diff --git a/ci/environment-py310.yml b/ci/environment-py310.yml
index 514ad93f..6c760a37 100644
--- a/ci/environment-py310.yml
+++ b/ci/environment-py310.yml
@@ -7,6 +7,7 @@ dependencies:
   - dask
   - zarr
   - xarray
+  - xarray-datatree
   - h5netcdf
   - h5py<3.9
   - pandas
diff --git a/ci/environment-py38.yml b/ci/environment-py38.yml
index e0334a88..3d276036 100644
--- a/ci/environment-py38.yml
+++ b/ci/environment-py38.yml
@@ -7,6 +7,7 @@ dependencies:
   - dask
   - zarr
   - xarray
+  - xarray-datatree
   - h5netcdf
   - h5py<3.9
   - pandas
diff --git a/ci/environment-py39.yml b/ci/environment-py39.yml
index ef0f41f1..f5f8f90d 100644
--- a/ci/environment-py39.yml
+++ b/ci/environment-py39.yml
@@ -7,6 +7,7 @@ dependencies:
   - dask
   - zarr
   - xarray
+  - xarray-datatree
   - h5netcdf
   - h5py<3.9
   - pandas
diff --git a/kerchunk/combine.py b/kerchunk/combine.py
index 4426d445..dc2d0b86 100644
--- a/kerchunk/combine.py
+++ b/kerchunk/combine.py
@@ -311,8 +311,13 @@ def store_coords(self):
                         for _ in v
                     ]
                 ).ravel()
-            if "fill_value" not in kw and data.dtype.kind == "i":
-                kw["fill_value"] = None
+            if "fill_value" not in kw:
+                if data.dtype.kind == "i":
+                    kw["fill_value"] = None
+                elif k in z:
+                    # Fall back to existing fill value
+                    kw["fill_value"] = z[k].fill_value
+
             arr = group.create_dataset(
                 name=k,
                 data=data,
diff --git a/kerchunk/grib2.py b/kerchunk/grib2.py
index bd67274d..27a051fb 100644
--- a/kerchunk/grib2.py
+++ b/kerchunk/grib2.py
@@ -1,5 +1,10 @@
 import base64
+import copy
 import logging
+from collections import defaultdict
+from typing import Iterable, List, Dict, Set
+
+import ujson
 
 try:
     import cfgrib
@@ -13,10 +18,12 @@
 
 import fsspec
 import zarr
+import xarray
 import numpy as np
 
 from kerchunk.utils import class_factory, _encode_for_JSON
 from kerchunk.codecs import GRIBCodec
+from kerchunk.combine import MultiZarrToZarr, drop
 
 
 # cfgrib copies over certain GRIB attributes
@@ -328,8 +335,6 @@ def example_combine(
     ...        "consolidated": False,
     ...        "storage_options": {"fo": tot, "remote_options": {"anon": True}}})
     """
-    from kerchunk.combine import MultiZarrToZarr, drop
-
     files = [
         "s3://noaa-hrrr-bdp-pds/hrrr.20190101/conus/hrrr.t22z.wrfsfcf01.grib2",
         "s3://noaa-hrrr-bdp-pds/hrrr.20190101/conus/hrrr.t23z.wrfsfcf01.grib2",
@@ -354,3 +359,224 @@ def example_combine(
         identical_dims=["heightAboveGround", "latitude", "longitude"],
     )
     return mzz.translate()
+
+
+def grib_tree(
+    message_groups: Iterable[Dict],
+    remote_options=None,
+) -> Dict:
+    """
+    Build a hierarchical data model from a set of scanned grib messages.
+
+    The iterable input groups should be a collection of results from scan_grib. Multiple grib files can
+    be processed together to produce an FMRC like collection.
+    The time (reference_time) and step coordinates will be used as concat_dims in the MultiZarrToZarr
+    aggregation. Each variable name will become a group with nested subgroups representing the grib
+    step type and grib level. The resulting hierarchy can be opened as a zarr_group or a xarray datatree.
+    Grib message variable names that decode as "unknown" are dropped
+    Grib typeOfLevel attributes that decode as unknown are treated as a single group
+    Grib steps that are missing due to WrongStepUnitError are patched with NaT
+    The input message_groups should not be modified by this method
+
+    Parameters
+    ----------
+    message_groups: iterable[dict]
+        a collection of zarr store like dictionaries as produced by scan_grib
+    remote_options: dict
+        remote options to pass to ZarrToMultiZarr
+
+    Returns
+    -------
+    list(dict): A new zarr store like dictionary for use as a reference filesystem mapper with zarr
+    or xarray datatree
+    """
+    # Hard code the filters in the correct order for the group hierarchy
+    filters = ["stepType", "typeOfLevel"]
+
+    # TODO allow passing a LazyReferenceMapper as output?
+    zarr_store = {}
+    zroot = zarr.open_group(store=zarr_store)
+
+    aggregations: Dict[str, List] = defaultdict(list)
+    aggregation_dims: Dict[str, Set] = defaultdict(set)
+
+    unknown_counter = 0
+    for msg_ind, group in enumerate(message_groups):
+        assert group["version"] == 1
+
+        gattrs = ujson.loads(group["refs"][".zattrs"])
+        coordinates = gattrs["coordinates"].split(" ")
+
+        # Find the data variable
+        vname = None
+        for key, entry in group["refs"].items():
+            name = key.split("/")[0]
+            if name not in [".zattrs", ".zgroup"] and name not in coordinates:
+                vname = name
+                break
+
+        if vname is None:
+            raise RuntimeError(
+                f"Can not find a data var for msg# {msg_ind} in {group['refs'].keys()}"
+            )
+
+        if vname == "unknown":
+            # To resolve unknown variables add custom grib tables.
+            # https://confluence.ecmwf.int/display/UDOC/Creating+your+own+local+definitions+-+ecCodes+GRIB+FAQ
+            # If you process the groups from a single file in order, you can use the msg# to compare with the
+            # IDX file. The idx files message index is 1 based where the grib_tree message count is zero based
+            logger.warning(
+                "Dropping unknown variable in msg# %d. Compare with the grib idx file to help identify it"
+                " and build an ecCodes local grib definitions file to fix it.",
+                msg_ind,
+            )
+            unknown_counter += 1
+            continue
+
+        logger.debug("Processing vname: %s", vname)
+        dattrs = ujson.loads(group["refs"][f"{vname}/.zattrs"])
+        # filter order matters - it determines the hierarchy
+        gfilters = {}
+        for key in filters:
+            attr_val = dattrs.get(f"GRIB_{key}")
+            if attr_val is None:
+                continue
+            if attr_val == "unknown":
+                logger.warning(
+                    "Found 'unknown' attribute value for key %s in var %s of msg# %s",
+                    key,
+                    vname,
+                    msg_ind,
+                )
+                # Use unknown as a group or drop it?
+
+            gfilters[key] = attr_val
+
+        zgroup = zroot.require_group(vname)
+        if "name" not in zgroup.attrs:
+            zgroup.attrs["name"] = dattrs.get("GRIB_name")
+
+        for key, value in gfilters.items():
+            if value:  # Ignore empty string and None
+                # name the group after the attribute values: surface, instant, etc
+                zgroup = zgroup.require_group(value)
+                # Add an attribute to give context
+                zgroup.attrs[key] = value
+
+        # Set the coordinates attribute for the group
+        zgroup.attrs["coordinates"] = " ".join(coordinates)
+        # add to the list of groups to multi-zarr
+        aggregations[zgroup.path].append(group)
+
+        # keep track of the level coordinate variables and their values
+        for key, entry in group["refs"].items():
+            name = key.split("/")[0]
+            if name == gfilters.get("typeOfLevel") and key.endswith("0"):
+                if isinstance(entry, list):
+                    entry = tuple(entry)
+                aggregation_dims[zgroup.path].add(entry)
+
+    concat_dims = ["time", "step"]
+    identical_dims = ["longitude", "latitude"]
+    for path in aggregations.keys():
+        # Parallelize this step!
+        catdims = concat_dims.copy()
+        idims = identical_dims.copy()
+
+        level_dimension_value_count = len(aggregation_dims.get(path, ()))
+        level_group_name = path.split("/")[-1]
+        if level_dimension_value_count == 0:
+            logger.debug(
+                "Path % has no value coordinate value associated with the level name %s",
+                path,
+                level_group_name,
+            )
+        elif level_dimension_value_count == 1:
+            idims.append(level_group_name)
+        elif level_dimension_value_count > 1:
+            # The level name should be the last element in the path
+            catdims.insert(3, level_group_name)
+
+        logger.info(
+            "%s calling MultiZarrToZarr with idims %s and catdims %s",
+            path,
+            idims,
+            catdims,
+        )
+
+        mzz = MultiZarrToZarr(
+            aggregations[path],
+            remote_options=remote_options,
+            concat_dims=catdims,
+            identical_dims=idims,
+        )
+        group = mzz.translate()
+
+        for key, value in group["refs"].items():
+            if key not in [".zattrs", ".zgroup"]:
+                zarr_store[f"{path}/{key}"] = value
+
+    # Force all stored values to decode as string, not bytes. String should be correct.
+    # ujson will reject bytes values by default.
+    # Using 'reject_bytes=False' one write would fail an equality check on read.
+    zarr_store = {
+        key: (val.decode() if isinstance(val, bytes) else val)
+        for key, val in zarr_store.items()
+    }
+    # TODO handle other kerchunk reference spec versions?
+    result = dict(refs=zarr_store, version=1)
+
+    return result
+
+
+def correct_hrrr_subhf_step(group: Dict) -> Dict:
+    """
+    Overrides the definition of the "step" variable.
+
+    Sets the value equal to the `valid_time - time`
+    in hours as a floating point value. This fixes issues with the HRRR SubHF grib2 step as read by
+    cfgrib via scan_grib.
+    The result is a deep copy, the original data is unmodified.
+
+    Parameters
+    ----------
+    group: dict
+        the zarr group store for a single grib message
+
+    Returns
+    -------
+    dict: A new zarr store like dictionary for use as a reference filesystem mapper with zarr
+    or xarray datatree
+    """
+    group = copy.deepcopy(group)
+    group["refs"]["step/.zarray"] = (
+        '{"chunks":[],"compressor":null,"dtype":"<f8","fill_value":"NaN","filters":null,"order":"C",'
+        '"shape":[],"zarr_format":2}'
+    )
+    group["refs"]["step/.zattrs"] = (
+        '{"_ARRAY_DIMENSIONS":[],"long_name":"time since forecast_reference_time",'
+        '"standard_name":"forecast_period","units":"hours"}'
+    )
+
+    # add step to coords
+    attrs = ujson.loads(group["refs"][".zattrs"])
+    if "step" not in attrs["coordinates"]:
+        attrs["coordinates"] += " step"
+    group["refs"][".zattrs"] = ujson.dumps(attrs)
+
+    fo = fsspec.filesystem("reference", fo=group, mode="r")
+    xd = xarray.open_dataset(fo.get_mapper(), engine="zarr", consolidated=False)
+
+    correct_step = xd.valid_time.values - xd.time.values
+
+    assert correct_step.shape == ()
+    step_float = correct_step.astype("timedelta64[s]").astype("float") / 3600.0
+    step_bytes = step_float.tobytes()
+    try:
+        enocded_val = step_bytes.decode("ascii")
+    except UnicodeDecodeError:
+        enocded_val = (b"base64:" + base64.b64encode(step_bytes)).decode("ascii")
+
+    group["refs"]["step/0"] = enocded_val
+
+    return group
diff --git a/kerchunk/tests/hrrr.wrfsfcf.subset.json b/kerchunk/tests/hrrr.wrfsfcf.subset.json
new file mode 100644
index 00000000..a3d9cddd
--- /dev/null
+++ b/kerchunk/tests/hrrr.wrfsfcf.subset.json
@@ -0,0 +1,776 @@
+[
+  {
+    "version": 1,
+    "refs": {
+      ".zgroup": "{\"zarr_format\":2}",
+      ".zattrs": "{\"GRIB_centre\":\"kwbc\",\"GRIB_centreDescription\":\"US National Weather Service - NCEP \",\"GRIB_edition\":2,\"GRIB_subCentre\":0,\"coordinates\":\"isobaricInhPa latitude longitude step time valid_time\",\"institution\":\"US National Weather Service - NCEP \"}",
+      "u/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"u\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "u/0.0": [
+        "{{u}}",
+        3769472,
+        778132
+      ],
+      "u/.zattrs": "{\"GRIB_DxInMetres\":3000.0,\"GRIB_DyInMetres\":3000.0,\"GRIB_LaDInDegrees\":38.5,\"GRIB_Latin1InDegrees\":38.5,\"GRIB_Latin2InDegrees\":38.5,\"GRIB_LoVInDegrees\":262.5,\"GRIB_NV\":0,\"GRIB_Nx\":1799,\"GRIB_Ny\":1059,\"GRIB_cfName\":\"eastward_wind\",\"GRIB_cfVarName\":\"u\",\"GRIB_dataType\":\"fc\",\"GRIB_gridDefinitionDescription\":\"Lambert Conformal can be secant or tangent, conical or bipolar\",\"GRIB_gridType\":\"lambert\",\"GRIB_iScansNegatively\":0,\"GRIB_jPointsAreConsecutive\":0,\"GRIB_jScansPositively\":1,\"GRIB_latitudeOfFirstGridPointInDegrees\":21.138123,\"GRIB_latitudeOfSouthernPoleInDegrees\":0.0,\"GRIB_longitudeOfFirstGridPointInDegrees\":237.280472,\"GRIB_longitudeOfSouthernPoleInDegrees\":0.0,\"GRIB_missingValue\":3.4028234663852886e+38,\"GRIB_name\":\"U component of wind\",\"GRIB_numberOfPoints\":1905141,\"GRIB_paramId\":131,\"GRIB_shortName\":\"u\",\"GRIB_stepType\":\"instant\",\"GRIB_stepUnits\":1,\"GRIB_typeOfLevel\":\"isobaricInhPa\",\"GRIB_units\":\"m s**-1\",\"_ARRAY_DIMENSIONS\":[\"y\",\"x\"],\"long_name\":\"U component of wind\",\"standard_name\":\"eastward_wind\",\"units\":\"m s**-1\"}",
+      "isobaricInhPa/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "isobaricInhPa/0": "\u0000\u0000\u0000\u0000\u0000@o@",
+      "isobaricInhPa/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"long_name\":\"pressure\",\"positive\":\"down\",\"standard_name\":\"air_pressure\",\"stored_direction\":\"decreasing\",\"units\":\"hPa\"}",
+      "latitude/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"latitude\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "latitude/0.0": [
+        "{{u}}",
+        3769472,
+        778132
+      ],
+      "latitude/.zattrs": "{\"_ARRAY_DIMENSIONS\":[\"y\",\"x\"],\"long_name\":\"latitude\",\"standard_name\":\"latitude\",\"units\":\"degrees_north\"}",
+      "longitude/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"longitude\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "longitude/0.0": [
+        "{{u}}",
+        3769472,
+        778132
+      ],
+      "longitude/.zattrs": "{\"_ARRAY_DIMENSIONS\":[\"y\",\"x\"],\"long_name\":\"longitude\",\"standard_name\":\"longitude\",\"units\":\"degrees_east\"}",
+      "step/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "step/0": "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000",
+      "step/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"long_name\":\"time since forecast_reference_time\",\"standard_name\":\"forecast_period\",\"units\":\"hours\"}",
+      "time/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<i8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "time/0": "base64:ENAUZQAAAAA=",
+      "time/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"calendar\":\"proleptic_gregorian\",\"long_name\":\"initial time of forecast\",\"standard_name\":\"forecast_reference_time\",\"units\":\"seconds since 1970-01-01T00:00:00\"}",
+      "valid_time/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<i8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "valid_time/0": "base64:ENAUZQAAAAA=",
+      "valid_time/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"calendar\":\"proleptic_gregorian\",\"long_name\":\"time\",\"standard_name\":\"time\",\"units\":\"seconds since 1970-01-01T00:00:00\"}"
+    },
+    "templates": {
+      "u": "testdata/hrrr.t01z.wrfsfcf00.grib2"
+    }
+  },
+  {
+    "version": 1,
+    "refs": {
+      ".zgroup": "{\"zarr_format\":2}",
+      ".zattrs": "{\"GRIB_centre\":\"kwbc\",\"GRIB_centreDescription\":\"US National Weather Service - NCEP \",\"GRIB_edition\":2,\"GRIB_subCentre\":0,\"coordinates\":\"isobaricInhPa latitude longitude step time valid_time\",\"institution\":\"US National Weather Service - NCEP \"}",
+      "u/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"u\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "u/0.0": [
+        "{{u}}",
+        5336183,
+        758639
+      ],
+      "u/.zattrs": "{\"GRIB_DxInMetres\":3000.0,\"GRIB_DyInMetres\":3000.0,\"GRIB_LaDInDegrees\":38.5,\"GRIB_Latin1InDegrees\":38.5,\"GRIB_Latin2InDegrees\":38.5,\"GRIB_LoVInDegrees\":262.5,\"GRIB_NV\":0,\"GRIB_Nx\":1799,\"GRIB_Ny\":1059,\"GRIB_cfName\":\"eastward_wind\",\"GRIB_cfVarName\":\"u\",\"GRIB_dataType\":\"fc\",\"GRIB_gridDefinitionDescription\":\"Lambert Conformal can be secant or tangent, conical or bipolar\",\"GRIB_gridType\":\"lambert\",\"GRIB_iScansNegatively\":0,\"GRIB_jPointsAreConsecutive\":0,\"GRIB_jScansPositively\":1,\"GRIB_latitudeOfFirstGridPointInDegrees\":21.138123,\"GRIB_latitudeOfSouthernPoleInDegrees\":0.0,\"GRIB_longitudeOfFirstGridPointInDegrees\":237.280472,\"GRIB_longitudeOfSouthernPoleInDegrees\":0.0,\"GRIB_missingValue\":3.4028234663852886e+38,\"GRIB_name\":\"U component of wind\",\"GRIB_numberOfPoints\":1905141,\"GRIB_paramId\":131,\"GRIB_shortName\":\"u\",\"GRIB_stepType\":\"instant\",\"GRIB_stepUnits\":1,\"GRIB_typeOfLevel\":\"isobaricInhPa\",\"GRIB_units\":\"m s**-1\",\"_ARRAY_DIMENSIONS\":[\"y\",\"x\"],\"long_name\":\"U component of wind\",\"standard_name\":\"eastward_wind\",\"units\":\"m s**-1\"}",
+      "isobaricInhPa/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "isobaricInhPa/0": "base64:AAAAAADAckA=",
+      "isobaricInhPa/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"long_name\":\"pressure\",\"positive\":\"down\",\"standard_name\":\"air_pressure\",\"stored_direction\":\"decreasing\",\"units\":\"hPa\"}",
+      "latitude/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"latitude\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "latitude/0.0": [
+        "{{u}}",
+        5336183,
+        758639
+      ],
+      "latitude/.zattrs": "{\"_ARRAY_DIMENSIONS\":[\"y\",\"x\"],\"long_name\":\"latitude\",\"standard_name\":\"latitude\",\"units\":\"degrees_north\"}",
+      "longitude/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"longitude\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "longitude/0.0": [
+        "{{u}}",
+        5336183,
+        758639
+      ],
+      "longitude/.zattrs": "{\"_ARRAY_DIMENSIONS\":[\"y\",\"x\"],\"long_name\":\"longitude\",\"standard_name\":\"longitude\",\"units\":\"degrees_east\"}",
+      "step/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "step/0": "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000",
+      "step/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"long_name\":\"time since forecast_reference_time\",\"standard_name\":\"forecast_period\",\"units\":\"hours\"}",
+      "time/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<i8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "time/0": "base64:ENAUZQAAAAA=",
+      "time/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"calendar\":\"proleptic_gregorian\",\"long_name\":\"initial time of forecast\",\"standard_name\":\"forecast_reference_time\",\"units\":\"seconds since 1970-01-01T00:00:00\"}",
+      "valid_time/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<i8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "valid_time/0": "base64:ENAUZQAAAAA=",
+      "valid_time/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"calendar\":\"proleptic_gregorian\",\"long_name\":\"time\",\"standard_name\":\"time\",\"units\":\"seconds since 1970-01-01T00:00:00\"}"
+    },
+    "templates": {
+      "u": "testdata/hrrr.t01z.wrfsfcf00.grib2"
+    }
+  },
+  {
+    "version": 1,
+    "refs": {
+      ".zgroup": "{\"zarr_format\":2}",
+      ".zattrs": "{\"GRIB_centre\":\"kwbc\",\"GRIB_centreDescription\":\"US National Weather Service - NCEP \",\"GRIB_edition\":2,\"GRIB_subCentre\":0,\"coordinates\":\"isobaricInhPa latitude longitude step time valid_time\",\"institution\":\"US National Weather Service - NCEP \"}",
+      "u/.zarray": "{\"chunks\":[1059,1799],\"compressor\":null,\"dtype\":\"<f8\",\"fill_value\":null,\"filters\":[{\"dtype\":\"float64\",\"id\":\"grib\",\"var\":\"u\"}],\"order\":\"C\",\"shape\":[1059,1799],\"zarr_format\":2}",
+      "u/0.0": [
+        "{{u}}",
+        9129785,
+        583526
+      ],
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+    },
+    "templates": {
+      "u": "testdata/hrrr.t01z.wrfsfcf01.grib2"
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diff --git a/kerchunk/tests/hrrr.wrfsubhf.subset.json b/kerchunk/tests/hrrr.wrfsubhf.subset.json
new file mode 100644
index 00000000..e32d0052
--- /dev/null
+++ b/kerchunk/tests/hrrr.wrfsubhf.subset.json
@@ -0,0 +1,644 @@
+[
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+      "valid_time/.zarray": "{\"chunks\":[],\"compressor\":null,\"dtype\":\"<i8\",\"fill_value\":null,\"filters\":null,\"order\":\"C\",\"shape\":[],\"zarr_format\":2}",
+      "valid_time/0": "base64:IN4UZQAAAAA=",
+      "valid_time/.zattrs": "{\"_ARRAY_DIMENSIONS\":[],\"calendar\":\"proleptic_gregorian\",\"long_name\":\"time\",\"standard_name\":\"time\",\"units\":\"seconds since 1970-01-01T00:00:00\"}"
+    },
+    "templates": {
+      "u": "testdata/hrrr.t01z.wrfsubhf01.grib2"
+    }
+  }
+]
diff --git a/kerchunk/tests/test_grib.py b/kerchunk/tests/test_grib.py
index f964670c..f6835a30 100644
--- a/kerchunk/tests/test_grib.py
+++ b/kerchunk/tests/test_grib.py
@@ -4,8 +4,16 @@
 import numpy as np
 import pytest
 import xarray as xr
-
-from kerchunk.grib2 import scan_grib, _split_file, GribToZarr
+import datatree
+import zarr
+import ujson
+from kerchunk.grib2 import (
+    scan_grib,
+    _split_file,
+    GribToZarr,
+    grib_tree,
+    correct_hrrr_subhf_step,
+)
 
 cfgrib = pytest.importorskip("cfgrib")
 here = os.path.dirname(__file__)
@@ -83,3 +91,179 @@ def test_subhourly():
     fpath = os.path.join(here, "hrrr.wrfsubhf.sample.grib2")
     result = scan_grib(fpath)
     assert len(result) == 2, "Expected two grib messages"
+
+
+def test_grib_tree():
+    """
+    End-to-end test from grib file to zarr hierarchy
+    """
+    fpath = os.path.join(here, "hrrr.wrfsubhf.sample.grib2")
+    scanned_msg_groups = scan_grib(fpath)
+    corrected_msg_groups = [correct_hrrr_subhf_step(msg) for msg in scanned_msg_groups]
+    result = grib_tree(corrected_msg_groups)
+    fs = fsspec.filesystem("reference", fo=result)
+    zg = zarr.open_group(fs.get_mapper(""))
+    assert isinstance(zg["refc/instant/atmosphere/refc"], zarr.Array)
+    assert isinstance(zg["vbdsf/avg/surface/vbdsf"], zarr.Array)
+    assert (
+        zg["vbdsf/avg/surface"].attrs["coordinates"]
+        == "surface latitude longitude time valid_time step"
+    )
+    assert (
+        zg["refc/instant/atmosphere"].attrs["coordinates"]
+        == "atmosphere latitude longitude step time valid_time"
+    )
+    # Assert that the fill value is set correctly
+    assert zg.refc.instant.atmosphere.step.fill_value is np.NaN
+
+
+# The following two tests use json fixture data generated from calling scan grib
+#   scan_grib("testdata/hrrr.t01z.wrfsubhf00.grib2")
+#   scan_grib("testdata/hrrr.t01z.wrfsubhf01.grib2")
+# and filtering the results message groups for keys starting with "dswrf" or "u"
+# The original files are:
+# gs://high-resolution-rapid-refresh/hrrr.20210928/conus/hrrr.t01z.wrfsubhf00.grib2"
+# gs://high-resolution-rapid-refresh/hrrr.20210928/conus/hrrr.t01z.wrfsubhf01.grib2"
+
+
+def test_correct_hrrr_subhf_group_step():
+    fpath = os.path.join(here, "hrrr.wrfsubhf.subset.json")
+    with open(fpath, "rb") as fobj:
+        scanned_msgs = ujson.load(fobj)
+
+    original_zg = [
+        zarr.open_group(fsspec.filesystem("reference", fo=val).get_mapper(""))
+        for val in scanned_msgs
+    ]
+
+    corrected_msgs = [correct_hrrr_subhf_step(msg) for msg in scanned_msgs]
+
+    corrected_zg = [
+        zarr.open_group(fsspec.filesystem("reference", fo=val).get_mapper(""))
+        for val in corrected_msgs
+    ]
+
+    # The groups that were missing a step variable got fixed
+    assert all(["step" in zg.array_keys() for zg in corrected_zg])
+    assert not all(["step" in zg.array_keys() for zg in original_zg])
+
+    # The step values are corrected to floating point hour
+    assert all([zg.step[()] <= 1.0 for zg in corrected_zg])
+    # The original seems to have values in minutes for some step variables!
+    assert not all(
+        [zg.step[()] <= 1.0 for zg in original_zg if "step" in zg.array_keys()]
+    )
+
+
+def test_hrrr_subhf_corrected_grib_tree():
+    fpath = os.path.join(here, "hrrr.wrfsubhf.subset.json")
+    with open(fpath, "rb") as fobj:
+        scanned_msgs = ujson.load(fobj)
+
+    corrected_msgs = [correct_hrrr_subhf_step(msg) for msg in scanned_msgs]
+    merged = grib_tree(corrected_msgs)
+    zg = zarr.open_group(fsspec.filesystem("reference", fo=merged).get_mapper(""))
+    # Check the values and shape of the time coordinates
+    assert zg.u.instant.heightAboveGround.step[:].tolist() == [
+        0.0,
+        0.25,
+        0.5,
+        0.75,
+        1.0,
+    ]
+    assert zg.u.instant.heightAboveGround.step.shape == (5,)
+
+    assert zg.u.instant.heightAboveGround.valid_time[:].tolist() == [
+        [1695862800, 1695863700, 1695864600, 1695865500, 1695866400]
+    ]
+    assert zg.u.instant.heightAboveGround.valid_time.shape == (1, 5)
+
+    assert zg.u.instant.heightAboveGround.time[:].tolist() == [1695862800]
+    assert zg.u.instant.heightAboveGround.time.shape == (1,)
+
+    assert zg.dswrf.avg.surface.step[:].tolist() == [0.0, 0.25, 0.5, 0.75, 1.0]
+    assert zg.dswrf.avg.surface.step.shape == (5,)
+
+    assert zg.dswrf.avg.surface.valid_time[:].tolist() == [
+        [1695862800, 1695863700, 1695864600, 1695865500, 1695866400]
+    ]
+    assert zg.dswrf.avg.surface.valid_time.shape == (1, 5)
+
+    assert zg.dswrf.avg.surface.time[:].tolist() == [1695862800]
+    assert zg.dswrf.avg.surface.time.shape == (1,)
+
+
+# The following two test use json fixture data generated from calling scan grib
+#   scan_grib("testdata/hrrr.t01z.wrfsfcf00.grib2")
+#   scan_grib("testdata/hrrr.t01z.wrfsfcf01.grib2")
+# and filtering the results for keys starting with "dswrf" or "u"
+# The original files are:
+# gs://high-resolution-rapid-refresh/hrrr.20210928/conus/hrrr.t01z.wrfsfcf00.grib2"
+# gs://high-resolution-rapid-refresh/hrrr.20210928/conus/hrrr.t01z.wrfsfcf01.grib2"
+def test_hrrr_sfcf_grib_tree():
+    fpath = os.path.join(here, "hrrr.wrfsfcf.subset.json")
+    with open(fpath, "rb") as fobj:
+        scanned_msgs = ujson.load(fobj)
+    merged = grib_tree(scanned_msgs)
+    zg = zarr.open_group(fsspec.filesystem("reference", fo=merged).get_mapper(""))
+    # Check the heightAboveGround level shape of the time coordinates
+    assert zg.u.instant.heightAboveGround.heightAboveGround[()] == 80.0
+    assert zg.u.instant.heightAboveGround.heightAboveGround.shape == ()
+
+    assert zg.u.instant.heightAboveGround.step[:].tolist() == [0.0, 1.0]
+    assert zg.u.instant.heightAboveGround.step.shape == (2,)
+
+    assert zg.u.instant.heightAboveGround.valid_time[:].tolist() == [
+        [1695862800, 1695866400]
+    ]
+    assert zg.u.instant.heightAboveGround.valid_time.shape == (1, 2)
+
+    assert zg.u.instant.heightAboveGround.time[:].tolist() == [1695862800]
+    assert zg.u.instant.heightAboveGround.time.shape == (1,)
+
+    # Check the isobaricInhPa level shape and time coordinates
+    assert zg.u.instant.isobaricInhPa.isobaricInhPa[:].tolist() == [
+        250.0,
+        300.0,
+        500.0,
+        700.0,
+        850.0,
+        925.0,
+        1000.0,
+    ]
+    assert zg.u.instant.isobaricInhPa.isobaricInhPa.shape == (7,)
+
+    assert zg.u.instant.isobaricInhPa.step[:].tolist() == [0.0, 1.0]
+    assert zg.u.instant.isobaricInhPa.step.shape == (2,)
+
+    # Valid time values get exploded by isobaricInhPa aggregation
+    # Is this a feature or a bug?
+    expected_valid_times = [
+        [
+            [1695862800 for _ in range(7)],
+            [1695866400 for _ in range(7)],
+        ]
+    ]
+    assert zg.u.instant.isobaricInhPa.valid_time[:].tolist() == expected_valid_times
+    assert zg.u.instant.isobaricInhPa.valid_time.shape == (1, 2, 7)
+
+    assert zg.u.instant.isobaricInhPa.time[:].tolist() == [1695862800]
+    assert zg.u.instant.isobaricInhPa.time.shape == (1,)
+
+
+def test_hrrr_sfcf_grib_datatree():
+    fpath = os.path.join(here, "hrrr.wrfsfcf.subset.json")
+    with open(fpath, "rb") as fobj:
+        scanned_msgs = ujson.load(fobj)
+    merged = grib_tree(scanned_msgs)
+    dt = datatree.open_datatree(
+        fsspec.filesystem("reference", fo=merged).get_mapper(""),
+        engine="zarr",
+        consolidated=False,
+    )
+    # Assert a few things... but if it loads we are mostly done.
+    np.testing.assert_array_equal(
+        dt.u.instant.heightAboveGround.step.values[:],
+        np.array([0, 3600 * 10**9], dtype="timedelta64[ns]"),
+    )
+    assert dt.u.attrs == dict(name="U component of wind")
diff --git a/pyproject.toml b/pyproject.toml
index 603b3e27..94d98675 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -44,6 +44,7 @@ dev = [
     "s3fs",
     "types-ujson",
     "xarray",
+    "xarray-datatree",
     "cfgrib",
     "scipy",
     "netcdf4"