for temperature for considering rain and t_snow (deg C) which is the threshold for temperature for considering snow. The default values are {'t_rain': 2.5, 't_snow': 0.6} that are adopted from https://doi.org/10.5194/gmd-11-1077-2018.

  • ssl (bool, optional) – Whether to verify SSL certification, defaults to True.

  • to_xarray (bool, optional) – Return the data as an xarray.Dataset. Defaults to False.

Returns:

pandas.DataFrame or xarray.Dataset – Daily climate data for a single or list of locations.

Return type:

pandas.DataFrame | xarray.Dataset

Examples

>>> import pygridmet as gridmet
>>> coords = (-1431147.7928, 318483.4618)
>>> dates = ("2000-01-01", "2000-01-31")
>>> clm = gridmet.get_bycoords(
...     coords,
...     dates,
...     crs=3542,
... )
>>> clm["pr (mm)"].mean()
9.677
pygridmet.pygridmet.get_bygeom(geometry, dates, crs=4326, variables=None, snow=False, snow_params=None, ssl=True)#

Get gridded data from the GridMET database at 1-km resolution.

Parameters:

  • geometry (Polygon, MultiPolygon, or bbox) – The geometry of the region of interest.

  • dates (tuple or list, optional) – Start and end dates as a tuple (start, end) or a list of years [2001, 2010, …].

  • crs (str, int, or pyproj.CRS, optional) – The CRS of the input geometry, defaults to epsg:4326.

  • variables (str or list) – List of variables to be downloaded. The acceptable variables are: pr, rmax, rmin, sph, srad, th, tmmn, tmmx, vs, bi, fm100, fm1000, erc, etr, pet, and vpd. Descriptions can be found here. Defaults to None, i.e., all the variables are downloaded.

  • snow (bool, optional) – Compute snowfall from precipitation and minimum temperature. Defaults to False.

  • snow_params (dict, optional) – Model-specific parameters as a dictionary that is passed to the snowfall function. These parameters are only used if snow is True. Two parameters are required: t_rain (deg C) which is the threshold for temperature for considering rain and t_snow (deg C) which is the threshold for temperature for considering snow. The default values are {'t_rain': 2.5, 't_snow': 0.6} that are adopted from https://doi.org/10.5194/gmd-11-1077-2018.

  • ssl (bool, optional) – Whether to verify SSL certification, defaults to True.

Returns:

xarray.Dataset – Daily climate data within the target geometry.

Return type:

xarray.Dataset

Examples

>>> from shapely import Polygon
>>> import pygridmet as gridmet
>>> geometry = Polygon(
...     [[-69.77, 45.07], [-69.31, 45.07], [-69.31, 45.45], [-69.77, 45.45], [-69.77, 45.07]]
... )
>>> clm = gridmet.get_bygeom(geometry, 2010, variables="tmmn")
>>> clm["tmmn"].mean().item()
274.167
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