diff --git a/doc/source/extended_documentation/calibration/reliability_calibration/reliability_calibration_examples.rst b/doc/source/extended_documentation/calibration/reliability_calibration/reliability_calibration_examples.rst
index 5afd0485c4..ce65e10e0f 100644
--- a/doc/source/extended_documentation/calibration/reliability_calibration/reliability_calibration_examples.rst
+++ b/doc/source/extended_documentation/calibration/reliability_calibration/reliability_calibration_examples.rst
@@ -12,7 +12,7 @@ The reliability calibration tables returned by this plugin are structured as sho
        Auxiliary coordinates:
             table_row_name                            -                   x                   -                           -                             -
        Scalar coordinates:
-            cycle_hour: 22
+            forecast_reference_time: 2017-11-11 00:00:00, bound=(2017-11-10 00:00:00, 2017-11-11 00:00:00)
             forecast_period: 68400 seconds
        Attributes:
             institution: Met Office
diff --git a/improver/calibration/dataframe_utilities.py b/improver/calibration/dataframe_utilities.py
index 9585b97d1c..2c520dff5c 100644
--- a/improver/calibration/dataframe_utilities.py
+++ b/improver/calibration/dataframe_utilities.py
@@ -326,17 +326,15 @@ def _prepare_dataframes(
         keep="last",
     )
     # Sort to ensure a consistent ordering after removing duplicates.
-    forecast_df.sort_values(
-        by=["blend_time", "percentile", "wmo_id"], inplace=True, ignore_index=True,
+    forecast_df = forecast_df.sort_values(
+        by=["blend_time", "percentile", "wmo_id"], ignore_index=True,
     )
 
     # Remove truth duplicates.
     truth_cols = ["diagnostic", "time", "wmo_id"]
     truth_df = truth_df.drop_duplicates(subset=truth_cols, keep="last",)
     # Sort to ensure a consistent ordering after removing duplicates.
-    truth_df.sort_values(
-        by=truth_cols, inplace=True, ignore_index=True,
-    )
+    truth_df = truth_df.sort_values(by=truth_cols, ignore_index=True)
 
     # Find the common set of WMO IDs.
     common_wmo_ids = sorted(
diff --git a/improver/calibration/ensemble_calibration.py b/improver/calibration/ensemble_calibration.py
index 994a3f1939..f950700651 100644
--- a/improver/calibration/ensemble_calibration.py
+++ b/improver/calibration/ensemble_calibration.py
@@ -1130,7 +1130,7 @@ def mask_cube(cube: Cube, landsea_mask: Cube) -> None:
             IndexError: if the cube and landsea_mask shapes are not compatible.
         """
         try:
-            cube.data[..., ~landsea_mask.data.astype(np.bool)] = np.nan
+            cube.data[..., ~landsea_mask.data.astype(bool)] = np.nan
         except IndexError as err:
             msg = "Cube and landsea_mask shapes are not compatible. {}".format(err)
             raise IndexError(msg)
@@ -1380,7 +1380,6 @@ def process(
             forecast_var,
             number_of_realizations,
         )
-
         return coefficients_cubelist
 
 
diff --git a/improver/calibration/reliability_calibration.py b/improver/calibration/reliability_calibration.py
index b25b23588b..a2dd9f389b 100644
--- a/improver/calibration/reliability_calibration.py
+++ b/improver/calibration/reliability_calibration.py
@@ -233,10 +233,11 @@ def _create_reliability_table_cube(
     ) -> Cube:
         """
         Construct a reliability table cube and populate it with the provided
-        data. The returned cube will include a cycle hour coordinate, which
-        describes the model cycle hour at which the forecast data was produced.
-        It will further include the forecast period, threshold coordinate,
-        and spatial coordinates from the forecast cube.
+        data. The returned cube will include a forecast_reference_time
+        coordinate, which will be the maximum range of bounds of the input
+        forecast reference times, with the point value set to the latest
+        of those in the inputs. It will further include the forecast period,
+        threshold coordinate, and spatial coordinates from the forecast cube.
 
         Args:
             forecast:
@@ -443,11 +444,11 @@ def process(self, historic_forecasts: Cube, truths: Cube) -> Cube:
         whether the data is thresholded below or above a given diagnostic
         threshold.
 
+        `historic_forecasts` and `truths` should have matching validity times.
+
         Args:
             historic_forecasts:
                 A cube containing the historical forecasts used in calibration.
-                These are expected to all have a consistent cycle hour, that is
-                the hour in the forecast reference time.
             truths:
                 A cube containing the thresholded gridded truths used in
                 calibration.
diff --git a/improver/cli/wxcode.py b/improver/cli/wxcode.py
index 22baa7beb5..0e57214e19 100755
--- a/improver/cli/wxcode.py
+++ b/improver/cli/wxcode.py
@@ -63,11 +63,11 @@ def process(
             the decision tree. It will only be used if the decision tree
             provided has threshold values defined with an associated period.
         check_tree (bool):
-            If set the decision tree will be checked to see if it conforms to
-            the expected format; the only other argument required is the path
-            to the decision tree. If the tree is found to be valid the required
-            inputs will be listed. Setting this flag will prevent the CLI
-            performing any other actions.
+            If set, the decision tree will be checked to see if it conforms to
+            the expected format and that all nodes can be reached; the only other
+            argument required is the path to the decision tree. If the tree is found
+            to be valid the required inputs will be listed. Setting this flag will
+            prevent the CLI performing any other actions.
 
     Returns:
         iris.cube.Cube:
diff --git a/improver/cli/wxcode_modal.py b/improver/cli/wxcode_modal.py
index 206263495c..85e67cc1ba 100644
--- a/improver/cli/wxcode_modal.py
+++ b/improver/cli/wxcode_modal.py
@@ -36,7 +36,7 @@
 
 @cli.clizefy
 @cli.with_output
-def process(*cubes: cli.inputcube):
+def process(*cubes: cli.inputcube, model_id_attr: str = None):
     """Generates a modal weather symbol for the period covered by the input
     weather symbol cubes. Where there are different weather codes available
     for night and day, the modal code returned is always a day code, regardless
@@ -46,6 +46,10 @@ def process(*cubes: cli.inputcube):
         cubes (iris.cube.CubeList):
             A cubelist containing weather symbols cubes that cover the period
             over which a modal symbol is desired.
+        model_id_attr (str):
+            Name of attribute recording source models that should be
+            inherited by the output cube. The source models are expected as
+            a space-separated string.
 
     Returns:
         iris.cube.Cube:
@@ -56,4 +60,4 @@ def process(*cubes: cli.inputcube):
     if not cubes:
         raise RuntimeError("Not enough input arguments. See help for more information.")
 
-    return ModalWeatherCode()(cubes)
+    return ModalWeatherCode(model_id_attr=model_id_attr)(cubes)
diff --git a/improver/synthetic_data/set_up_test_cubes.py b/improver/synthetic_data/set_up_test_cubes.py
index c416bf02ce..dbb253f00a 100644
--- a/improver/synthetic_data/set_up_test_cubes.py
+++ b/improver/synthetic_data/set_up_test_cubes.py
@@ -260,7 +260,7 @@ def _create_dimension_coord(
 
         coord_array = np.array(coord_array)
 
-        if issubclass(coord_array.dtype.type, np.float):
+        if issubclass(coord_array.dtype.type, float):
             # option needed for realizations percentile & probability cube setup
             # and heights coordinate
             coord_array = coord_array.astype(np.float32)
diff --git a/improver/wxcode/modal_code.py b/improver/wxcode/modal_code.py
index 25dc25d16f..024352cf20 100644
--- a/improver/wxcode/modal_code.py
+++ b/improver/wxcode/modal_code.py
@@ -73,10 +73,20 @@ class ModalWeatherCode(BasePlugin):
     covered by the input files.
     """
 
-    def __init__(self):
-        """Create an aggregator instance for reuse"""
+    def __init__(self, model_id_attr: str = None):
+        """
+        Set up plugin and create an aggregator instance for reuse
+
+        Args:
+            model_id_attr:
+                Name of attribute recording source models that should be
+                inherited by the output cube. The source models are expected as
+                a space-separated string.
+        """
         self.aggregator_instance = Aggregator("mode", self.mode_aggregator)
 
+        self.model_id_attr = model_id_attr
+
         # Create the expected cell method for use with single cube inputs
         # that do not pass through the aggregator.
         self.mode_cell_method = iris.coords.CellMethod("mode", coords="time")
@@ -205,6 +215,16 @@ def process(self, cubes: CubeList) -> Cube:
             result = cube.collapsed("time", self.aggregator_instance)
         self._set_blended_times(result)
 
+        if self.model_id_attr:
+            # Update contributing models
+            contributing_models = set()
+            for source_cube in cubes:
+                for model in source_cube.attributes[self.model_id_attr].split(" "):
+                    contributing_models.update([model])
+            result.attributes[self.model_id_attr] = " ".join(
+                sorted(list(contributing_models))
+            )
+
         # Handle any unset points where it was hard to determine a suitable mode
         if (result.data == UNSET_CODE_INDICATOR).any():
             self._group_codes(result, cube)
diff --git a/improver/wxcode/utilities.py b/improver/wxcode/utilities.py
index 6968c72a5a..b98d7c4085 100644
--- a/improver/wxcode/utilities.py
+++ b/improver/wxcode/utilities.py
@@ -34,6 +34,7 @@
 from typing import Any, Dict, List, Optional
 
 import iris
+import numpy as np
 from iris.cube import Cube
 
 REQUIRED_KEY_WORDS = [
@@ -393,6 +394,10 @@ def check_tree(
         raise ValueError("Decision tree is not a dictionary")
 
     issues = []
+    start_node = list(wxtree.keys())[0]
+    all_targets = np.array(
+        [(n["if_true"], n["if_false"]) for n in wxtree.values()]
+    ).flatten()
     wxtree = update_tree_thresholds(wxtree, target_period)
     valid_codes = list(WX_DICT.keys())
 
@@ -403,6 +408,10 @@ def check_tree(
             if entry not in all_key_words:
                 issues.append(f"Node {node} contains unknown key '{entry}'")
 
+        # Check that this node is reachable, or is the start_node
+        if not ((node == start_node) or node in all_targets):
+            issues.append(f"Unreachable node '{node}'")
+
         # Check that if_diagnostic_missing key points at a if_true or if_false
         # node
         if "if_diagnostic_missing" in items:
diff --git a/improver_tests/acceptance/SHA256SUMS b/improver_tests/acceptance/SHA256SUMS
index 8bf0f11b93..29fe4596a9 100644
--- a/improver_tests/acceptance/SHA256SUMS
+++ b/improver_tests/acceptance/SHA256SUMS
@@ -94,10 +94,6 @@ b5ca2030a23ba6440c712a659f6f4f3dcbe8566afeef1a68e2e5a6a9b6e7484e  ./combine/broa
 88b1b0d76a4d1d90a59766d8ad166a9e001556f951201022976ab3fa4e45c1c8  ./combine/cellmethods/kgo.nc
 7e4e3da97b7725de34d1e56fbdcb837cc2fc50eaddb29e3e8452ab72aff8e56b  ./combine/cellmethods/precipitation_accumulation-PT01H.nc
 45893b7a7c10936f33cac4899a01c6f2c5ee2b5abe20fa400144558d3b9a15b3  ./combine/cellmethods/precipitation_is_snow.nc
-a4b991d8e0fa174cec415efcf936b38e67fb936c57f7a9e2794beb6629b9b824  ./combine/minimum_realizations/20220128T1900Z-PT0010H00M-temperature_at_screen_level_max-PT01H.nc
-4bca51d7208294112240b10e3a36692491472dd3f7ebb072ca030e76a495f14f  ./combine/minimum_realizations/20220128T2000Z-PT0011H00M-temperature_at_screen_level_max-PT01H.nc
-41daf1da910869fbd9338e31a530aaa68a221828d6e6b8cdbb4302a8e65fc223  ./combine/minimum_realizations/20220128T2100Z-PT0012H00M-temperature_at_screen_level_max-PT01H.nc
-b60f6046c86319f8b7ca3b5d7902dbaf3a52f571f30ba56a1a4bc814c42dd341  ./combine/minimum_realizations/kgo.nc
 0bd96af6cb5c6caa045e397589dd0ce3b498af837d989fe73326f5e9459c6054  ./construct-reliability-tables/basic/forecast_0.nc
 fbc14286b4ce41e2e60df0870ae4911c1b00a38ec96912f43c6187fcaf7d02f6  ./construct-reliability-tables/basic/forecast_1.nc
 902e5cb9d3dc5d2b78bb99aff8370f9815adf5064b2caeb7abed73a56a897a43  ./construct-reliability-tables/basic/kgo_single_value_bins.nc
@@ -637,73 +633,73 @@ e653ae271dd789dde8b03e6237cc7b48552dd9d5206d3430ee68d283683247a0  ./wind_downsca
 de5cee66ebf03a6dc6fe4350fb8fedd782adc0bb5da72726a4098c3b62422130  ./wind_downscaling/with_realization/kgo.nc
 e50cf0c7e23b12317412e34ea45f959532beb6ba4247e075be08c2a6732e8cd8  ./wind_downscaling/with_realization/sigma.nc
 933e1b15c22d3bf302c57dff3fc92c33d45d056b9b6eaa6d677e870537a42e7c  ./wind_downscaling/with_realization/standard_orog.nc
-97de7945072ae9a2845424904e925aaa1e722fac0c959bb3e534392d4e4a7795  ./wxcode-modal/blend_mismatch_inputs/20201209T0700Z-weather_symbols-PT01H.nc
-f76ea9210c563997354ee5e5d3d819192a9d7bb329374839cdebe02d464643d6  ./wxcode-modal/blend_mismatch_inputs/20201209T0800Z-weather_symbols-PT01H.nc
-b5f4fc01b1f03811505d4a64cd4e3aa49d94f7a0cb6882b22a5cfd826f82be0d  ./wxcode-modal/blend_mismatch_inputs/20201209T0900Z-weather_symbols-PT01H.nc
-80bc644d22207485bad81b2ee6d3abab8327ccde982c0771d05e9887d6b9ff7d  ./wxcode-modal/blend_mismatch_inputs/20201209T1000Z-weather_symbols-PT01H.nc
-cc20c480460357559bbe9151ad069e97535df17a0687ae9e0fa1f5ef3cf9f9a6  ./wxcode-modal/blend_mismatch_inputs/20201209T1100Z-weather_symbols-PT01H.nc
-a4576afe3c045c00718692164a89bc0acb70191f70f34bcd300dfa899b9c6f15  ./wxcode-modal/blend_mismatch_inputs/20201209T1200Z-weather_symbols-PT01H.nc
-b479b8d6e9b002e716575a8a9051a70891f4cc8862af8ee80b53ecb05d37e8a3  ./wxcode-modal/blend_mismatch_inputs/20201209T1300Z-weather_symbols-PT01H.nc
-2b1241b79c97e991ed06c34e6109e85dc1517726d2d375f104a7ac85d939d0a2  ./wxcode-modal/blend_mismatch_inputs/20201209T1400Z-weather_symbols-PT01H.nc
-e42830916b5bf8b3f97ace0e54dec8e80be3cf93d9faf420274bab1cfb1772e4  ./wxcode-modal/blend_mismatch_inputs/20201209T1500Z-weather_symbols-PT01H.nc
-b4bba902e61db128924d33f145e73aecb6cdf27dc71db6d66d17c9dcfcc498b5  ./wxcode-modal/blend_mismatch_inputs/20201209T1600Z-weather_symbols-PT01H.nc
-09da6234ded5f1572183b8e297aa8f633fc20fe3571b5fc3d22b3a8c95c80a8a  ./wxcode-modal/blend_mismatch_inputs/20201209T1700Z-weather_symbols-PT01H.nc
-56a8d8f763072027c4a4819e0cab298ea0b820cf32903ae010132fa0a72a2eb9  ./wxcode-modal/blend_mismatch_inputs/20201209T1800Z-weather_symbols-PT01H.nc
-d081b4d643e2e7734a6a2084efd69a2a0a01ce90f8c234190729a4ceeba53c2b  ./wxcode-modal/blend_mismatch_inputs/kgo.nc
-c2d9ae0d65cd3da79a7484e8db57b351159d68ab66c3024294b66a95f166f957  ./wxcode-modal/gridded_input/20201209T0700Z-weather_symbols-PT01H.nc
-61a2e23ae036a1dc91ce3acc4a8eb105405507718e19a0c07696a47017cd7d75  ./wxcode-modal/gridded_input/20201209T0800Z-weather_symbols-PT01H.nc
-181011db593ba39126b67a0166f28844658fc4f9ab9479dd7bdc507f8222f3a5  ./wxcode-modal/gridded_input/20201209T0900Z-weather_symbols-PT01H.nc
-fd430134e0fefe8e20a23cee7e301ef99526336c4864ac89d26494400fbecfe8  ./wxcode-modal/gridded_input/20201209T1000Z-weather_symbols-PT01H.nc
-7ab5b13f76af59c38b5b725b8f7eee3e3dcd66c07df03c7c9540f7ff8d8a1a1c  ./wxcode-modal/gridded_input/20201209T1100Z-weather_symbols-PT01H.nc
-f71ebe242cc3f677e2418f04b67c88ce6f152ad31315f066329735512b9fee3d  ./wxcode-modal/gridded_input/20201209T1200Z-weather_symbols-PT01H.nc
-3845f7fa94d0b595d03438a5f6e36a6f2bcd94b313a50135ae475924f6c2b5cd  ./wxcode-modal/gridded_input/20201209T1300Z-weather_symbols-PT01H.nc
-9370226d29ba38f069c9e6ac44a85fb6969db0e24b99e6a55675a509677d55e1  ./wxcode-modal/gridded_input/20201209T1400Z-weather_symbols-PT01H.nc
-9e91a6231b1ded5bfd6781a4dae87927981657d65d103cff7406d43663928261  ./wxcode-modal/gridded_input/20201209T1500Z-weather_symbols-PT01H.nc
-9e84306e6cb7f0bed95c7cc02a6905e8c3389d778acbd14c5d9a4f0ce28d0b44  ./wxcode-modal/gridded_input/20201209T1600Z-weather_symbols-PT01H.nc
-11847c4063f937ddb9a6612521228ee14f2658c7ca38a56ce2f26c799caedf85  ./wxcode-modal/gridded_input/20201209T1700Z-weather_symbols-PT01H.nc
-e053c2d1a27f708a0bc24c52d2a069e2cf57e499014a7a905e72652ccdebf26b  ./wxcode-modal/gridded_input/20201209T1800Z-weather_symbols-PT01H.nc
-af44bfd1d30dbe0cd673f15a4e0d0d51473ba320365d6cfe9502c23c9d81dfbb  ./wxcode-modal/gridded_input/kgo.nc
-6da2c57289d7a5577b4b68f5def5364b35f600f20e823d6bf8803c266e670914  ./wxcode-modal/gridded_ties/20201209T0700Z-weather_symbols-PT01H.nc
-c42d8c691b3cf06f12a8bb007bb68a2bf4d2e2d0b195938e852a3e269a1298e1  ./wxcode-modal/gridded_ties/20201209T0800Z-weather_symbols-PT01H.nc
-e471cd8a242598db8350e73dedd8f144966b38fd8eb2cd193048e26de5b9b973  ./wxcode-modal/gridded_ties/20201209T0900Z-weather_symbols-PT01H.nc
-0dc5ebf2212b3ac28dddefcf5d95ab85a93cf91d93604ce40ed67156c03ebfda  ./wxcode-modal/gridded_ties/20201209T1000Z-weather_symbols-PT01H.nc
-f6a8a6897507309a1f350f73fb5ba72f2d926f477f7b31026db7e740f36ddbc1  ./wxcode-modal/gridded_ties/20201209T1100Z-weather_symbols-PT01H.nc
-ec0f5e8f13facb0f107fa3112e14c46ff34d4ac7531b6c36f9b186fc1e38d3a7  ./wxcode-modal/gridded_ties/20201209T1200Z-weather_symbols-PT01H.nc
-9f930cd3fcd02769bc686994e9728d2e47eb7229704377d924391ac45c2321d0  ./wxcode-modal/gridded_ties/20201209T1300Z-weather_symbols-PT01H.nc
-8e0af1a80bd199c6c8bfdf011a435203474d7a3f7778b5373b02a6d73a8761ff  ./wxcode-modal/gridded_ties/20201209T1400Z-weather_symbols-PT01H.nc
-a47daaf93e3fb3ac81b196a1e549b5870c3f21f43ad42e077567aaf0031cde43  ./wxcode-modal/gridded_ties/20201209T1500Z-weather_symbols-PT01H.nc
-fa3ec96946235d6655cd82c1f687013b1b02596bde0629aa5081223c26f3c324  ./wxcode-modal/gridded_ties/20201209T1600Z-weather_symbols-PT01H.nc
-a316682d14c5d45344ad3863dbb29ca9f414aa009536c602759a2c45eb5576bf  ./wxcode-modal/gridded_ties/20201209T1700Z-weather_symbols-PT01H.nc
-f61521064c98081d182630bae2ecc36150f6c53bbb6279725b4ebade326c3af7  ./wxcode-modal/gridded_ties/20201209T1800Z-weather_symbols-PT01H.nc
-c28ef3583fc70dc5fa7ccaa4edc832da8467ac8afe170d7340b50aab43087fc4  ./wxcode-modal/gridded_ties/kgo.nc
-7ba7e060ed581804c956ac2e3e66a41c625ba100541dc63256208cc801f6ea63  ./wxcode-modal/single_input/20201210T0000Z-weather_symbols-PT01H.nc
-bb4a38f1b0f93ae6920fd00c6683c8258fe5b2edebb23195771a4cb7358e742d  ./wxcode-modal/single_input/kgo.nc
-97de7945072ae9a2845424904e925aaa1e722fac0c959bb3e534392d4e4a7795  ./wxcode-modal/spot_input/20201209T0700Z-weather_symbols-PT01H.nc
-f76ea9210c563997354ee5e5d3d819192a9d7bb329374839cdebe02d464643d6  ./wxcode-modal/spot_input/20201209T0800Z-weather_symbols-PT01H.nc
-b5f4fc01b1f03811505d4a64cd4e3aa49d94f7a0cb6882b22a5cfd826f82be0d  ./wxcode-modal/spot_input/20201209T0900Z-weather_symbols-PT01H.nc
-80bc644d22207485bad81b2ee6d3abab8327ccde982c0771d05e9887d6b9ff7d  ./wxcode-modal/spot_input/20201209T1000Z-weather_symbols-PT01H.nc
-cc20c480460357559bbe9151ad069e97535df17a0687ae9e0fa1f5ef3cf9f9a6  ./wxcode-modal/spot_input/20201209T1100Z-weather_symbols-PT01H.nc
-950bee63559cf13b6af9f4b56d8d7a628a6afc77353345d44ea6798015c2148f  ./wxcode-modal/spot_input/20201209T1200Z-weather_symbols-PT01H.nc
-b479b8d6e9b002e716575a8a9051a70891f4cc8862af8ee80b53ecb05d37e8a3  ./wxcode-modal/spot_input/20201209T1300Z-weather_symbols-PT01H.nc
-2b1241b79c97e991ed06c34e6109e85dc1517726d2d375f104a7ac85d939d0a2  ./wxcode-modal/spot_input/20201209T1400Z-weather_symbols-PT01H.nc
-e42830916b5bf8b3f97ace0e54dec8e80be3cf93d9faf420274bab1cfb1772e4  ./wxcode-modal/spot_input/20201209T1500Z-weather_symbols-PT01H.nc
-b4bba902e61db128924d33f145e73aecb6cdf27dc71db6d66d17c9dcfcc498b5  ./wxcode-modal/spot_input/20201209T1600Z-weather_symbols-PT01H.nc
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+bc1022649e1b0ace39d9da1f3fbca03be855a6ec785e4487b584139e92d7fb59  ./wxcode-modal/gridded_input/20201209T1400Z-weather_symbols-PT01H.nc
+724a98e9d311546af88b68796b4ba40968183b06ffd4688a0a24e1b6299864b1  ./wxcode-modal/gridded_input/20201209T1500Z-weather_symbols-PT01H.nc
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+8d150f86a12e4b914e7f2ee269e643022db664d26c2539927d83f4424706cb3e  ./wxcode-modal/gridded_input/20201209T1800Z-weather_symbols-PT01H.nc
+a37d508c6c356325b1c31c245726aa5459484c4fb34fb2fbf309b10265d24712  ./wxcode-modal/gridded_input/kgo.nc
+94a8758ca265acf625a21f4f9c6a246181b7b51e35710f312675d22b394f4d54  ./wxcode-modal/gridded_ties/20201209T0700Z-weather_symbols-PT01H.nc
+0dbf6702561e52bcd97a86ef3d954e353a5218b78a1376792144eeb0a673a0c8  ./wxcode-modal/gridded_ties/20201209T0800Z-weather_symbols-PT01H.nc
+08881255e7b71679aa1eda6e7bec8b85ceba5adc3547ae169e192abfc3238c39  ./wxcode-modal/gridded_ties/20201209T0900Z-weather_symbols-PT01H.nc
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+ed07ba61392f465d5890a06c6ad3cee9d9adf31ab03ed365e1d9771d005d646c  ./wxcode-modal/gridded_ties/20201209T1100Z-weather_symbols-PT01H.nc
+350f0754f7bb247a0a81bb81d05f3e69da4d8d768d8269c82f6fef73713b5b06  ./wxcode-modal/gridded_ties/20201209T1200Z-weather_symbols-PT01H.nc
+e8dc643ec6446d495a96d4dda6104b958d08137c15c3e832bb397a50229062eb  ./wxcode-modal/gridded_ties/20201209T1300Z-weather_symbols-PT01H.nc
+77375be1e8258f0b641ea6475c7739beec64252dc66ae4216a5cc02a3f01cd6e  ./wxcode-modal/gridded_ties/20201209T1400Z-weather_symbols-PT01H.nc
+148cdbc9282a8fb44e4793144852757ee36c5cd63d6efebfcb63337186e96a2f  ./wxcode-modal/gridded_ties/20201209T1500Z-weather_symbols-PT01H.nc
+44c510792f566329597239006b43ea736b5faa1a884202fd523aba31ebc4f445  ./wxcode-modal/gridded_ties/20201209T1600Z-weather_symbols-PT01H.nc
+182211dba9b8d18c2d48dce89f2871854ae9baa9457bc9cfad16c1cea80faf09  ./wxcode-modal/gridded_ties/20201209T1700Z-weather_symbols-PT01H.nc
+2ed494728e73e8f740680ae090504770ee6b60131d543698c640db5ed4e08598  ./wxcode-modal/gridded_ties/20201209T1800Z-weather_symbols-PT01H.nc
+fab89f125fe7b016a35fcbf52cd9b713cfa28c7009b90150f289f47d7bea6b12  ./wxcode-modal/gridded_ties/kgo.nc
+9fbcfc0e0c3f6af0d07690c6be12e2f641123168af0c7e165b8e0ec6a2ca6f98  ./wxcode-modal/single_input/20201210T0000Z-weather_symbols-PT01H.nc
+4e52a42de445064a987da6f93b7e888c0a8ca974063a4d9cf9818a57d9b60367  ./wxcode-modal/single_input/kgo.nc
+6513b722e1fdca6ad68acd768f3506ec814f78e9ccd4799972ec00a2316ef7cf  ./wxcode-modal/spot_input/20201209T0700Z-weather_symbols-PT01H.nc
+d4c5d626ad2e5a805f90f2902f6da5d33651eb6fdcb1e16116e2290d0c608bf6  ./wxcode-modal/spot_input/20201209T0800Z-weather_symbols-PT01H.nc
+4642fc43faf4bdde6a678578868fdebb22163064fe6ef0eef41f2f39a959ca89  ./wxcode-modal/spot_input/20201209T0900Z-weather_symbols-PT01H.nc
+ce6a854694aecd515063c91774eeb6160f39c9dbf5e970f72f960a3527e157e9  ./wxcode-modal/spot_input/20201209T1000Z-weather_symbols-PT01H.nc
+300c38e7f522ff1e9257708b183148f3441bb1a861c15eaff2a5f1713bbbc872  ./wxcode-modal/spot_input/20201209T1100Z-weather_symbols-PT01H.nc
+48ef16ad1fd0b499f1d38f585ddb0a4299c39e7d7df625ad0fa6c3dc24e0f094  ./wxcode-modal/spot_input/20201209T1200Z-weather_symbols-PT01H.nc
+5ab6b249ec615ddab9995629aaddbd85bba352ad52b7bcb0be5b3037d75c22ca  ./wxcode-modal/spot_input/20201209T1300Z-weather_symbols-PT01H.nc
+340205e12625ff9466c1afba8c007343aa40e21b837ab2f2143fa032e6c47bd3  ./wxcode-modal/spot_input/20201209T1400Z-weather_symbols-PT01H.nc
+776739d288b6e43b3d9341ba301feead160986d2540514f0663f243ca003e55a  ./wxcode-modal/spot_input/20201209T1500Z-weather_symbols-PT01H.nc
+4655358c22abdd0acfd5f4e08f156a7f4f629a1764dd8314f28f121964725078  ./wxcode-modal/spot_input/20201209T1600Z-weather_symbols-PT01H.nc
+28ee3f9d419bb9766cc68e1c74fa9e8e5010dc63a600635a54356a33795f6644  ./wxcode-modal/spot_input/20201209T1700Z-weather_symbols-PT01H.nc
+073fd0175e35afcf7f9fe61fedfa63bbd9ea5299f68d8b0287bc30085339b942  ./wxcode-modal/spot_input/20201209T1800Z-weather_symbols-PT01H.nc
+a6b3f48f8c2003c2d84da0a4cb8634bed24f0d54594d6cc9235c470858f49c19  ./wxcode-modal/spot_input/kgo.nc
+db1612786b08a929ad096f13ca603f37cfd78de681e1306d909654f345f9f409  ./wxcode-modal/spot_ties/20201209T0700Z-weather_symbols-PT01H.nc
+b5beedd2fa6c58ecaaa51358b8b7abadddc1ad072af2d5141bbf0019b458e84e  ./wxcode-modal/spot_ties/20201209T0800Z-weather_symbols-PT01H.nc
+c92e6cb002b8ff8f185c097a55ff214793c7595667c8a7ba4afc1ae0f456c13e  ./wxcode-modal/spot_ties/20201209T0900Z-weather_symbols-PT01H.nc
+fd216b5dc3837202f3293f22e664d4fa623f7a349abda9ee8aef3fb19df3f660  ./wxcode-modal/spot_ties/20201209T1000Z-weather_symbols-PT01H.nc
+fbff18f7ef5c2e3d462cbc0d78de30aca7534a37f427472011f6fa1835ec8020  ./wxcode-modal/spot_ties/20201209T1100Z-weather_symbols-PT01H.nc
+129beabb8e059e46d3f00be1c5a5b8c1cdd44c4f1f08fc86a4249d4a0f854272  ./wxcode-modal/spot_ties/20201209T1200Z-weather_symbols-PT01H.nc
+063e6f151af8a05f4c2276995384fc4058571144a851953752653eff7886130c  ./wxcode-modal/spot_ties/20201209T1300Z-weather_symbols-PT01H.nc
+4d23d6d1f8861feeb288ec7b68e57e893fa2ffd633de1b25ab4d4bc4aa3316cc  ./wxcode-modal/spot_ties/20201209T1400Z-weather_symbols-PT01H.nc
+71aac305fce32fb389ea63822cd88511137cf1260c1c0a55e28c1a5fcbc91cab  ./wxcode-modal/spot_ties/20201209T1500Z-weather_symbols-PT01H.nc
+6fe4b1563f0fbb4579dba46b12654f983cd6486f39986d52231aad5d18acf508  ./wxcode-modal/spot_ties/20201209T1600Z-weather_symbols-PT01H.nc
+d506afe1572ded010805662f057926ba19367a8bf0c9f700380b6b63ab440399  ./wxcode-modal/spot_ties/20201209T1700Z-weather_symbols-PT01H.nc
+1c0c3b4fcc95ea2d5c9658ebd42cbee8a2043afa607843e0e9013d273f7fc460  ./wxcode-modal/spot_ties/20201209T1800Z-weather_symbols-PT01H.nc
+cb558fc0cb3d1f0535e2402fc293982a122a08380df7d5594cacb9cf032acc5a  ./wxcode-modal/spot_ties/kgo.nc
 803b2a645f8a33db4601a45dc16325f8a90f13fdea430b98ea94f032ee032180  ./wxcode/bad_wx_decision_tree.json
 5a3a808f0cc7952e6cc4225d09136276f685c5fdd6798a795c0430aa65ca71fa  ./wxcode/basic/kgo.nc
 3b86d135373b44989c22bdfbc6cb0798e974ff1d77c4c6b6a83fce650863e59c  ./wxcode/basic/kgo_no_lightning.nc
@@ -742,5 +738,4 @@ f20a9eabe867ea00e9570eb1177c01841b7606e2969287432d4a50ae888062a3  ./wxcode/nativ
 ec818f319f2c137ab356f97a3980788357254f17c0761908b4e656170c0e5d37  ./wxcode/native_units/probability_of_shower_condition_above_threshold.nc
 1b8de3a02ac7dcf18ec9208a5e0b583b37c420093781b861b67eb5c8eaa37cd9  ./wxcode/native_units/probability_of_thickness_of_rainfall_amount_above_threshold.nc
 554f9ed54f1092f31013b0e85de20e13932ca9533d50ac6316f170641901a209  ./wxcode/native_units/probability_of_visibility_in_air_below_threshold.nc
-3de7bd3e25d9d5b9eafbef91ead03f1ebfa6202b9f90826dbe81ad99fdc74277  ./wxcode/wx_decision_tree_1h.json
-80a0dc027f33a9f209d8304a92eb60fd204620492bea5bb9c0fe5c4d8a285edb  ./wxcode/wx_decision_tree_3h.json
+be969533132f0d1651959ef9db777b2c71b109673b75eb59edb0e87e78698a9f  ./wxcode/wx_decision_tree.json
diff --git a/improver_tests/acceptance/acceptance.py b/improver_tests/acceptance/acceptance.py
index b133f14a2d..df102691eb 100644
--- a/improver_tests/acceptance/acceptance.py
+++ b/improver_tests/acceptance/acceptance.py
@@ -69,7 +69,7 @@ def run_cli(cli_name, verbose=True):
     def run_function(args):
         if not checksum_ignore():
             verify_checksums(args)
-        cli.main("improver", cli_name, *args, verbose=verbose)
+        return cli.main("improver", cli_name, *args, verbose=verbose)
 
     return run_function
 
diff --git a/improver_tests/acceptance/test_wxcode.py b/improver_tests/acceptance/test_wxcode.py
index c423547c7a..86236d879b 100644
--- a/improver_tests/acceptance/test_wxcode.py
+++ b/improver_tests/acceptance/test_wxcode.py
@@ -29,6 +29,7 @@
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 # POSSIBILITY OF SUCH DAMAGE.
 """Tests for the wxcode CLI"""
+import re
 
 import pytest
 
@@ -187,3 +188,24 @@ def test_no_lightning(tmp_path):
     ]
     run_cli(args)
     acc.compare(output_path, kgo_path)
+
+
+@pytest.mark.parametrize(
+    "wxtree,expected",
+    (
+        ("wx_decision_tree.json", "Decision tree OK\nRequired inputs are:"),
+        ("bad_wx_decision_tree.json", "Unreachable node 'unreachable'"),
+    ),
+)
+def test_trees(wxtree, expected):
+    """Test the check-tree option"""
+    kgo_dir = acc.kgo_root() / "wxcode"
+    args = [
+        "--wxtree",
+        kgo_dir / wxtree,
+        "--check-tree",
+        "--target-period",
+        "3600",
+    ]
+    result = run_cli(args)
+    assert re.match(expected, result)
diff --git a/improver_tests/acceptance/test_wxcode_modal.py b/improver_tests/acceptance/test_wxcode_modal.py
index 4c49d7668d..21320df4ac 100644
--- a/improver_tests/acceptance/test_wxcode_modal.py
+++ b/improver_tests/acceptance/test_wxcode_modal.py
@@ -68,6 +68,8 @@ def test_expected(tmp_path, test_path):
     output_path = tmp_path / "output.nc"
     args = [
         *input_paths,
+        "--model-id-attr",
+        "mosg__model_configuration",
         "--output",
         output_path,
     ]
diff --git a/improver_tests/calibration/dataframe_utilities/test_dataframe_utilities.py b/improver_tests/calibration/dataframe_utilities/test_dataframe_utilities.py
index 0bebcfee88..2ebad25b37 100644
--- a/improver_tests/calibration/dataframe_utilities/test_dataframe_utilities.py
+++ b/improver_tests/calibration/dataframe_utilities/test_dataframe_utilities.py
@@ -474,9 +474,9 @@ def test_site_coord_mismatch(self):
         and forecasts. In this case, the position (lat/lon/alt) from the
         forecast will be used."""
         df = self.truth_subset_df.copy()
-        df.at[::3, "altitude"] = 45
-        df.at[::3, "latitude"] = 52
-        df.at[::3, "longitude"] = -12
+        df.loc[::3, "altitude"] = 45
+        df.loc[::3, "latitude"] = 52
+        df.loc[::3, "longitude"] = -12
         result = forecast_and_truth_dataframes_to_cubes(
             self.forecast_df,
             df,
diff --git a/improver_tests/wxcode/wxcode/test_ModalCode.py b/improver_tests/wxcode/wxcode/test_ModalCode.py
index 7001a1898c..17b5230c73 100644
--- a/improver_tests/wxcode/wxcode/test_ModalCode.py
+++ b/improver_tests/wxcode/wxcode/test_ModalCode.py
@@ -33,10 +33,11 @@
 from calendar import timegm
 from datetime import datetime as dt
 from datetime import timedelta
+from typing import Tuple
 
 import numpy as np
 import pytest
-from iris.cube import Cube
+from iris.cube import CubeList
 
 from improver.spotdata.build_spotdata_cube import build_spotdata_cube
 from improver.synthetic_data.set_up_test_cubes import construct_scalar_time_coords
@@ -44,11 +45,14 @@
 
 from . import set_up_wxcube
 
+MODEL_ID_ATTR = "mosg__model_configuration"
 TARGET_TIME = dt(2020, 6, 15, 18)
 
 
 @pytest.fixture(name="wxcode_series")
-def wxcode_series_fixture(data, cube_type, offset_reference_times: bool) -> Cube:
+def wxcode_series_fixture(
+    data, cube_type, offset_reference_times: bool, model_id_attr: bool,
+) -> Tuple[bool, CubeList]:
     """Generate a time series of weather code cubes for combination to create
     a period representative code. When offset_reference_times is set, each
     successive cube will have a reference time one hour older."""
@@ -56,7 +60,7 @@ def wxcode_series_fixture(data, cube_type, offset_reference_times: bool) -> Cube
     time = TARGET_TIME
 
     ntimes = len(data)
-    wxcubes = []
+    wxcubes = CubeList()
 
     for i in range(ntimes):
         wxtime = time - timedelta(hours=i)
@@ -93,9 +97,13 @@ def wxcode_series_fixture(data, cube_type, offset_reference_times: bool) -> Cube
                     scalar_coords=time_coords,
                 )
             )
-    return wxcubes
+        if model_id_attr:
+            [c.attributes.update({MODEL_ID_ATTR: "uk_ens"}) for c in wxcubes]
+            wxcubes[0].attributes.update({MODEL_ID_ATTR: "uk_det uk_ens"})
+    return model_id_attr, wxcubes
 
 
+@pytest.mark.parametrize("model_id_attr", [False, True])
 @pytest.mark.parametrize("offset_reference_times", [False, True])
 @pytest.mark.parametrize("cube_type", ["gridded", "spot"])
 @pytest.mark.parametrize(
@@ -133,13 +141,15 @@ def wxcode_series_fixture(data, cube_type, offset_reference_times: bool) -> Cube
 )
 def test_expected_values(wxcode_series, expected):
     """Test that the expected period representative symbol is returned."""
-    result = ModalWeatherCode()(wxcode_series)
+    _, wxcode_cubes = wxcode_series
+    result = ModalWeatherCode()(wxcode_cubes)
     assert result.data.flatten()[0] == expected
 
 
+@pytest.mark.parametrize("model_id_attr", [False, True])
 @pytest.mark.parametrize("offset_reference_times", [False, True])
 @pytest.mark.parametrize("cube_type", ["gridded", "spot"])
-@pytest.mark.parametrize("data", [np.ones((12)), np.ones((1))])
+@pytest.mark.parametrize("data", [np.ones(12), np.ones(1)])
 def test_metadata(wxcode_series):
     """Check that the returned metadata is correct. In this case we expect a
     time coordinate with bounds that describe the full period over which the
@@ -154,9 +164,16 @@ def test_metadata(wxcode_series):
     def as_utc_timestamp(time):
         return timegm(time.utctimetuple())
 
-    result = ModalWeatherCode()(wxcode_series)
+    model_id_attr, wxcode_cubes = wxcode_series
 
-    n_times = len(wxcode_series)
+    if model_id_attr:
+        kwargs = {"model_id_attr": MODEL_ID_ATTR}
+    else:
+        kwargs = {}
+
+    result = ModalWeatherCode(**kwargs)(wxcode_cubes)
+
+    n_times = len(wxcode_cubes)
     expected_time = TARGET_TIME
     expected_bounds = [TARGET_TIME - timedelta(hours=n_times), TARGET_TIME]
     expected_reference_time = TARGET_TIME - timedelta(hours=18)
@@ -180,3 +197,7 @@ def as_utc_timestamp(time):
     )
     assert result.cell_methods[0].method == expected_cell_method[0]
     assert result.cell_methods[0].coord_names[0] == expected_cell_method[1]
+    if model_id_attr:
+        assert result.attributes[MODEL_ID_ATTR] == "uk_det uk_ens"
+    else:
+        assert MODEL_ID_ATTR not in result.attributes.keys()
diff --git a/improver_tests/wxcode/wxcode/test_utilities.py b/improver_tests/wxcode/wxcode/test_utilities.py
index d34314c467..0f7808bc2d 100644
--- a/improver_tests/wxcode/wxcode/test_utilities.py
+++ b/improver_tests/wxcode/wxcode/test_utilities.py
@@ -511,28 +511,29 @@ def modify_tree_fixture(node, key, value):
             ),
         ),
         (
-            "lightning",
+            "lightning_shower",
             "if_true",
             100,
             (
-                "Node lightning results in an invalid weather code of 100 for the "
+                "Node lightning_shower results in an invalid weather code of 100 for the "
                 "if_true condition"
             ),
         ),
         (
-            "lightning",
+            "lightning_shower",
             "if_false",
             100,
             (
-                "Node lightning results in an invalid weather code of 100 for the "
+                "Node lightning_shower results in an invalid weather code of 100 for the "
                 "if_false condition"
             ),
         ),
         (
-            "lightning",
+            "lightning_shower",
             "if_false",
             "kittens",
-            "Node lightning has an invalid destination of kittens for the if_false condition",
+            "Node lightning_shower has an invalid destination of kittens for the if_false "
+            "condition",
         ),
         (
             "snow_in_vicinity",
@@ -590,6 +591,12 @@ def modify_tree_fixture(node, key, value):
             ["kittens"],
             "Node lightning has a non-numeric probability threshold ['kittens']",
         ),
+        (
+            "mist_conditions",
+            "if_true",
+            "no_precipitation_cloud",
+            "Unreachable node 'fog_conditions'",
+        ),
     ),
 )
 def test_check_tree(modify_tree, expected):