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

Retrieve estimated daily precipitation and temperature data from the Northeast Regional Climate Center 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 Cornell’s Northeast Regional Climate Center (NRCC) 1.

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

  • 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

  • grid (int) – Options are either 1 (for “NRCC Interpolated”) or 3 (for “NRCC Hi-Resolution”). See 2 for details.

  • 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.


  • data (pandas.DataFrame) – Daily precipitation [mm], temperature [Celsius], and degree day [Celsius-days] data

  • metadata (dict) – Metadata of the selected grid cell


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


The returned values are 24-hour aggregates, but the aggregation period may not be midnight to midnight in local time. Check the ACIS and NRCC documentation for details.





ACIS Gridded Data


ACIS Web Services


>>> from pvlib.iotools import get_acis_nrcc
>>> df, meta = get_acis_nrcc(40, -80, '2020-01-01', '2020-12-31', grid=1)