Computational Utilities for working with Climate gridMET data

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What is gridMET?

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.

Using command line gridMET utility 

    usage: python -m dorieh.rasters.launcher [-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:

Example 

python -u -m dorieh.rasters.launcher --var tmmx -y 2001 --shapes_dir shapes/zip_shape_files --strategy downscale

The results can be then found in data/processed folder

Python modules 