pvlib.iotools.get_acis_prism#

pvlib.iotools.get_acis_prism(latitude, longitude, start, end, map_variables=True, url='https://data.rcc-acis.org/GridData', **kwargs)[source]#

Retrieve estimated daily precipitation and temperature data from PRISM via the Applied Climate Information System (ACIS).

ACIS [2], [3] aggregates and provides access to climate data from many underlying sources. This function retrieves daily data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) [1], a gridded precipitation and temperature model from Oregon State University.

Geographical coverage: US, Central America, and part of South America. Approximately 0° to 50° in latitude and -130° to -65° in longitude.

Parameters:

latitude (float) – in decimal degrees, between -90 and 90, north is positive
longitude (float) – in decimal degrees, between -180 and 180, east is positive
start (datetime-like) – First day of the requested period
end (datetime-like) – Last day of the requested period
map_variables (bool, default True) – When True, rename data columns and metadata keys to pvlib variable names where applicable. See variable VARIABLE_MAP.
url (str, default: ‘https://data.rcc-acis.org/GridData’) – API endpoint URL
kwargs – Optional parameters passed to requests.post.

Returns:

data (pandas.DataFrame) – Daily precipitation [mm], temperature [Celsius], and degree day [Celsius-days] data
metadata (dict) – Metadata of the selected grid cell

Raises:

requests.HTTPError – A message from the ACIS server if the request is rejected

Notes

PRISM data is aggregated from 12:00 to 12:00 UTC, meaning data labeled May 26 reflects to the 24 hours ending at 7:00am Eastern Standard Time on May 26.

References

Examples

>>> from pvlib.iotools import get_acis_prism
>>> df, meta = get_acis_prism(40, 80, '2020-01-01', '2020-12-31')