diff --git a/pdebench/models/metrics.py b/pdebench/models/metrics.py
index ab16704..5f87098 100644
--- a/pdebench/models/metrics.py
+++ b/pdebench/models/metrics.py
@@ -157,21 +157,24 @@
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-def metric_func(pred, target, if_mean=True, Lx=1.0, Ly=1.0, Lz=1.0, iLow=4, iHigh=12):
+def metric_func(pred, target, if_mean=True, Lx=1.0, Ly=1.0, Lz=1.0, iLow=4, iHigh=12, initial_step=1):
     """
     code for calculate metrics discussed in the Brain-storming session
     RMSE, normalized RMSE, max error, RMSE at the boundaries, conserved variables, RMSE in Fourier space, temporal sensitivity
     """
     pred, target = pred.to(device), target.to(device)
     # (batch, nx^i..., timesteps, nc)
+    # slice out `initial context` timesteps
+    pred = pred[..., initial_step:, :]
+    target = target[..., initial_step:, :]
     idxs = target.size()
-    if len(idxs) == 4:
+    if len(idxs) == 4: # 1D
         pred = pred.permute(0, 3, 1, 2)
         target = target.permute(0, 3, 1, 2)
-    if len(idxs) == 5:
+    if len(idxs) == 5: # 2D
         pred = pred.permute(0, 4, 1, 2, 3)
         target = target.permute(0, 4, 1, 2, 3)
-    elif len(idxs) == 6:
+    elif len(idxs) == 6: # 3D
         pred = pred.permute(0, 5, 1, 2, 3, 4)
         target = target.permute(0, 5, 1, 2, 3, 4)
     idxs = target.size()
@@ -238,12 +241,12 @@ def metric_func(pred, target, if_mean=True, Lx=1.0, Ly=1.0, Lz=1.0, iLow=4, iHig
         err_BD_z = (pred[:, :, :, :, 0] - target[:, :, :, :, 0]) ** 2
         err_BD_z += (pred[:, :, :, :, -1] - target[:, :, :, :, -1]) ** 2
         err_BD = (
-            torch.sum(err_BD_x.view([nb, -1, nt]), dim=-2)
-            + torch.sum(err_BD_y.view([nb, -1, nt]), dim=-2)
-            + torch.sum(err_BD_z.view([nb, -1, nt]), dim=-2)
+            torch.sum(err_BD_x.contiguous().view([nb, -1, nt]), dim=-2)
+            + torch.sum(err_BD_y.contiguous().view([nb, -1, nt]), dim=-2)
+            + torch.sum(err_BD_z.contiguous().view([nb, -1, nt]), dim=-2)
         )
         err_BD = err_BD / (2 * nx * ny + 2 * ny * nz + 2 * nz * nx)
-        err_BD = torch.mean(torch.sqrt(err_BD), dim=0)
+        err_BD = torch.sqrt(err_BD)
 
     if len(idxs) == 4:  # 1D
         nx = idxs[2]
@@ -350,7 +353,7 @@ def metrics(
                     _err_Max,
                     _err_BD,
                     _err_F,
-                ) = metric_func(pred, yy, if_mean=True, Lx=Lx, Ly=Ly, Lz=Lz)
+                ) = metric_func(pred, yy, if_mean=True, Lx=Lx, Ly=Ly, Lz=Lz, initial_step=initial_step)
 
                 if itot == 0:
                     err_RMSE, err_nRMSE, err_CSV, err_Max, err_BD, err_F = (
@@ -408,7 +411,7 @@ def metrics(
                     _err_Max,
                     _err_BD,
                     _err_F,
-                ) = metric_func(pred, yy, if_mean=True, Lx=Lx, Ly=Ly, Lz=Lz)
+                ) = metric_func(pred, yy, if_mean=True, Lx=Lx, Ly=Ly, Lz=Lz, initial_step=initial_step)
                 if itot == 0:
                     err_RMSE, err_nRMSE, err_CSV, err_Max, err_BD, err_F = (
                         _err_RMSE,