diff --git a/modules/deeplearning/cloud_fraction_fcn_abi.py b/modules/deeplearning/cloud_fraction_fcn_abi.py
index ef6f7d126b60254f5eeb2bf2ffd80fb738890a2d..4651ecf5208cdfbaa61ac81c3e42a568200b7b95 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi.py
@@ -776,6 +776,44 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     h5f = h5py.File(in_file, 'r')
 
+    bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
+    y_len, x_len = bt.shape
+    refl = get_grid_values_all(h5f, 'refl_0_65um_nom')
+    refl_lo = get_grid_values_all(h5f, 'refl_0_65um_nom_min_sub')
+    refl_hi = get_grid_values_all(h5f, 'refl_0_65um_nom_max_sub')
+    refl_std = get_grid_values_all(h5f, 'refl_0_65um_nom_stddev_sub')
+    cp = get_grid_values_all(h5f, label_param)
+    lons = get_grid_values_all(h5f, 'longitude')
+    lats = get_grid_values_all(h5f, 'latitude')
+
+    h5f.close()
+
+    cld_frac = run_evaluate_static_(bt, refl, refl_lo, refl_hi, refl_std, cp, ckpt_dir)
+
+    cld_frac_out = np.zeros((y_len, x_len), dtype=np.int8)
+    border = int((KERNEL_SIZE - 1)/2)
+    cld_frac_out[border:y_len-border, border:x_len - border] = cld_frac[0, :, :]
+
+    var_names = ['cloud_fraction', 'temp_11_0um', 'refl_0_65um']
+    dims = ['num_params', 'y', 'x']
+    da = xr.DataArray(np.stack([cld_frac_out, bt, refl], axis=0), dims=dims)
+    da.assign_coords({
+        'num_params': var_names,
+        'lat': (['y', 'x'], lats),
+        'lon': (['y', 'x'], lons)
+    })
+
+    if out_file is not None:
+        np.save(out_file, (cld_frac_out, bt, refl, cp, lons, lats))
+    else:
+        return [cld_frac_out, bt, refl, cp, lons, lats]
+
+
+def run_evaluate_static_full_disk(in_file, out_file, ckpt_dir):
+    gc.collect()
+
+    h5f = h5py.File(in_file, 'r')
+
     bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
     y_len, x_len = bt.shape
     h_y_len = int(y_len/2)
@@ -812,9 +850,6 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_frac_out[border:h_y_len, border:x_len - border] = cld_frac_nh[0, :, :]
     cld_frac_out[h_y_len:y_len - border, border:x_len - border] = cld_frac_sh[0, :, :]
 
-    bt = denormalize(bt, 'temp_11_0um_nom', mean_std_dct)
-    refl = denormalize(refl, 'refl_0_65um_nom', mean_std_dct)
-
     var_names = ['cloud_fraction', 'temp_11_0um', 'refl_0_65um']
     dims = ['num_params', 'y', 'x']
     da = xr.DataArray(np.stack([cld_frac_out, bt, refl], axis=0), dims=dims)