diff --git a/interpolation/ORAS5_enlarge_ORCA025.py b/interpolation/ORAS5_enlarge_ORCA025.py
new file mode 100644
index 0000000000000000000000000000000000000000..f960183cb3a0d2f65736107b66ea876d72d6c863
--- /dev/null
+++ b/interpolation/ORAS5_enlarge_ORCA025.py
@@ -0,0 +1,44 @@
+## Load all this modules before running the script in CTE-POWER: 
+module load gcc
+module load NETCDF
+module load HDF5
+module load UDUNITS
+module load CDO
+module load gsl
+module load nco
+module load ncview
+module load Python/2.7.15-foss-2018b
+module load IPython/5.7.0-foss-2018b-Python-2.7.15
+module load Cartopy/0.16.0-foss-2018b-Python-2.7.15
+module load xarray/0.11.2-foss-2018b-Python-2.7.15
+##################################################
+
+import xarray as xr
+import numpy as np
+
+ds = xr.open_dataset('mesh_mask_ORCA025.L75_ece3.2_2017.nc4').rename({'z':'nav_lev'})
+for year in range(2008,2018):
+    ds2 = xr.open_dataset('/gpfs/projects/bsc32/repository/nudging/ocean/s5/ORCA025L75_ORAS5/fc0/s5_fc0_'+str(year)+'.nc')
+    for var in ('votemper','vosaline'):
+        print var
+        values = np.empty((ds2.dims['time'],ds2.dims['nav_lev'],ds.dims['y'],ds.dims['x']))
+        ds_out = xr.DataArray(values,
+                              coords={'nav_lon':(['y','x'],ds.nav_lon),
+                                      'nav_lat':(['y','x'],ds.nav_lat),
+                                      'nav_lev':(['nav_lev'],ds.nav_lev),
+                                      'time_counter':ds2.time.rename({'time':'time_counter'})},
+                              dims=('time_counter','nav_lev','y','x'),
+                              name=var)
+    # select variable
+        var_os5 = ds2[var]
+    # extrapolate on land, only for Antarctic case : if find a nan value, then copy the value from the line above.
+        for j in range(250,0,-1):
+            var_os5[:,:,j,:]=xr.where(np.isnan(var_os5[:,:,j,:]),var_os5[:,:,j+1,:],var_os5[:,:,j,:])
+    # affect to new field on ORCA025
+        ds_out[:,:,29:,:]=var_os5*ds.tmask[0,:,29:,:].where(ds.tmask[0,:,29:,:]==1)
+    # expend to full ORCA025 grid
+        for i in range(1,30):
+            ds_out[:,:,i,:]=var_os5[:,:,1,:]*ds.tmask[0,:,i,:].where(ds.tmask[0,:,i,:]==1)
+        ds_out.to_netcdf('s5_fc0_'+str(year)+'_'+var+'.nc',unlimited_dims='time_counter')
+
+