# Computational Utilities for working with Climate gridMET data [Documentation Home](home) ```{contents} --- local: --- ``` ## 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 ```shell 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 ```{toctree} --- maxdepth: 2 glob: --- members/gridmet_tools members/launcher members/task members/registry ``` ## CWL pipelines and tools ```{toctree} --- maxdepth: 2 glob: --- pipeline/gridmet* ```