diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 7271cd68800c00e57a4306f67a8bb27867103e5b..aeb70a6355b8826452a4e8377bdb6230fd43db85 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -1191,18 +1191,18 @@ def run_evaluate_static_avg(ckpt_dir_s_path, day_night='NIGHT', l1b_andor_l2='BO
         sum += w
     avg_weights = sum / len(weight_s)
 
-    # ---------------------------------------------
-
+    # -- Make a new model for the averaged weights
     new_model = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_andor_l2, use_flight_altitude=use_flight_altitude)
     new_model.build_model()
     new_model.build_training()
     new_model.build_evaluation()
 
-    if ckpt_dir is None:
-        if not os.path.exists(modeldir):
-            os.mkdir(modeldir)
-        ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
-        ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
+    # -- save the averaged weights to a new the model
+    if not os.path.exists(modeldir):
+        os.mkdir(modeldir)
+    ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
+    ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
+
     new_model.model.set_weights(avg_weights)
     ckpt_manager.save()