diff --git a/applications/rollout_to_netcdf.py b/applications/rollout_to_netcdf.py
index ab24f2c1..96475ae6 100644
--- a/applications/rollout_to_netcdf.py
+++ b/applications/rollout_to_netcdf.py
@@ -496,7 +496,16 @@ def predict(rank, world_size, conf, p):
         + len(conf["data"]["forcing_variables"])
         + len(conf["data"]["static_variables"])
     )
-
+    
+    # ------------------------------------------------------- #
+    # clamp to remove outliers
+    if conf["data"]["data_clamp"] is None:
+        flag_clamp = False
+    else:
+        flag_clamp = True
+        clamp_min = float(conf["data"]["data_clamp"][0])
+        clamp_max = float(conf["data"]["data_clamp"][1])
+        
     # ====================================================== #
     # postblock opts outside of model
     post_conf = conf["model"]["post_conf"]
@@ -646,6 +655,13 @@ def predict(rank, world_size, conf, p):
 
             # -------------------------------------------------------------------------------------- #
             # start prediction
+
+            # --------------------------------------------- #
+            # clamp
+            if flag_clamp:
+                x = torch.clamp(x, min=clamp_min, max=clamp_max)
+                #y = torch.clamp(y, min=clamp_min, max=clamp_max)
+            
             y_pred = model(x)
 
             # ============================================= #
diff --git a/config/example_physics_single.yml b/config/example_physics_single.yml
index 653d948f..506ba318 100644
--- a/config/example_physics_single.yml
+++ b/config/example_physics_single.yml
@@ -1,8 +1,5 @@
 # --------------------------------------------------------------------------------------------------------------------- #
-# This yaml file implements 6 hourly FuXi on NSF NCAR HPCs (casper.ucar.edu and derecho.hpc.ucar.edu) 
-# the FuXi architecture has been modified to reduce the overall model size
-# The model is trained on hourly model-level ERA5 data with top solar irradiance, geopotential, and land-sea mask inputs
-# Output variables: model level [U, V, T, Q], single level [SP, t2m], and 500 hPa [U, V, T, Z, Q]
+# example
 # --------------------------------------------------------------------------------------------------------------------- #
 save_loc: '/glade/work/$USER/CREDIT_runs/fuxi_conserve/'
 seed: 1000
@@ -47,6 +44,7 @@ data:
     
     # data workflow
     scaler_type: 'std_new'  
+    data_clamp: [-16, 16]
     
     # number of input states
     # FuXi has 2 input states
diff --git a/credit/loss.py b/credit/loss.py
index 10fd701e..067deafc 100644
--- a/credit/loss.py
+++ b/credit/loss.py
@@ -369,7 +369,7 @@ def latitude_weights(conf):
     return L
 
 
-def variable_weights(conf, channels, surface_channels, frames):
+def variable_weights(conf, channels, frames):
     """Create variable-specific weights for different atmospheric
     and surface channels.
 
@@ -382,7 +382,6 @@ def variable_weights(conf, channels, surface_channels, frames):
         conf (dict): Configuration dictionary containing the
             variable weights.
         channels (int): Number of channels for atmospheric variables.
-        surface_channels (int): Number of channels for surface variables.
         frames (int): Number of time frames.
 
     Returns:
@@ -393,41 +392,25 @@ def variable_weights(conf, channels, surface_channels, frames):
     varname_upper_air = conf["data"]["variables"]
     varname_surface = conf["data"]["surface_variables"]
     varname_diagnostics = conf["data"]["diagnostic_variables"]
-    # N_levels = conf['data']['levels']
 
-    # weights_UVTQ = torch.tensor([
-    #     conf["loss"]["variable_weights"]["U"],
-    #     conf["loss"]["variable_weights"]["V"],
-    #     conf["loss"]["variable_weights"]["T"],
-    #     conf["loss"]["variable_weights"]["Q"]
-    # ]).view(1, channels * frames, 1, 1)
-
-    weights_UVTQ = torch.tensor(
+    # surface + diag channels
+    N_channels_single = len(varname_surface) + len(varname_diagnostics)
+    
+    weights_upper_air = torch.tensor(
         [conf["loss"]["variable_weights"][var] for var in varname_upper_air]
     ).view(1, channels * frames, 1, 1)
-
-    # Load weights for SP, t2m, V500, U500, T500, Z500, Q500
-    # weights_sfc = torch.tensor([
-    #     conf["loss"]["variable_weights"]["SP"],
-    #     conf["loss"]["variable_weights"]["t2m"],
-    #     conf["loss"]["variable_weights"]["V500"],
-    #     conf["loss"]["variable_weights"]["U500"],
-    #     conf["loss"]["variable_weights"]["T500"],
-    #     conf["loss"]["variable_weights"]["Z500"],
-    #     conf["loss"]["variable_weights"]["Q500"]
-    # ]).view(1, surface_channels, 1, 1)
-
-    weights_sfc = torch.tensor(
+    
+    weights_single = torch.tensor(
         [
             conf["loss"]["variable_weights"][var]
             for var in (varname_surface + varname_diagnostics)
         ]
-    ).view(1, surface_channels, 1, 1)
+    ).view(1, N_channels_single, 1, 1)
 
     # Combine all weights along the color channel
-    variable_weights = torch.cat([weights_UVTQ, weights_sfc], dim=1)
+    var_weights = torch.cat([weights_upper_air, weights_single], dim=1)
 
-    return variable_weights
+    return var_weights
 
 
 class VariableTotalLoss2D(torch.nn.Module):
@@ -471,18 +454,25 @@ def __init__(self, conf, validation=False):
             logger.info("Using latitude weights in loss calculations")
             self.lat_weights = latitude_weights(conf)[:, 10].unsqueeze(0).unsqueeze(-1)
 
+        # ------------------------------------------------------------- #
+        # variable weights
+        # order: upper air --> surface --> diagnostics
         self.var_weights = None
         if conf["loss"]["use_variable_weights"]:
             logger.info("Using variable weights in loss calculations")
+            
             var_weights = [
                 value if isinstance(value, list) else [value]
                 for value in conf["loss"]["variable_weights"].values()
             ]
+            
             var_weights = np.array(
                 [item for sublist in var_weights for item in sublist]
             )
+            
             self.var_weights = torch.from_numpy(var_weights)
-
+        # ------------------------------------------------------------- #
+        
         self.use_spectral_loss = conf["loss"]["use_spectral_loss"]
         if self.use_spectral_loss:
             self.spectral_lambda_reg = conf["loss"]["spectral_lambda_reg"]
diff --git a/credit/models/fuxi.py b/credit/models/fuxi.py
index 162e8807..7627ef5c 100644
--- a/credit/models/fuxi.py
+++ b/credit/models/fuxi.py
@@ -226,7 +226,15 @@ class UTransformer(nn.Module):
     """
 
     def __init__(
-        self, embed_dim, num_groups, input_resolution, num_heads, window_size, depth
+        self, embed_dim, 
+        num_groups, 
+        input_resolution, 
+        num_heads, 
+        window_size, 
+        depth,
+        proj_drop,
+        attn_drop,
+        drop_path
     ):
         super().__init__()
         num_groups = to_2tuple(num_groups)
@@ -248,7 +256,15 @@ def __init__(
 
         # SwinT block
         self.layer = SwinTransformerV2Stage(
-            embed_dim, embed_dim, input_resolution, depth, num_heads, window_size[0]
+            embed_dim, 
+            embed_dim, 
+            input_resolution, 
+            depth, 
+            num_heads, 
+            window_size[0],
+            proj_drop=proj_drop,
+            attn_drop=attn_drop,
+            drop_path=drop_path
         )  # <--- window_size[0] get window_size[int] from tuple
 
         # up-sampling block
@@ -315,6 +331,9 @@ def __init__(
         window_size=7,
         use_spectral_norm=True,
         interp=True,
+        proj_drop=0,
+        attn_drop=0,
+        drop_path=0,
         padding_conf=None,
         post_conf=None,
         **kwargs,
@@ -323,11 +342,15 @@ def __init__(
 
         self.use_interp = interp
         self.use_spectral_norm = use_spectral_norm
+        
         if padding_conf is None:
             padding_conf = {"activate": False}
+            
         self.use_padding = padding_conf["activate"]
+        
         if post_conf is None:
             post_conf = {"activate": False}
+            
         self.use_post_block = post_conf["activate"]
 
         # input tensor size (time, lat, lon)
@@ -362,8 +385,17 @@ def __init__(
         self.cube_embedding = CubeEmbedding(img_size, patch_size, in_chans, dim)
 
         # Downsampling --> SwinTransformerV2 stacks --> Upsampling
+        logger.info(f"Define UTransforme with proj_drop={proj_drop}, attn_drop={attn_drop}, drop_path={drop_path}")
+        
         self.u_transformer = UTransformer(
-            dim, num_groups, input_resolution, num_heads, window_size, depth=depth
+            dim, num_groups, 
+            input_resolution, 
+            num_heads, 
+            window_size, 
+            depth=depth,
+            proj_drop=proj_drop,
+            attn_drop=attn_drop,
+            drop_path=drop_path
         )
 
         # dense layer applied on channel dmension
@@ -383,11 +415,12 @@ def __init__(
         if self.use_padding:
             self.padding_opt = TensorPadding(**padding_conf)
 
+        # Move the model to the device
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.to(device)
+        
         if self.use_spectral_norm:
             logger.info("Adding spectral norm to all conv and linear layers")
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            # Move the model to the device
-            self.to(device)
             apply_spectral_norm(self)
 
         if self.use_post_block:
diff --git a/credit/parser.py b/credit/parser.py
index c50e7a45..b7db64ef 100644
--- a/credit/parser.py
+++ b/credit/parser.py
@@ -250,6 +250,9 @@ def credit_main_parser(
     )
 
     ## I/O data sizes
+
+    conf["data"].setdefault("data_clamp", None)
+    
     if parse_training:
         assert (
             "train_years" in conf["data"]
@@ -264,7 +267,7 @@ def credit_main_parser(
         assert (
             "forecast_len" in conf["data"]
         ), "Number of time frames for loss compute ('forecast_len') is missing from conf['data']"
-
+        
         if "valid_history_len" not in conf["data"]:
             # use "history_len" for "valid_history_len"
             conf["data"]["valid_history_len"] = conf["data"]["history_len"]
@@ -420,8 +423,8 @@ def credit_main_parser(
         )
 
         # # debug only
-        # conf['model']['post_conf']['varname_input'] = varname_input
-        # conf['model']['post_conf']['varname_output'] = varname_output
+        conf['model']['post_conf']['varname_input'] = varname_input
+        conf['model']['post_conf']['varname_output'] = varname_output
         # --------------------------------------------------------------------- #
 
     # SKEBS
@@ -478,6 +481,7 @@ def credit_main_parser(
         conf["model"]["post_conf"]["global_mass_fixer"].setdefault("denorm", True)
         conf["model"]["post_conf"]["global_mass_fixer"].setdefault("simple_demo", False)
         conf["model"]["post_conf"]["global_mass_fixer"].setdefault("midpoint", False)
+        conf['model']['post_conf']['global_mass_fixer'].setdefault('grid_type', 'pressure')
 
         assert (
             "fix_level_num" in conf["model"]["post_conf"]["global_mass_fixer"]
@@ -487,7 +491,11 @@ def credit_main_parser(
             assert (
                 "lon_lat_level_name" in conf["model"]["post_conf"]["global_mass_fixer"]
             ), "Must specifiy var names for lat/lon/level in physics reference file"
-
+        
+        if conf['model']['post_conf']['global_mass_fixer']['grid_type'] == 'sigma':
+            assert 'surface_pressure_name' in conf['model']['post_conf']['global_mass_fixer'], (
+                'Must specifiy surface pressure var name when using hybrid sigma-pressure coordinates')
+        
         q_inds = [
             i_var
             for i_var, var in enumerate(varname_output)
@@ -498,6 +506,13 @@ def credit_main_parser(
         ]
         conf["model"]["post_conf"]["global_mass_fixer"]["q_inds"] = q_inds
 
+        if conf['model']['post_conf']['global_mass_fixer']['grid_type'] == 'sigma':
+            sp_inds = [
+                i_var for i_var, var in enumerate(varname_output) 
+                if var in conf['model']['post_conf']['global_mass_fixer']['surface_pressure_name']
+            ]        
+            conf['model']['post_conf']['global_mass_fixer']['sp_inds'] = sp_inds[0]
+    
     # --------------------------------------------------------------------- #
     # global water fixer
     flag_water = (
@@ -518,12 +533,17 @@ def credit_main_parser(
             "simple_demo", False
         )
         conf["model"]["post_conf"]["global_water_fixer"].setdefault("midpoint", False)
+        conf['model']['post_conf']['global_water_fixer'].setdefault('grid_type', 'pressure')
 
         if conf["model"]["post_conf"]["global_water_fixer"]["simple_demo"] is False:
             assert (
                 "lon_lat_level_name" in conf["model"]["post_conf"]["global_water_fixer"]
             ), "Must specifiy var names for lat/lon/level in physics reference file"
 
+        if conf['model']['post_conf']['global_water_fixer']['grid_type'] == 'sigma':
+            assert 'surface_pressure_name' in conf['model']['post_conf']['global_water_fixer'], (
+                'Must specifiy surface pressure var name when using hybrid sigma-pressure coordinates')
+        
         q_inds = [
             i_var
             for i_var, var in enumerate(varname_output)
@@ -551,6 +571,13 @@ def credit_main_parser(
         conf["model"]["post_conf"]["global_water_fixer"]["precip_ind"] = precip_inds[0]
         conf["model"]["post_conf"]["global_water_fixer"]["evapor_ind"] = evapor_inds[0]
 
+        if conf['model']['post_conf']['global_water_fixer']['grid_type'] == 'sigma':
+            sp_inds = [
+                i_var for i_var, var in enumerate(varname_output) 
+                if var in conf['model']['post_conf']['global_water_fixer']['surface_pressure_name']
+            ]        
+            conf['model']['post_conf']['global_water_fixer']['sp_inds'] = sp_inds[0]
+    
     # --------------------------------------------------------------------- #
     # global energy fixer
     flag_energy = (
@@ -571,6 +598,7 @@ def credit_main_parser(
             "simple_demo", False
         )
         conf["model"]["post_conf"]["global_energy_fixer"].setdefault("midpoint", False)
+        conf['model']['post_conf']['global_energy_fixer'].setdefault('grid_type', 'pressure')
 
         if conf["model"]["post_conf"]["global_energy_fixer"]["simple_demo"] is False:
             assert (
@@ -578,6 +606,10 @@ def credit_main_parser(
                 in conf["model"]["post_conf"]["global_energy_fixer"]
             ), "Must specifiy var names for lat/lon/level in physics reference file"
 
+        if conf['model']['post_conf']['global_energy_fixer']['grid_type'] == 'sigma':
+            assert 'surface_pressure_name' in conf['model']['post_conf']['global_energy_fixer'], (
+                'Must specifiy surface pressure var name when using hybrid sigma-pressure coordinates')
+        
         T_inds = [
             i_var
             for i_var, var in enumerate(varname_output)
@@ -645,6 +677,13 @@ def credit_main_parser(
             surf_flux_inds
         )
 
+        if conf['model']['post_conf']['global_energy_fixer']['grid_type'] == 'sigma':
+            sp_inds = [
+                i_var for i_var, var in enumerate(varname_output) 
+                if var in conf['model']['post_conf']['global_energy_fixer']['surface_pressure_name']
+            ]        
+            conf['model']['post_conf']['global_energy_fixer']['sp_inds'] = sp_inds[0]
+    
     # --------------------------------------------------------- #
     # conf['trainer'] section
 
diff --git a/credit/trainers/trainerERA5_multistep_grad_accum.py b/credit/trainers/trainerERA5_multistep_grad_accum.py
index 3d2c840e..7b88f3ad 100644
--- a/credit/trainers/trainerERA5_multistep_grad_accum.py
+++ b/credit/trainers/trainerERA5_multistep_grad_accum.py
@@ -112,6 +112,15 @@ def train_one_epoch(
         ):
             scheduler.step()
 
+        # ------------------------------------------------------- #
+        # clamp to remove outliers
+        if conf["data"]["data_clamp"] is None:
+            flag_clamp = False
+        else:
+            flag_clamp = True
+            clamp_min = float(conf["data"]["data_clamp"][0])
+            clamp_max = float(conf["data"]["data_clamp"][1])
+        
         # ====================================================== #
         # postblock opts outside of model
         post_conf = conf["model"]["post_conf"]
@@ -195,6 +204,11 @@ def train_one_epoch(
                             # concat on var dimension
                             x = torch.cat((x, x_forcing_batch), dim=1)
 
+                        # --------------------------------------------- #
+                        # clamp
+                        if flag_clamp:
+                            x = torch.clamp(x, min=clamp_min, max=clamp_max)
+                        
                         # predict with the model
                         y_pred = self.model(x)
 
@@ -246,6 +260,11 @@ def train_one_epoch(
                                 # concat on var dimension
                                 y = torch.cat((y, y_diag_batch), dim=1)
 
+                            # --------------------------------------------- #
+                            # clamp
+                            if flag_clamp:
+                                y = torch.clamp(y, min=clamp_min, max=clamp_max)
+                                
                             loss = criterion(y.to(y_pred.dtype), y_pred).mean()
 
                             # track the loss
@@ -405,6 +424,15 @@ def validate(self, epoch, conf, valid_loader, criterion, metrics):
                 else len(valid_loader)
             )
 
+        # ------------------------------------------------------- #
+        # clamp to remove outliers
+        if conf["data"]["data_clamp"] is None:
+            flag_clamp = False
+        else:
+            flag_clamp = True
+            clamp_min = float(conf["data"]["data_clamp"][0])
+            clamp_max = float(conf["data"]["data_clamp"][1])
+        
         # ====================================================== #
         # postblock opts outside of model
         post_conf = conf["model"]["post_conf"]
@@ -466,7 +494,11 @@ def validate(self, epoch, conf, valid_loader, criterion, metrics):
                         # concat on var dimension
                         x = torch.cat((x, x_forcing_batch), dim=1)
 
-                    # logger.info('k = {}; x.shape() = {}'.format(forecast_step, x.shape))
+                    # --------------------------------------------- #
+                    # clamp
+                    if flag_clamp:
+                        x = torch.clamp(x, min=clamp_min, max=clamp_max)
+                        
                     y_pred = self.model(x)
 
                     # ============================================= #
@@ -517,6 +549,11 @@ def validate(self, epoch, conf, valid_loader, criterion, metrics):
                             # concat on var dimension
                             y = torch.cat((y, y_diag_batch), dim=1)
 
+                        # --------------------------------------------- #
+                        # clamp
+                        if flag_clamp:
+                            y = torch.clamp(y, min=clamp_min, max=clamp_max)
+                        
                         # ----------------------------------------------------------------------- #
                         # calculate rolling loss
                         loss = criterion(y.to(y_pred.dtype), y_pred).mean()
diff --git a/credit/trainers/trainerERA5_v2.py b/credit/trainers/trainerERA5_v2.py
index 02c7b947..2c261a6a 100644
--- a/credit/trainers/trainerERA5_v2.py
+++ b/credit/trainers/trainerERA5_v2.py
@@ -67,6 +67,15 @@ def train_one_epoch(
         ):
             scheduler.step()
 
+        # ------------------------------------------------------- #
+        # clamp to remove outliers
+        if conf["data"]["data_clamp"] is None:
+            flag_clamp = False
+        else:
+            flag_clamp = True
+            clamp_min = float(conf["data"]["data_clamp"][0])
+            clamp_max = float(conf["data"]["data_clamp"][1])
+        
         # ====================================================== #
         # postblock opts outside of model
         post_conf = conf["model"]["post_conf"]
@@ -157,6 +166,12 @@ def train_one_epoch(
                     # concat on var dimension
                     y = torch.cat((y, y_diag_batch), dim=1)
 
+                # --------------------------------------------- #
+                # clamp
+                if flag_clamp:
+                    x = torch.clamp(x, min=clamp_min, max=clamp_max)
+                    y = torch.clamp(y, min=clamp_min, max=clamp_max)
+                
                 # single step predict
                 y_pred = self.model(x)
 
@@ -340,6 +355,15 @@ def validate(self, epoch, conf, valid_loader, criterion, metrics):
 
         results_dict = defaultdict(list)
 
+        # ------------------------------------------------------- #
+        # clamp to remove outliers
+        if conf["data"]["data_clamp"] is None:
+            flag_clamp = False
+        else:
+            flag_clamp = True
+            clamp_min = float(conf["data"]["data_clamp"][0])
+            clamp_max = float(conf["data"]["data_clamp"][1])
+        
         # ====================================================== #
         # postblock opts outside of model
         post_conf = conf["model"]["post_conf"]
@@ -424,6 +448,12 @@ def validate(self, epoch, conf, valid_loader, criterion, metrics):
                     # concat on var dimension
                     y = torch.cat((y, y_diag_batch), dim=1)
 
+                # --------------------------------------------- #
+                # clamp
+                if flag_clamp:
+                    x = torch.clamp(x, min=clamp_min, max=clamp_max)
+                    y = torch.clamp(y, min=clamp_min, max=clamp_max)
+                
                 y_pred = self.model(x)
 
                 # ============================================= #