update values in unit tests to pass post xarray v2024.03.0 decoding c…

…hanges

tests/unit/test_cocip.py

@@ -698,6 +698,15 @@ def test_eval_persistent(cocip_persistent: Cocip, regenerate_results: bool) -> N
                 rtol=1e-2,
             )
             continue
+        if key in ["tau_cirrus", "specific_cloud_ice_water_content"]:
+            np.testing.assert_allclose(
+                cocip_persistent.source.get_data_or_attr(key),
+                flight_output[key],
+                err_msg=key,
+                rtol=rtol,
+                atol=1e-14,  # for trace cloud ice
+            )
+            continue
         np.testing.assert_allclose(
             cocip_persistent.source.get_data_or_attr(key),
             flight_output[key],
@@ -761,7 +770,7 @@ def test_eval_persistent2(cocip_persistent2: Cocip, regenerate_results: bool) ->
     contrail_output["age"] = pd.to_timedelta(contrail_output["age"])
     contrail_output["dt_integration"] = pd.to_timedelta(contrail_output["dt_integration"])
 
-    rtol = 1e-3
+    rtol = 1e-4
     for key in flight_output:
         if key in ["time", "flight_id"]:
             assert np.all(cocip_persistent2.source[key] == flight_output[key])
@@ -771,7 +780,15 @@ def test_eval_persistent2(cocip_persistent2: Cocip, regenerate_results: bool) ->
                 cocip_persistent2.source.level, flight_output[key], err_msg=key
             )
             continue
-
+        if key in ["tau_cirrus", "specific_cloud_ice_water_content"]:
+            np.testing.assert_allclose(
+                cocip_persistent2.source[key],
+                flight_output[key],
+                err_msg=key,
+                rtol=rtol,
+                atol=1e-14,  # for trace cloud ice
+            )
+            continue
         np.testing.assert_allclose(
             cocip_persistent2.source[key], flight_output[key], err_msg=key, rtol=rtol
         )
@@ -935,9 +952,9 @@ def test_cocip_contrail_contrail_overlapping(
     out = cocip.eval(fleet)
 
     if contrail_contrail_overlapping:
-        assert out["ef"].sum() == pytest.approx(621504.7e8, abs=8e7)
+        assert out["ef"].sum() == pytest.approx(621498.2e8, abs=8e7)
     else:
-        assert out["ef"].sum() == pytest.approx(621506.7e8, abs=8e7)
+        assert out["ef"].sum() == pytest.approx(621500.2e8, abs=8e7)
 
 
 # ------

tests/unit/test_humidity_scaling.py

@@ -347,7 +347,7 @@ def test_histogram_matching_reanalysis(
 
     # Check that the two methods give different results
     diff = np.abs(np.log(q1) - np.log(q0))
-    assert diff.min() >= 1e-6
+    assert diff.min() >= 5e-7
     assert diff.max() <= 2
     assert 0.02 < diff.mean() < 0.1
 

tests/unit/test_interpolation.py

@@ -320,7 +320,7 @@ def test_scipy19_interpolation_methods(mda: MetDataArray, method: str) -> None:
 
     # Pin the RMSE (out of curiosity)
     rmse = (np.mean((out1 - out2) ** 2)) ** 0.5
-    assert rmse == pytest.approx(3.3319390877860893, rel=1e-10)
+    assert rmse == pytest.approx(3.3319389446, rel=1e-10)
 
 
 @pytest.fixture()

tests/unit/test_tau_cirrus.py

@@ -58,8 +58,8 @@ def test_implementations_close(met_cocip1: MetDataset) -> None:
     assert (da1 <= 2 * da2).all()
 
     # Pin the mean values of each
-    assert da1.mean() == 0.01444357167929411
-    assert da2.mean().item() == 0.008129739202558994
+    assert da1.mean() == pytest.approx(0.014443565160, rel=1e-10)
+    assert da2.mean() == pytest.approx(0.0081297373399, rel=1e-10)
 
 
 def test_geopotential_approximation(met_cocip1: MetDataset):