Regridding ROMS Output

Regridding ROMS Output#

[1]:

from roms_tools import Grid, ROMSOutput

We first read some ROMS data. For more details on reading ROMS data, see this notebook.

[2]:

grid = Grid(filename=
    "/global/cfs/projectdirs/m4746/Datasets/ROMSOutput/eastpac25km/epac25km_grd.nc"
)

2026-01-08 15:04:04 - WARNING - Vertical coordinates (Cs_r, Cs_w) not found in grid file.
2026-01-08 15:04:04 - INFO - === Preparing the vertical coordinate system using N = 100, theta_s = 5.0, theta_b = 2.0, hc = 300.0 ===
2026-01-08 15:04:04 - INFO - Total time: 0.003 seconds
2026-01-08 15:04:04 - INFO - ================================================================================================

[3]:

roms_output = ROMSOutput(
    grid=grid,
    path=[
        "/global/cfs/projectdirs/m4746/Datasets/ROMSOutput/eastpac25km/eastpac25km_rst.19980106000000.nc",
        "/global/cfs/projectdirs/m4746/Datasets/ROMSOutput/eastpac25km/eastpac25km_rst.19990201000000.nc",
    ],
    use_dask=True,
)

[4]:

roms_output.ds

[4]:

<xarray.Dataset> Size: 3GB
Dimensions:                (time: 4, auxil: 6, eta_rho: 162, xi_rho: 122,
                            xi_u: 121, eta_v: 161, s_rho: 100)
Coordinates:
  * time                   (time) datetime64[ns] 32B 1998-01-05T23:50:00 ... ...
    lon_rho                (eta_rho, xi_rho) float64 158kB ...
    lat_rho                (eta_rho, xi_rho) float64 158kB ...
    lat_u                  (eta_rho, xi_u) float64 157kB 7.72 7.831 ... 52.23
    lon_u                  (eta_rho, xi_u) float64 157kB 231.9 232.1 ... 237.4
    lat_v                  (eta_v, xi_rho) float64 157kB 7.758 7.87 ... 52.18
    lon_v                  (eta_v, xi_rho) float64 157kB 231.8 232.0 ... 237.6
Dimensions without coordinates: auxil, eta_rho, xi_rho, xi_u, eta_v, s_rho
Data variables: (12/58)
    ocean_time             (time) float64 32B dask.array<chunksize=(1,), meta=np.ndarray>
    time_step              (time, auxil) int32 96B dask.array<chunksize=(1, 6), meta=np.ndarray>
    zeta                   (time, eta_rho, xi_rho) float64 632kB dask.array<chunksize=(1, 162, 122), meta=np.ndarray>
    ubar                   (time, eta_rho, xi_u) float64 627kB dask.array<chunksize=(1, 162, 121), meta=np.ndarray>
    vbar                   (time, eta_v, xi_rho) float64 629kB dask.array<chunksize=(1, 161, 122), meta=np.ndarray>
    MARBL_PH_3D            (time, s_rho, eta_rho, xi_rho) float64 63MB dask.array<chunksize=(1, 100, 162, 122), meta=np.ndarray>
    ...                     ...
    u_slow                 (time, s_rho, eta_rho, xi_u) float64 63MB dask.array<chunksize=(1, 100, 162, 121), meta=np.ndarray>
    v_slow                 (time, s_rho, eta_v, xi_rho) float64 63MB dask.array<chunksize=(1, 100, 161, 122), meta=np.ndarray>
    p_slow                 (time, s_rho, eta_rho, xi_rho) float64 63MB dask.array<chunksize=(1, 100, 162, 122), meta=np.ndarray>
    mask_rho               (eta_rho, xi_rho) float64 158kB ...
    mask_u                 (eta_rho, xi_u) int32 78kB 1 1 1 1 1 1 ... 0 0 0 0 0
    mask_v                 (eta_v, xi_rho) int32 79kB 1 1 1 1 1 1 ... 0 0 0 0 0
Attributes: (12/35)
    title:                 eastpac25km , 25km resolution
    grid_file:             /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/ep...
    init_file:             /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastp...
    ntimes:                4610
    ndtfast:               45
    dt:                    600.0
    ...                    ...
    SRCS:                  SRCS $(shell ls *$(UPF77_ext)) SRCS : $(filter-out...
    CPPS:                  <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV...
    surf_forcing_strings:
    bc_options:             OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, O...
    git_version:
    type:                  ROMS restart file

ROMS output is provided on its native grid, which has dimensions eta, xi, and s, with horizontal and vertical staggering reflected in the _rho, _u, and _v suffixes. Plotting and analysis are best performed on this native grid, as demonstrated in this notebook.

However, if you prefer working on a latitude-longitude-depth (lat-lon-z) grid for your own analysis or storage, ROMS-Tools offers the .regrid() method, which transforms data from the native ROMS grid to a lat-lon-z grid.

[5]:

ds_regridded = roms_output.regrid()

[6]:

ds_regridded

[6]:

<xarray.Dataset> Size: 6GB
Dimensions:                (time: 4, lat: 185, lon: 197, depth: 100, xi_u: 121,
                            eta_v: 161, auxil: 6)
Coordinates:
  * time                   (time) datetime64[ns] 32B 1998-01-05T23:50:00 ... ...
  * lat                    (lat) float32 740B 7.0 7.25 7.5 ... 52.5 52.75 53.0
  * lon                    (lon) float32 788B 208.0 208.2 208.5 ... 256.8 257.0
  * depth                  (depth) float32 400B 1.46 4.45 ... 5.528e+03
Dimensions without coordinates: xi_u, eta_v, auxil
Data variables: (12/58)
    v_slow                 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    zooC                   (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    spC                    (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    hbls                   (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
    u_slow                 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    diazFe                 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    ...                     ...
    vbar                   (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
    DU_avg2                (time, xi_u, lat, lon) float64 141MB dask.array<chunksize=(1, 121, 185, 197), meta=np.ndarray>
    DONr                   (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    MARBL_PH_3D            (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    DON                    (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    diazChl                (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
Attributes: (12/36)
    title:                 eastpac25km , 25km resolution
    grid_file:             /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/ep...
    init_file:             /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastp...
    ntimes:                4610
    ndtfast:               45
    dt:                    600.0
    ...                    ...
    CPPS:                  <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV...
    surf_forcing_strings:
    bc_options:             OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, O...
    git_version:
    type:                  ROMS restart file
    regrid_method:         bilinear

As you can see, the data in ds_regridded is now on a lat-lon-z grid. Since we instantiated the ROMSOutput class with use_dask=True, the data in ds_regridded is lazily loaded (consisting of dask.arrays).

You can use xarray’s built-in plotting methods to visualize the data. However, we recommend plotting on the native ROMS grid, as shown in this notebook.

[7]:

%time ds_regridded["ALK"].isel(depth=0, time=0).plot()

CPU times: user 180 ms, sys: 28.8 ms, total: 208 ms
Wall time: 246 ms

[7]:

<matplotlib.collections.QuadMesh at 0x7f1a505ae270>
_images/regridding_roms_output_11_2.png 
[8]:

%time ds_regridded["u"].isel(depth=0, time=0).plot()

CPU times: user 246 ms, sys: 52.6 ms, total: 299 ms
Wall time: 353 ms

[8]:

<matplotlib.collections.QuadMesh at 0x7f1a5046f250>
_images/regridding_roms_output_12_2.png

You can also save the regridded dataset ds_regridded using xarray’s .to_netcdf() method. This may take some time, as the regridding computation is triggered only during the saving process because we are using dask.

[9]:

%time ds_regridded.to_netcdf("/pscratch/sd/n/nloose/output/regridded_output.nc")

CPU times: user 28.8 s, sys: 13.4 s, total: 42.1 s
Wall time: 27.8 s

Specifying the horizontal target resolution#

The .regrid() method has an optional parameter horizontal_resolution, which is the horizontal target resolution in degrees. If this parameter is not provided, ROMS-Tools automatically computes an approximate nominal resolution.

In the example above, ROMS-Tools selected a horizontal resolution of 0.25°, as confirmed below.

[10]:

ds_regridded.lon

[10]:

<xarray.DataArray 'lon' (lon: 197)> Size: 788B
array([208.  , 208.25, 208.5 , 208.75, 209.  , 209.25, 209.5 , 209.75, 210.  ,
       210.25, 210.5 , 210.75, 211.  , 211.25, 211.5 , 211.75, 212.  , 212.25,
       212.5 , 212.75, 213.  , 213.25, 213.5 , 213.75, 214.  , 214.25, 214.5 ,
       214.75, 215.  , 215.25, 215.5 , 215.75, 216.  , 216.25, 216.5 , 216.75,
       217.  , 217.25, 217.5 , 217.75, 218.  , 218.25, 218.5 , 218.75, 219.  ,
       219.25, 219.5 , 219.75, 220.  , 220.25, 220.5 , 220.75, 221.  , 221.25,
       221.5 , 221.75, 222.  , 222.25, 222.5 , 222.75, 223.  , 223.25, 223.5 ,
       223.75, 224.  , 224.25, 224.5 , 224.75, 225.  , 225.25, 225.5 , 225.75,
       226.  , 226.25, 226.5 , 226.75, 227.  , 227.25, 227.5 , 227.75, 228.  ,
       228.25, 228.5 , 228.75, 229.  , 229.25, 229.5 , 229.75, 230.  , 230.25,
       230.5 , 230.75, 231.  , 231.25, 231.5 , 231.75, 232.  , 232.25, 232.5 ,
       232.75, 233.  , 233.25, 233.5 , 233.75, 234.  , 234.25, 234.5 , 234.75,
       235.  , 235.25, 235.5 , 235.75, 236.  , 236.25, 236.5 , 236.75, 237.  ,
       237.25, 237.5 , 237.75, 238.  , 238.25, 238.5 , 238.75, 239.  , 239.25,
       239.5 , 239.75, 240.  , 240.25, 240.5 , 240.75, 241.  , 241.25, 241.5 ,
       241.75, 242.  , 242.25, 242.5 , 242.75, 243.  , 243.25, 243.5 , 243.75,
       244.  , 244.25, 244.5 , 244.75, 245.  , 245.25, 245.5 , 245.75, 246.  ,
       246.25, 246.5 , 246.75, 247.  , 247.25, 247.5 , 247.75, 248.  , 248.25,
       248.5 , 248.75, 249.  , 249.25, 249.5 , 249.75, 250.  , 250.25, 250.5 ,
       250.75, 251.  , 251.25, 251.5 , 251.75, 252.  , 252.25, 252.5 , 252.75,
       253.  , 253.25, 253.5 , 253.75, 254.  , 254.25, 254.5 , 254.75, 255.  ,
       255.25, 255.5 , 255.75, 256.  , 256.25, 256.5 , 256.75, 257.  ],
      dtype=float32)
Coordinates:
  * lon      (lon) float32 788B 208.0 208.2 208.5 208.8 ... 256.5 256.8 257.0
Attributes:
    long_name:  Longitude
    units:      Degrees East
[11]:

ds_regridded.lat

[11]:

<xarray.DataArray 'lat' (lat: 185)> Size: 740B
array([ 7.  ,  7.25,  7.5 ,  7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,
        9.5 ,  9.75, 10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75,
       12.  , 12.25, 12.5 , 12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25,
       14.5 , 14.75, 15.  , 15.25, 15.5 , 15.75, 16.  , 16.25, 16.5 , 16.75,
       17.  , 17.25, 17.5 , 17.75, 18.  , 18.25, 18.5 , 18.75, 19.  , 19.25,
       19.5 , 19.75, 20.  , 20.25, 20.5 , 20.75, 21.  , 21.25, 21.5 , 21.75,
       22.  , 22.25, 22.5 , 22.75, 23.  , 23.25, 23.5 , 23.75, 24.  , 24.25,
       24.5 , 24.75, 25.  , 25.25, 25.5 , 25.75, 26.  , 26.25, 26.5 , 26.75,
       27.  , 27.25, 27.5 , 27.75, 28.  , 28.25, 28.5 , 28.75, 29.  , 29.25,
       29.5 , 29.75, 30.  , 30.25, 30.5 , 30.75, 31.  , 31.25, 31.5 , 31.75,
       32.  , 32.25, 32.5 , 32.75, 33.  , 33.25, 33.5 , 33.75, 34.  , 34.25,
       34.5 , 34.75, 35.  , 35.25, 35.5 , 35.75, 36.  , 36.25, 36.5 , 36.75,
       37.  , 37.25, 37.5 , 37.75, 38.  , 38.25, 38.5 , 38.75, 39.  , 39.25,
       39.5 , 39.75, 40.  , 40.25, 40.5 , 40.75, 41.  , 41.25, 41.5 , 41.75,
       42.  , 42.25, 42.5 , 42.75, 43.  , 43.25, 43.5 , 43.75, 44.  , 44.25,
       44.5 , 44.75, 45.  , 45.25, 45.5 , 45.75, 46.  , 46.25, 46.5 , 46.75,
       47.  , 47.25, 47.5 , 47.75, 48.  , 48.25, 48.5 , 48.75, 49.  , 49.25,
       49.5 , 49.75, 50.  , 50.25, 50.5 , 50.75, 51.  , 51.25, 51.5 , 51.75,
       52.  , 52.25, 52.5 , 52.75, 53.  ], dtype=float32)
Coordinates:
  * lat      (lat) float32 740B 7.0 7.25 7.5 7.75 8.0 ... 52.25 52.5 52.75 53.0
Attributes:
    long_name:  Latitude
    units:      Degrees North

Next, we specify our own target resolution of 0.5°.

[12]:

ds_regridded_with_coarser_horizontal_resolution = roms_output.regrid(
    horizontal_resolution=0.5
)

[13]:

ds_regridded_with_coarser_horizontal_resolution

[13]:

<xarray.Dataset> Size: 1GB
Dimensions:                (time: 4, lat: 93, lon: 99, depth: 100, xi_u: 121,
                            eta_v: 161, auxil: 6)
Coordinates:
  * time                   (time) datetime64[ns] 32B 1998-01-05T23:50:00 ... ...
  * lat                    (lat) float32 372B 7.0 7.5 8.0 8.5 ... 52.0 52.5 53.0
  * lon                    (lon) float32 396B 208.0 208.5 209.0 ... 256.5 257.0
  * depth                  (depth) float32 400B 1.46 4.45 ... 5.528e+03
Dimensions without coordinates: xi_u, eta_v, auxil
Data variables: (12/58)
    v_slow                 (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    zooC                   (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    spC                    (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    hbls                   (time, lat, lon) float64 295kB dask.array<chunksize=(1, 93, 99), meta=np.ndarray>
    u_slow                 (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    diazFe                 (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    ...                     ...
    vbar                   (time, lat, lon) float64 295kB dask.array<chunksize=(1, 93, 99), meta=np.ndarray>
    DU_avg2                (time, xi_u, lat, lon) float64 36MB dask.array<chunksize=(1, 121, 93, 99), meta=np.ndarray>
    DONr                   (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    MARBL_PH_3D            (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    DON                    (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
    diazChl                (time, lat, lon, depth) float64 29MB dask.array<chunksize=(1, 93, 99, 100), meta=np.ndarray>
Attributes: (12/36)
    title:                 eastpac25km , 25km resolution
    grid_file:             /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/ep...
    init_file:             /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastp...
    ntimes:                4610
    ndtfast:               45
    dt:                    600.0
    ...                    ...
    CPPS:                  <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV...
    surf_forcing_strings:
    bc_options:             OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, O...
    git_version:
    type:                  ROMS restart file
    regrid_method:         bilinear

The horizontal resolution of the new dataset is now 0.5° as desired.

[14]:

ds_regridded_with_coarser_horizontal_resolution.lon

[14]:

<xarray.DataArray 'lon' (lon: 99)> Size: 396B
array([208. , 208.5, 209. , 209.5, 210. , 210.5, 211. , 211.5, 212. , 212.5,
       213. , 213.5, 214. , 214.5, 215. , 215.5, 216. , 216.5, 217. , 217.5,
       218. , 218.5, 219. , 219.5, 220. , 220.5, 221. , 221.5, 222. , 222.5,
       223. , 223.5, 224. , 224.5, 225. , 225.5, 226. , 226.5, 227. , 227.5,
       228. , 228.5, 229. , 229.5, 230. , 230.5, 231. , 231.5, 232. , 232.5,
       233. , 233.5, 234. , 234.5, 235. , 235.5, 236. , 236.5, 237. , 237.5,
       238. , 238.5, 239. , 239.5, 240. , 240.5, 241. , 241.5, 242. , 242.5,
       243. , 243.5, 244. , 244.5, 245. , 245.5, 246. , 246.5, 247. , 247.5,
       248. , 248.5, 249. , 249.5, 250. , 250.5, 251. , 251.5, 252. , 252.5,
       253. , 253.5, 254. , 254.5, 255. , 255.5, 256. , 256.5, 257. ],
      dtype=float32)
Coordinates:
  * lon      (lon) float32 396B 208.0 208.5 209.0 209.5 ... 256.0 256.5 257.0
Attributes:
    long_name:  Longitude
    units:      Degrees East
[15]:

ds_regridded_with_coarser_horizontal_resolution.lat

[15]:

<xarray.DataArray 'lat' (lat: 93)> Size: 372B
array([ 7. ,  7.5,  8. ,  8.5,  9. ,  9.5, 10. , 10.5, 11. , 11.5, 12. , 12.5,
       13. , 13.5, 14. , 14.5, 15. , 15.5, 16. , 16.5, 17. , 17.5, 18. , 18.5,
       19. , 19.5, 20. , 20.5, 21. , 21.5, 22. , 22.5, 23. , 23.5, 24. , 24.5,
       25. , 25.5, 26. , 26.5, 27. , 27.5, 28. , 28.5, 29. , 29.5, 30. , 30.5,
       31. , 31.5, 32. , 32.5, 33. , 33.5, 34. , 34.5, 35. , 35.5, 36. , 36.5,
       37. , 37.5, 38. , 38.5, 39. , 39.5, 40. , 40.5, 41. , 41.5, 42. , 42.5,
       43. , 43.5, 44. , 44.5, 45. , 45.5, 46. , 46.5, 47. , 47.5, 48. , 48.5,
       49. , 49.5, 50. , 50.5, 51. , 51.5, 52. , 52.5, 53. ], dtype=float32)
Coordinates:
  * lat      (lat) float32 372B 7.0 7.5 8.0 8.5 9.0 ... 51.0 51.5 52.0 52.5 53.0
Attributes:
    long_name:  Latitude
    units:      Degrees North

Note

Specifying a horizontal resolution much higher than the nominal resolution can lead to aliasing errors. It is generally advisable to stick with the default horizontal resolution by setting horizontal_resolution = None.

Specifying the target depth levels#

Similar to specifying the horizontal target resolution, you can also define the target depth levels using the optional depth_levels parameter. If this parameter is not provided, ROMS-Tools automatically computes the target vertical depth levels using an exponential profile, resulting in higher resolution near the surface and lower resolution at the bottom. The number of depth levels is automatically set to match the value of roms_output.ds.s_rho, which corresponds to the number of vertical layers in the native ROMS grid.

Here are the automatically chosen depth levels from the previous example.

[16]:

ds_regridded.depth

[16]:

<xarray.DataArray 'depth' (depth: 100)> Size: 400B
array([1.46000e+00, 4.45000e+00, 7.58000e+00, 1.08500e+01, 1.42700e+01,
       1.78500e+01, 2.16000e+01, 2.55200e+01, 2.96100e+01, 3.39000e+01,
       3.83800e+01, 4.30700e+01, 4.79800e+01, 5.31100e+01, 5.84800e+01,
       6.40900e+01, 6.99700e+01, 7.61100e+01, 8.25400e+01, 8.92600e+01,
       9.62900e+01, 1.03650e+02, 1.11340e+02, 1.19390e+02, 1.27810e+02,
       1.36610e+02, 1.45820e+02, 1.55460e+02, 1.65540e+02, 1.76080e+02,
       1.87110e+02, 1.98640e+02, 2.10710e+02, 2.23330e+02, 2.36530e+02,
       2.50340e+02, 2.64790e+02, 2.79900e+02, 2.95700e+02, 3.12240e+02,
       3.29530e+02, 3.47620e+02, 3.66550e+02, 3.86340e+02, 4.07050e+02,
       4.28710e+02, 4.51360e+02, 4.75060e+02, 4.99850e+02, 5.25780e+02,
       5.52900e+02, 5.81280e+02, 6.10950e+02, 6.42000e+02, 6.74470e+02,
       7.08440e+02, 7.43970e+02, 7.81140e+02, 8.20010e+02, 8.60680e+02,
       9.03220e+02, 9.47710e+02, 9.94260e+02, 1.04294e+03, 1.09387e+03,
       1.14714e+03, 1.20287e+03, 1.26116e+03, 1.32213e+03, 1.38591e+03,
       1.45262e+03, 1.52240e+03, 1.59540e+03, 1.67176e+03, 1.75163e+03,
       1.83517e+03, 1.92256e+03, 2.01398e+03, 2.10960e+03, 2.20962e+03,
       2.31425e+03, 2.42370e+03, 2.53818e+03, 2.65793e+03, 2.78319e+03,
       2.91422e+03, 3.05127e+03, 3.19464e+03, 3.34461e+03, 3.50147e+03,
       3.66556e+03, 3.83720e+03, 4.01674e+03, 4.20455e+03, 4.40100e+03,
       4.60649e+03, 4.82144e+03, 5.04629e+03, 5.28148e+03, 5.52750e+03],
      dtype=float32)
Coordinates:
  * depth    (depth) float32 400B 1.46 4.45 7.58 ... 5.281e+03 5.528e+03
Attributes:
    long_name:  Depth
    units:      m

Next, we specify our own target depth levels, which can be done using an xarray.DataArray, numpy.ndarray, or list.

Let’s specify target depth levels with 1m resolution for the upper 500m, and 500m resolution below that.

[17]:

import numpy as np

[18]:

target_depth_levels = np.concatenate((np.arange(0, 500), np.arange(500, 5000, 500)))

These are 509 depth levels in total!

[19]:

len(target_depth_levels)

[19]:

509

[20]:

ds_regridded_with_custom_vertical_depth_levels = roms_output.regrid(
    depth_levels=target_depth_levels
)

[21]:

ds_regridded_with_custom_vertical_depth_levels

[21]:

<xarray.Dataset> Size: 25GB
Dimensions:                (time: 4, lat: 185, lon: 197, depth: 509, xi_u: 121,
                            eta_v: 161, auxil: 6)
Coordinates:
  * time                   (time) datetime64[ns] 32B 1998-01-05T23:50:00 ... ...
  * lat                    (lat) float32 740B 7.0 7.25 7.5 ... 52.5 52.75 53.0
  * lon                    (lon) float32 788B 208.0 208.2 208.5 ... 256.8 257.0
  * depth                  (depth) float32 2kB 0.0 1.0 2.0 ... 4e+03 4.5e+03
Dimensions without coordinates: xi_u, eta_v, auxil
Data variables: (12/58)
    v_slow                 (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    zooC                   (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    spC                    (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    hbls                   (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
    u_slow                 (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    diazFe                 (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    ...                     ...
    vbar                   (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
    DU_avg2                (time, xi_u, lat, lon) float64 141MB dask.array<chunksize=(1, 121, 185, 197), meta=np.ndarray>
    DONr                   (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    MARBL_PH_3D            (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    DON                    (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
    diazChl                (time, lat, lon, depth) float64 594MB dask.array<chunksize=(1, 185, 197, 509), meta=np.ndarray>
Attributes: (12/36)
    title:                 eastpac25km , 25km resolution
    grid_file:             /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/ep...
    init_file:             /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastp...
    ntimes:                4610
    ndtfast:               45
    dt:                    600.0
    ...                    ...
    CPPS:                  <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV...
    surf_forcing_strings:
    bc_options:             OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, O...
    git_version:
    type:                  ROMS restart file
    regrid_method:         bilinear

You can see that the new dataset contains the 509 depth levels we specified.

Specifying the variables to be regridded#

Finally, we can specify a subset of the original variables to be regridded.

[22]:

ds_regridded_with_subset_of_variables = roms_output.regrid(
    var_names=["zeta", "temp", "salt", "u", "v", "ALK"]
)

[23]:

ds_regridded_with_subset_of_variables

[23]:

<xarray.Dataset> Size: 584MB
Dimensions:  (time: 4, lat: 185, lon: 197, depth: 100)
Coordinates:
  * time     (time) datetime64[ns] 32B 1998-01-05T23:50:00 ... 1999-02-01
  * lat      (lat) float32 740B 7.0 7.25 7.5 7.75 8.0 ... 52.25 52.5 52.75 53.0
  * lon      (lon) float32 788B 208.0 208.2 208.5 208.8 ... 256.5 256.8 257.0
  * depth    (depth) float32 400B 1.46 4.45 7.58 ... 5.281e+03 5.528e+03
Data variables:
    salt     (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    ALK      (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    temp     (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    zeta     (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
    u        (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
    v        (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
Attributes: (12/36)
    title:                 eastpac25km , 25km resolution
    grid_file:             /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/ep...
    init_file:             /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastp...
    ntimes:                4610
    ndtfast:               45
    dt:                    600.0
    ...                    ...
    CPPS:                  <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV...
    surf_forcing_strings:
    bc_options:             OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, O...
    git_version:
    type:                  ROMS restart file
    regrid_method:         bilinear
[ ]: