diff --git a/modules/deeplearning/esrgan_exp.py b/modules/deeplearning/esrgan_exp.py
index 4d0380bf3cc3525756b11b3c8430e06481ffb0d5..7d87331ccb2a2af7afd716b644c6942318a18bfa 100644
--- a/modules/deeplearning/esrgan_exp.py
+++ b/modules/deeplearning/esrgan_exp.py
@@ -12,7 +12,8 @@ from util.setup import home_dir
 target_param = 'cld_opd_dcomp'
 
 # SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
-SAVED_MODEL_PATH = home_dir + '/esrgan-tf2_1'
+# SAVED_MODEL_PATH = home_dir + '/esrgan-tf2_1'
+SAVED_MODEL_PATH = '/ships22/cloud/scratch/Satellite_Output/GOES-16/global/NREL_2023/2023_east_cf/tf_model_esrgan'
 
 model = hub.load(SAVED_MODEL_PATH)
 
@@ -27,25 +28,38 @@ def get_image_from_file(in_file):
     # s_x = slice(1854, 3902)
     # s_y = slice(1854, 3902)
 
-    # bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
-    # bt = bt[s_y, s_x]
+    refl = get_grid_values_all(h5f, 'refl_0_65um_nom')
+    refl = refl[s_y, s_x]
 
     cld_opd = get_grid_values_all(h5f, target_param)
     cld_opd = cld_opd[s_y, s_x]
 
     shape = cld_opd.shape
-    data = cld_opd.flatten()
+    cld_opd = cld_opd.flatten()
 
     lo = 0.0
     hi = 160.0
+    cld_opd -= lo
+    cld_opd /= (hi - lo)
 
-    data -= lo
-    data /= (hi - lo)
+    not_valid = np.isnan(cld_opd)
+    cld_opd[not_valid] = 0
 
-    not_valid = np.isnan(data)
-    data[not_valid] = 0
+    cld_opd = np.reshape(cld_opd, shape)
+    # -----------------------------------------------------------
+    refl = refl.flatten()
 
-    data = np.reshape(data, shape)
+    lo = -2.0
+    hi = 120.0
+    refl -= lo
+    refl /= (hi - lo)
+
+    not_valid = np.isnan(refl)
+    refl[not_valid] = 0
+
+    refl = np.reshape(refl, shape)
+
+    data = np.stack([refl, cld_opd], axis=2)
 
     return data
 
@@ -86,6 +100,8 @@ def preprocess_image(hr_image):
       hr_image = tmp_image
       hr_image *= 255.0
 
+  hr_image *= 255.0
+
   hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
   hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
   hr_image = tf.cast(hr_image, tf.float32)
@@ -94,10 +110,8 @@ def preprocess_image(hr_image):
 
 
 def run(in_file, out_file):
-    # model = hub.load(SAVED_MODEL_PATH)
     t0 = time.time()
     hr_image = get_image_from_file(in_file)
-    # hr_image = get_image(in_file)
     hr_image = preprocess_image(hr_image)
     t1 = time.time()
     print('processing time: ', (t1-t0))