gridMET is a dataset of daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US from 1979-yesterday. The data are also known and cited as METDATA.
Executing pipelines from this package require a collection of shape files corresponding to geographies for which data is aggregated (for example, zip code areas or counties).
The data has to be placed in the following directory structure:
${year}/${geo_type: zip|county|etc.}/${shape:point|polygon}/
Which geography is used is defined by geography argument that defaults
to "zip". Only actually used geographies must have their shape files
for the years actually used.
Hosted Documentation describes Python code and API
usage: gridmet.py [-h] --variable
{bi,erc,etr,fm100,fm1000,pet,pr,rmax,rmin,sph,srad,th,tmmn,tmmx,vpd,vs}
[{bi,erc,etr,fm100,fm1000,pet,pr,rmax,rmin,sph,srad,th,tmmn,tmmx,vpd,vs} ...]
[--strategy {default,all_touched,combined}]
[--destination DESTINATION] [--downloads DOWNLOADS]
[--geography GEOGRAPHY] [--shapes_dir SHAPES_DIR]
[--shapes [SHAPES [SHAPES ...]]]
optional arguments:
-h, --help show this help message and exit
--years [YEARS [YEARS ...]], -y [YEARS [YEARS ...]]
Year or list of years to download. For example, the
following argument: `-y 1992:1995 1998 1999 2011
2015:2017` will produce the following list:
[1992,1993,1994,1995,1998,1999,2011,2015,2016,2017] ,
default: 1990:2020
--compress, -c Use gzip compression for the result, default: True
--variables {bi,erc,etr,fm100,fm1000,pet,pr,rmax,rmin,sph,srad,th,tmmn,tmmx,vpd,vs} [{bi,erc,etr,fm100,fm1000,pet,pr,rmax,rmin,sph,srad,th,tmmn,tmmx,vpd,vs} ...], --var {bi,erc,etr,fm100,fm1000,pet,pr,rmax,rmin,sph,srad,th,tmmn,tmmx,vpd,vs} [{bi,erc,etr,fm100,fm1000,pet,pr,rmax,rmin,sph,srad,th,tmmn,tmmx,vpd,vs} ...]
Gridmet bands or variables
--strategy {default,all_touched,combined,downscale}, -s {default,all_touched,combined,downscale}
Rasterization Strategy, default: default
--destination DESTINATION, --dest DESTINATION, -d DESTINATION
Destination directory for the processed files,
default: data/processed
--raw_downloads RAW_DOWNLOADS
Directory for downloaded raw files, default:
data/downloads
--geography {zip,county,custom}
The type of geographic area over which we aggregate
data, default: zip
--shapes_dir SHAPES_DIR
Directory containing shape files for geographies.
Directory structure is expected to be:
.../${year}/${geo_type}/{point|polygon}/, default:
shapes
--shapes [{point,polygon} [{point,polygon} ...]]
Type of shapes to aggregate over, default: ['polygon']
--points POINTS Path to CSV file containing points, default:
--coordinates COORDINATES [COORDINATES ...], --xy COORDINATES [COORDINATES ...], --coord COORDINATES [COORDINATES ...]
Column names for coordinates, default:
--metadata METADATA [METADATA ...], -m METADATA [METADATA ...], --meta METADATA [METADATA ...]
Column names for metadata, default:
One can try it on nsaph-sandbox01.rc.fas.harvard.edu changing to folder:
/data/projects/gridmet/
and running the following command (do not forget -u option, or you
will not be able to see the progress):
source /home/nsaph/projects/tools/gridmet/.gridmet/bin/activate && PYTHONPATH=/home/nsaph/projects/tools/gridmet/src/python python -u -m gridmet --var tmmx -y 2001 --shapes_dir shapes/zip_shape_files --strategy downscale
The results can be then found in data/processed folder