Improve HRRR-Zarr demo program

herbie/hrrr_zarr.py

@@ -1,30 +1,90 @@
-## Brian Blaylock
-## July 29, 2021
-
 """
-Some initial work to use Herbie to access HRRR-Zarr
+Some initial work to use Herbie to access HRRR-Zarr.
+Code from Brian Blaylock and Taylor Gowan.
 
-The directory structure is very different than the GRIB2 format.
+The directory structure is very different from the GRIB2 format.
 
 - https://hrrrzarr.s3.amazonaws.com/index.html
 - https://mesowest.utah.edu/html/hrrr/
-
+- https://mesowest.utah.edu/html/hrrr/zarr_documentation/html/python_data_loading.html
+- https://mesowest.utah.edu/html/hrrr/zarr_documentation/html/xarray_one_day_analysis_example.html
+- https://github.com/ProjectPythia/intake-cookbook/tree/main/notebooks
 """
+import datetime
 
+import cartopy.crs as ccrs
 import pandas as pd
 import s3fs
 import xarray as xr
 
-# Let's grab an analysis file using s3fs
 
-fs = s3fs.S3FileSystem(anon=True)
+def load_dataset(urls):
+    """
+    We use xarray's `open_mfdataset` to load the data. There's a couple of things
+    missing from the metadata, so we use a metpy extension to add projection info
+    and latitude/longitude. We also promote the "time" attribute to a coordinate
+    so that combining the datasets for each hour will work later on.
+    """
+    projection = ccrs.LambertConformal(
+        central_longitude=262.5,
+        central_latitude=38.5,
+        standard_parallels=(38.5, 38.5),
+        globe=ccrs.Globe(semimajor_axis=6371229, semiminor_axis=6371229),
+    )
+
+    fs = s3fs.S3FileSystem(anon=True)
+    ds = xr.open_mfdataset([s3fs.S3Map(url, s3=fs) for url in urls], engine="zarr")
+    ds = ds.rename(projection_x_coordinate="x", projection_y_coordinate="y")
+    ds = ds.metpy.assign_crs(projection.to_cf())
+    ds = ds.metpy.assign_latitude_longitude()
+    ds = ds.set_coords("time")
+    return ds
+
+
+def load_combined_dataset(start_date, num_hours, level, param_short_name):
+    """
+    Format the URLs to load the data, and combine the hours using `xarray.concat`.
+    Note that because there's an extra level of nesting for the main data variable
+    (level and variable name), we have to get both the zarr group url and the url
+    for the nested subgroup. That's why we have to use `open_mfdataset`: "mf"
+    means "multifile") - other zarr datasets likely won't have this quirk.
+    """
+    combined_ds = None
+    for i in range(num_hours):
+        time = start_date + datetime.timedelta(hours=i)
+        group_url = time.strftime(
+            f"s3://hrrrzarr/sfc/%Y%m%d/%Y%m%d_%Hz_anl.zarr/{level}/{param_short_name}"
+        )
+        subgroup_url = f"{group_url}/{level}"
+        partial_ds = load_dataset([group_url, subgroup_url])
+        if not combined_ds:
+            combined_ds = partial_ds
+        else:
+            combined_ds = xr.concat(
+                [combined_ds, partial_ds], dim="time", combine_attrs="drop_conflicts"
+            )
+    return combined_ds
+
+
+def demo_tmp2m():
+
+    # Let's grab an analysis file.
+    ds = load_combined_dataset(
+        start_date=pd.to_datetime("2021-07-03 12:00"),
+        num_hours=4,
+        level="2m_above_ground",
+        param_short_name="TMP",
+    )
 
-date = pd.to_datetime("2021-07-03 12:00")
-level = "2m_above_ground"
-var = "TMP"
+    print(ds)
+    ds.info()
+    print()
+    print(ds.coords)
+    print()
+    print(ds.data_vars)
+    print()
+    print(ds["TMP"])
 
-url = f"hrrrzarr/sfc/{date:%Y%m%d}/{date:%Y%m%d_%H}z_anl.zarr/{level}/{var}/{level}/"
-store = s3fs.S3Map(root=url, s3=fs, check=False)
-ds = xr.open_zarr(store)
 
-ds.TMP.plot()
+if __name__ == "__main__":
+    demo_tmp2m()