diff --git a/icenet/__init__.py b/icenet/__init__.py
index 01932438..dbd51d00 100644
--- a/icenet/__init__.py
+++ b/icenet/__init__.py
@@ -4,4 +4,4 @@
 __copyright__ = "British Antarctic Survey"
 __email__ = "jambyr@bas.ac.uk"
 __license__ = "MIT"
-__version__ = "0.2.7a0"
+__version__ = "0.2.7a1"
diff --git a/icenet/process/predict.py b/icenet/process/predict.py
index c9cc0709..62a74d95 100644
--- a/icenet/process/predict.py
+++ b/icenet/process/predict.py
@@ -61,7 +61,7 @@ def get_refcube(north: bool = True, south: bool = False) -> object:
 
 def get_prediction_data(root: object,
                         name: object,
-                        date: object) -> object:
+                        date: object) -> tuple:
     """
 
     :param root:
@@ -85,12 +85,13 @@ def get_prediction_data(root: object,
 
     data = [np.load(f) for f in np_files]
     data = np.array(data)
+    ens_members = data.shape[0]
 
     logging.debug("Data read from disk: {} from: {}".format(data.shape, np_files))
 
     return np.stack(
         [data.mean(axis=0), data.std(axis=0)],
-        axis=-1).squeeze()
+        axis=-1).squeeze(), ens_members
 
 
 def date_arg(string: str) -> object:
@@ -148,9 +149,9 @@ def create_cf_output():
              for s in args.datefile.read().split()]
     args.datefile.close()
 
-    arr = np.array(
-        [get_prediction_data(args.root, args.name, date)
-         for date in dates])
+    arr, ens_members = zip(*[get_prediction_data(args.root, args.name, date) for date in dates])
+    ens_members = list(ens_members)
+    arr = np.array(arr)
 
     logging.info("Dataset arr shape: {}".format(arr.shape))
 
@@ -160,6 +161,19 @@ def create_cf_output():
     if args.mask:
         mask_gen = Masks(north=ds.north, south=ds.south)
 
+        if args.agcm:
+            logging.info("Applying active grid cell masks")
+
+            for idx, forecast_date in enumerate(dates):
+                for lead_idx in np.arange(0, arr.shape[3], 1):
+                    lead_dt = forecast_date + dt.timedelta(days=int(lead_idx) + 1)
+                    logging.debug("Active grid cell mask start {} forecast date {}".
+                                  format(forecast_date, lead_dt))
+
+                    grid_cell_mask = mask_gen.get_active_cell_mask(lead_dt.month)
+                    sic_mean[idx, ~grid_cell_mask, lead_idx] = 0
+                    sic_stddev[idx, ~grid_cell_mask, lead_idx] = 0
+
         if args.land:
             logging.info("Land masking the forecast output")
             land_mask = mask_gen.get_land_mask()
@@ -176,19 +190,6 @@ def create_cf_output():
                 sic_mean[mask] = 0
                 sic_stddev[mask] = 0
 
-        if args.agcm:
-            logging.info("Applying active grid cell masks")
-
-            for idx, forecast_date in enumerate(dates):
-                for lead_idx in np.arange(0, arr.shape[3], 1):
-                    lead_dt = forecast_date + dt.timedelta(days=int(lead_idx) + 1)
-                    logging.debug("Active grid cell mask start {} forecast date {}".
-                                  format(forecast_date, lead_dt))
-
-                    grid_cell_mask = mask_gen.get_active_cell_mask(lead_dt.month)
-                    sic_mean[idx, ~grid_cell_mask, lead_idx] = 0
-                    sic_stddev[idx, ~grid_cell_mask, lead_idx] = 0
-
     lists_of_fcast_dates = [
         [pd.Timestamp(date + dt.timedelta(days=int(lead_idx)))
          for lead_idx in np.arange(1, arr.shape[3] + 1, 1)]
@@ -200,6 +201,7 @@ def create_cf_output():
             Lambert_Azimuthal_Grid=ref_sic.Lambert_Azimuthal_Grid,
             sic_mean=(["time", "yc", "xc", "leadtime"], sic_mean),
             sic_stddev=(["time", "yc", "xc", "leadtime"], sic_stddev),
+            ensemble_members=(["time"], ens_members),
         ),
         coords=dict(
             time=[pd.Timestamp(d) for d in dates],
@@ -260,7 +262,7 @@ def create_cf_output():
             standard_name_vocabulary="CF Standard Name Table v27",
             summary="""
             This is an output of sea ice concentration predictions from the 
-            IceNet UNet run in an ensemble, with postprocessing to determine 
+            IceNet run in an ensemble, with postprocessing to determine 
             the mean and standard deviation across the runs.
             """,
             # Use ISO 8601:2004 duration format, preferably the extended format
@@ -332,6 +334,12 @@ def create_cf_output():
         units="1",
     )
 
+    xarr.ensemble_members.attrs = dict(
+        long_name="number of ensemble members used to create this prediction",
+        short_name="ensemble_members",
+        # units="1",
+    )
+
     # TODO: split into daily files
     output_path = os.path.join(args.output_dir, "{}.nc".format(args.name))
     logging.info("Saving to {}".format(output_path))