TTU_2019 Conversion to Zarr

Checklist for Workflow associated with dataset conversion:

Dataset Name: High-Resolution Precipitation Projections for the South Central U.S.

https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/TTU_2019/rcp45_gridded_annual_data https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/TTU_2019/rcp85_gridded_annual_data https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/TTU_2019/rcp45_time_slices https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/TTU_2019/rcp85_time_slices https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/TTU_2019/rcp45_station_data https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/TTU_2019/rcp85_station_data

https://cida.usgs.gov/thredds/catalog/demo/thredds/TTU_2019/catalog.html

Three spatio-temporal axes --

slices:

    Float64 lat[lat = 198];
    Float64 lon[lon = 337];
    Float64 time[time = 5];

Gridded annual:

    Float64 lat[lat = 198];
    Float64 lon[lon = 337];
    Float64 time[time = 151];

Stations:

    String station_ids[stations = 8701];
    Float64 time[time = 151];

Aggregations are semi complex -- suggest trying to subset OPeNDAP endpoints directly.

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• Identify Source Data location and access (check the dataset spreadsheet)

• Collect ownership information (Who do we ask questions of if we have problems?)

• Create new workflow notebook from template; stash in the ./workflows folder tree in an appropriate spot.
  • Identify landing spot on S3 (currently somewhere in: https://s3.console.aws.amazon.com/s3/buckets/nhgf-development?prefix=workspace/&region=us-west-2)
  • Calculate chunking, layout, compression, etc
  • Run notebook
  • Read test (pattern to be determined by the dataset)
• Create STAC catalog entry;
  • Verify all metadata
  • Create entry
• Reportage
  • add notebook and the dask performance report to the repo
  • Calculate summary statistics on output (compression ratio, total size)
  • Save STAC JSON snippet to repo
• Merge and close the issue.