Add recipe for gpcp

[rabernat]

This is a very widely used precipitation dataset.

[pangeo-forge-bot]

It looks like your meta.yaml does not conform to the specification.

            1 validation error for MetaYaml
    pangeo_notebook_version
      field required (type=value_error.missing)

Please correct your meta.yaml and commit the corrections to this PR.

[pangeo-forge-bot]

🎉 New recipe runs created for the following recipes at sha 9d4245b9003b7af09035a5ad75716d90f3f9213f:

• gpcp: https://pangeo-forge.org/dashboard/recipe-run/898

[rabernat]

/run recipe-test recipe_run_id=898

[pangeo-forge-bot]

When I tried to import your recipe module, I encountered this error

            line 17, in <module>
        fs.ls(url_base + str(year), detail=False)
    NameError: name 'fs' is not defined

Please correct your recipe module so that it's importable.

[pangeo-forge-bot]

🎉 New recipe runs created for the following recipes at sha e9e9a38140db467033c2b4520bcbe28734cc4d82:

• gpcp: https://pangeo-forge.org/dashboard/recipe-run/899

[rabernat]

/run recipe-test recipe_run_id=899

[pangeo-forge-bot]

✨ A test of your recipe gpcp is now running on Pangeo Forge Cloud!

I'll notify you with a comment on this thread when this test is complete. (This could be a little while...)

In the meantime, you can follow the logs for this recipe run at https://pangeo-forge.org/dashboard/recipe-run/899

[pangeo-forge-bot]

🥳 Hooray! The test execution of your recipe gpcp succeeded.

Here is a static representation of the dataset built by this recipe:

            <xarray.Dataset>
    Dimensions:      (latitude: 180, nv: 2, longitude: 360, time: 2)
    Coordinates:
        lat_bounds   (latitude, nv) float32 dask.array<chunksize=(180, 2), meta=np.ndarray>
      * latitude     (latitude) float32 -90.0 -89.0 -88.0 -87.0 ... 87.0 88.0 89.0
        lon_bounds   (longitude, nv) float32 dask.array<chunksize=(360, 2), meta=np.ndarray>
      * longitude    (longitude) float32 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0
      * time         (time) datetime64[ns] 1996-10-01 1996-10-02
        time_bounds  (time, nv) datetime64[ns] dask.array<chunksize=(2, 2), meta=np.ndarray>
    Dimensions without coordinates: nv
    Data variables:
        precip       (time, latitude, longitude) float32 dask.array<chunksize=(2, 180, 360), meta=np.ndarray>
    Attributes: (12/45)
        Conventions:                CF-1.6, ACDD 1.3
        Metadata_Conventions:       CF-1.6, Unidata Dataset Discovery v1.0, NOAA ...
        acknowledgment:             This project was supported in part by a grant...
        cdm_data_type:              Grid
        cdr_program:                NOAA Climate Data Record Program for satellit...
        cdr_variable:               precipitation
        ...                         ...
        standard_name_vocabulary:   CF Standard Name Table (v41, 22 February 2017)
        summary:                    Global Precipitation Climatology Project (GPC...
        time_coverage_duration:     P1D
        time_coverage_end:          1996-10-01T23:59:59Z
        time_coverage_start:        1996-10-01T00:00:00Z
        title:                      Global Precipitation Climatatology Project (G...

You can also open this dataset by running the following Python code

import fsspec
import xarray as xr

dataset_public_url = 'https://ncsa.osn.xsede.org/Pangeo/pangeo-forge-test/prod/recipe-run-899/pangeo-forge/staged-recipes/gpcp.zarr'
mapper = fsspec.get_mapper(dataset_public_url)
ds = xr.open_zarr(mapper, consolidated=True)
ds

in this (or your Python interpreter of choice).

Checklist

Please copy-and-paste the list below into a new comment on this thread, and check the boxes off as you've reviewed them.

Note: This test execution is limited to two increments in the concatenation dimension, so you should expect the length of that dimension (e.g, "time" or equivalent) to be 2.

- [ ] Are the dimension lengths correct?
- [ ] Are all of the expected variables present?
- [ ] Does plotting the data produce a plot that looks like your dataset?
- [ ] Can you run a simple computation/reduction on the data and produce a plausible result?