WIP: ssebop

recipes/us-ssebop/meta.yaml

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+title: 'US SSEBop Evapotranspiration'
+description: >
+  Actual Evapotranspiration (ETa) produced using MODIS and the operational Simplified Surface Energy Balance (SSEBop) model
+  for the period 2000 to 2022 at 1-kilometer (km) spatial resolution for the contiguous United States.
+pangeo_forge_version: '0.10.3'
+pangeo_notebook_version: '2022.07.13'
+recipes:
+  - id: us-ssebop
+    object: 'recipe:recipe'
+provenance:
+  providers:
+    - name: 'USGS'
+      description: 'US Geological Survey'
+      roles:
+        - host
+        - provider
+    - name: 'Gabriel Senay'
+      description: 'Gabriel Senay, Research Physical Scientist, US Geological Survey'
+      orcid: '0000-0002-8810-8539'
+      roles:
+        - producer
+      url: 'https://www.usgs.gov/staff-profiles/gabriel-b-senay'
+  license: 'CC0'
+maintainers:
+  - name: 'Timothy Hodson'
+    orcid: '0000-0003-0962-5130'
+    github: thodson-usgs
+bakery:
+  id: 'pangeo-ldeo-nsf-earthcube'

recipes/us-ssebop/recipe.py

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+from datetime import date
+
+import apache_beam as beam
+import pandas as pd
+import xarray as xr
+
+from pangeo_forge_recipes.patterns import ConcatDim, FilePattern
+from pangeo_forge_recipes.transforms import Indexed, StoreToZarr, T
+
+input_url_pattern = (
+    'zip+'
+    'https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/uswem/web/'
+    'conus/eta/modis_eta/daily/downloads/'
+    'det{yyyyjjj}.modisSSEBopETactual.zip'
+    '!/det{yyyyjjj}.modisSSEBopETactual.tif'
+)
+
+start = date(2001, 1, 1)
+end = date(2022, 10, 7)
+dates = pd.date_range(start, end, freq='1D')
+
+
+def make_url(time: pd.Timestamp) -> str:
+    return input_url_pattern.format(yyyyjjj=time.strftime('%Y%j'))
+
+
+pattern = FilePattern(make_url, ConcatDim(name='time', keys=dates, nitems_per_file=1))
+
+
+class Preprocess(beam.PTransform):
+    """Preprocessor transform."""
+
+    @staticmethod
+    def _preproc(item: Indexed[T]) -> Indexed[xr.Dataset]:
+        import numpy as np
+        import rioxarray
+
+        index, url = item
+        time_dim = index.find_concat_dim('time')
+        time_index = index[time_dim].value
+        time = dates[time_index]
+
+        da = rioxarray.open_rasterio(url).drop('band')
+        da = da.rename({'x': 'lon', 'y': 'lat'})
+        ds = da.to_dataset(name='aet')
+        ds = ds['aet'].where(ds['aet'] != 9999)
+        ds = ds.expand_dims(time=np.array([time]))
+
+        return index, ds
+
+    def expand(self, pcoll: beam.PCollection) -> beam.PCollection:
+        return pcoll | beam.Map(self._preproc)
+
+
+recipe = (
+    beam.Create(pattern.items())
+    | Preprocess()
+    | StoreToZarr(
+        store_name='us-ssebop.zarr',
+        combine_dims=pattern.combine_dim_keys,
+        target_chunks={'time': 1, 'lat': int(2834 / 2), 'lon': int(6612 / 6)},
+    )
+)

recipes/us-ssebop/requirements.txt

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+rioxarray