diff --git a/improver_tests/calibration/quantile_regression_random_forests_calibration/test_load_and_train_quantile_regression_random_forest.py b/improver_tests/calibration/quantile_regression_random_forests_calibration/test_load_and_train_quantile_regression_random_forest.py
index 61ebeb95ab..93135c17f1 100644
--- a/improver_tests/calibration/quantile_regression_random_forests_calibration/test_load_and_train_quantile_regression_random_forest.py
+++ b/improver_tests/calibration/quantile_regression_random_forests_calibration/test_load_and_train_quantile_regression_random_forest.py
@@ -158,7 +158,7 @@ def _create_multi_site_forecast_parquet_file(tmp_path, representation=None):
             [ALTITUDE_LOOKUP[_id] for _id in wmo_ids], dtype=np.float32
         ),
         "blend_time": [pd.Timestamp("2017-01-02 00:00:00", tz="utc")] * 5,
-        "forecast_period": [int(6 * 3.6e9)] * 5,
+        "forecast_period": [int(6 * 3600)] * 5,
         "forecast_reference_time": [pd.Timestamp("2017-01-02 00:00:00", tz="utc")] * 5,
         "latitude": np.array(
             [LATITUDE_LOOKUP[_id] for _id in wmo_ids], dtype=np.float32
@@ -211,7 +211,7 @@ def _create_multi_percentile_forecast_parquet_file(tmp_path, representation=None
             [ALTITUDE_LOOKUP[_id] for _id in wmo_ids], dtype=np.float32
         ),
         "blend_time": [pd.Timestamp("2017-01-02 00:00:00", tz="utc")] * 5,
-        "forecast_period": [int(6 * 3.6e9)] * 5,
+        "forecast_period": [int(6 * 3600)] * 5,
         "forecast_reference_time": [pd.Timestamp("2017-01-02 00:00:00", tz="utc")] * 5,
         "latitude": np.array(
             [LATITUDE_LOOKUP[_id] for _id in wmo_ids], dtype=np.float32
@@ -262,13 +262,7 @@ def _create_multi_forecast_period_forecast_parquet_file(tmp_path, representation
             [ALTITUDE_LOOKUP[_id] for _id in wmo_ids], dtype=np.float32
         ),
         "blend_time": [pd.Timestamp("2017-01-02 00:00:00")] * 4,
-        "forecast_period": np.repeat(
-            [
-                [int(6 * 3.6e9)],
-                [int(12 * 3.6e9)],
-            ],
-            2,
-        ),
+        "forecast_period": np.repeat([[6 * 3600], [12 * 3600]], 2),
         "forecast_reference_time": [pd.Timestamp("2017-01-02 00:00:00")] * 4,
         "latitude": np.array(
             [LATITUDE_LOOKUP[_id] for _id in wmo_ids], dtype=np.float32
diff --git a/improver_tests/calibration/quantile_regression_random_forests_calibration/test_quantile_regression_random_forest.py b/improver_tests/calibration/quantile_regression_random_forests_calibration/test_quantile_regression_random_forest.py
index ebb8c67c56..4f588edc38 100644
--- a/improver_tests/calibration/quantile_regression_random_forests_calibration/test_quantile_regression_random_forest.py
+++ b/improver_tests/calibration/quantile_regression_random_forests_calibration/test_quantile_regression_random_forest.py
@@ -569,7 +569,7 @@ def test_prep_features_from_config(feature_config):
         "realization": np.tile([1, 2], 3),
         "distance_to_water": np.tile([2.0, 3.0], 3),
         "blend_time": pd.Timestamp("2017-01-02 00:00:00", tz="utc"),
-        "forecast_period": int(6 * 3.6e9),
+        "forecast_period": int(6 * 3600),
         "forecast_reference_time": pd.Timestamp("2017-01-02 00:00:00", tz="utc"),
     }
     df = pd.DataFrame(data_dict)
diff --git a/improver_tests/calibration/test_init.py b/improver_tests/calibration/test_init.py
index b35f3186e1..73f9f8ed7f 100644
--- a/improver_tests/calibration/test_init.py
+++ b/improver_tests/calibration/test_init.py
@@ -712,7 +712,7 @@ def create_multi_site_forecast_parquet_file(tmp_path, include_forecast_period=Tr
 
     parquet_type = "truth"
     if include_forecast_period:
-        data_dict["forecast_period"] = [int(6 * 3.6e9)] * 5
+        data_dict["forecast_period"] = [int(6 * 3600)] * 5
         parquet_type = "forecast"
 
     data_df = pd.DataFrame(data_dict)