diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index caf873de77cb6295336e1b94979fbc022d162b40..343502bdc0fb39691fd75c7e31c1cceea46ac333 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -831,13 +831,12 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     grd_c = grd_c[slc_y_2, slc_x_2]
     grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
     grd_c = grd_c[y_k, x_k]
-
     if label_param != 'cloud_probability':
         grd_c = normalize(grd_c, label_param, mean_std_dct)
 
     # data = np.stack([grd_a, grd_b, grd_c], axis=2)
-    # data = np.stack([grd_a, grd_c], axis=2)
-    data = np.stack([grd_c], axis=2)
+    data = np.stack([grd_a, grd_c], axis=2)
+    # data = np.stack([grd_c], axis=2)
     data = np.expand_dims(data, axis=0)
 
     h5f.close()
@@ -845,9 +844,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     nn = SRCNN()
     out_sr = nn.run_evaluate(data, ckpt_dir)
     if out_file is not None:
-        np.save(out_file, (out_sr[0, :, :, 0], hr_grd_c))
+        np.save(out_file, (out_sr[0, :, :, 0], hr_grd_a, hr_grd_c))
     else:
-        return out_sr, hr_grd_c
+        return out_sr, hr_grd_a, hr_grd_c
 
 
 if __name__ == "__main__":