DOI-USGS / DOI-USGS/lake-temperature-process-models

Get GLM output into netCDF DSG format

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#31 10 comments 0 reactions 0 assignees View on GitHub
enhancement
Dominant language
R
Stars
2
Forks
4
PR merge metrics
No merged PRs in 30d

Description

Currently the GLM output is being [stored in feather files](https://github.com/USGS-R/lake-temperature-process-models/blob/main/3_extract.R#L7-L11), with one feather file per lake-gcm combo (6 files per lake). For sharing on [sciencebase](https://www.sciencebase.gov/catalog/item/6206d3c2d34ec05caca53071), we are currently (per #20) zipping these feather files together by tile number (4 zip files in total).

Per Jordan comments,
> I'd like to propose a new data release format that uses netcdf discrete sampling geom to put all of the lakes in a single file, like we did with Jared's EA-LSTM data release. Do do so, we'd need to address one challenge with depth but there are options for that.

we'd like to move to storing the output in netCDF DSG format. As GLM generates temperature profiles, this would mean adding another dimension for depth. That is not currently supported by the [`write_timeseries_dsg()`](https://github.com/USGS-R/ncdfgeom/blob/main/R/write_timeseries_dsg.R) function of `ncdfgeom`, but I will submit an issue there to see if it would be within scope of that function to add that functionality.

Contributor guide

Open the contributing guide

Research direction

Start with 3_extract.R lines 7–11 to understand the current feather outputs and review issue #20 for the existing packaging approach. Then read ncdfgeom's write_timeseries_dsg.R and the linked discussion about depth support. Done means defining and implementing an agreed netCDF DSG representation for all lakes and depth profiles, with the resulting files suitable for the ScienceBase release.

Written by the indexing model from the issue text.

Assessment

Tech stack
r
Domain
data-engineering
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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