SFBOFS (Regular Grid 2D)

NOTE: currently Broken – seems not to be auto-detecting the grid?

# Install required libraries
# !pip install -q xarray_subset_grid@git+https://github.com/asascience-open/xarray-subset-grid.git
# !pip install -q s3fs cftime xarray cf-xarray fsspec dask h5netcdf

import cf_xarray #noqa
import fsspec
import numpy as np
import xarray as xr


# Open the dataset from NODD s3 bucket directly
fs = fsspec.filesystem("s3", anon=True)
ds = xr.open_dataset(
    fs.open(
        "s3://noaa-nos-ofs-pds/sfbofs/netcdf/202407/sfbofs.t21z.20240722.regulargrid.f040.nc"
    ),
    chunks={},
    drop_variables=[]
)

ds

<xarray.Dataset> Size: 68MB
Dimensions:      (Depth: 21, ny: 329, nx: 553, time: 1)
Coordinates:
  * Depth        (Depth) float64 168B 0.0 1.0 2.0 4.0 ... 70.0 80.0 90.0 100.0
    Latitude     (ny, nx) float64 1MB dask.array<chunksize=(329, 553), meta=np.ndarray>
    Longitude    (ny, nx) float64 1MB dask.array<chunksize=(329, 553), meta=np.ndarray>
  * time         (time) datetime64[ns] 8B 2024-07-24T12:59:59.999999744
Dimensions without coordinates: ny, nx
Data variables:
    h            (ny, nx) float64 1MB dask.array<chunksize=(329, 553), meta=np.ndarray>
    mask         (ny, nx) float64 1MB dask.array<chunksize=(329, 553), meta=np.ndarray>
    Times        (time) |S26 26B dask.array<chunksize=(1,), meta=np.ndarray>
    zeta         (time, ny, nx) float32 728kB dask.array<chunksize=(1, 329, 553), meta=np.ndarray>
    zetatomllw   (time, ny, nx) float32 728kB dask.array<chunksize=(1, 329, 553), meta=np.ndarray>
    u_eastward   (time, Depth, ny, nx) float32 15MB dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>
    v_northward  (time, Depth, ny, nx) float32 15MB dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>
    temp         (time, Depth, ny, nx) float32 15MB dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>
    salt         (time, Depth, ny, nx) float32 15MB dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>
Attributes: (12/14)
    title:                       SFBOFS
    institution:                 School for Marine Science and Technology
    source:                      FVCOM_4.4.7
    history:                     model started at: 22/07/2024   21:33
    references:                  http://fvcom.smast.umassd.edu, https://githu...
    Conventions:                 CF-1.0
    ...                          ...
    Tidal_Forcing:               Tidal Forcing Time Series Title: sfbofs late...
    River_Forcing:               THERE ARE 9 RIVERS IN THIS MODEL.\nRIVER INF...
    GroundWater_Forcing:         GROUND WATER FORCING IS OFF!
    Surface_Heat_Forcing:        FVCOM variable surface heat forcing file:\nF...
    Surface_Wind_Forcing:        FVCOM variable surface Wind forcing:\nFILE N...
    Surface_PrecipEvap_Forcing:  SURFACE PRECIPITATION FORCING IS OFF

xarray.Dataset

• Dimensions:
  • Depth: 21
  • ny: 329
  • nx: 553
  • time: 1
• Coordinates: (4)
  • Depth
    (Depth)
    float64
    0.0 1.0 2.0 4.0 ... 80.0 90.0 100.0

    long_name :

    Depths of Standard Layer

    standard_name :

    depth

    units :

    m

    positive :

    down

    axis :

    Z

    array([  0.,   1.,   2.,   4.,   6.,   8.,  10.,  12.,  15.,  20.,  25.,  30.,
            35.,  40.,  45.,  50.,  60.,  70.,  80.,  90., 100.])

  • Latitude
    (ny, nx)
    float64
    dask.array<chunksize=(329, 553), meta=np.ndarray>

    long_name :

    Latitude in common grid

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 1.39 MiB 1.39 MiB Shape (329, 553) (329, 553) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Longitude
    (ny, nx)
    float64
    dask.array<chunksize=(329, 553), meta=np.ndarray>

    long_name :

    Longitude in common grid

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 1.39 MiB 1.39 MiB Shape (329, 553) (329, 553) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    datetime64[ns]
    2024-07-24T12:59:59.999999744

    long_name :

    time since initialization

    field :

    time, scalar, series

    array(['2024-07-24T12:59:59.999999744'], dtype='datetime64[ns]')

• Data variables: (9)
  • h
    (ny, nx)
    float64
    dask.array<chunksize=(329, 553), meta=np.ndarray>

    long_name :

    bathymetry

    standard_name :

    sea_floor_depth_below_mean_sea_level

    units :

    meter

    positive :

    down

    grid :

    grid

    location :

    face

    field :

    bath, scaler

    Array Chunk Bytes 1.39 MiB 1.39 MiB Shape (329, 553) (329, 553) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask
    (ny, nx)
    float64
    dask.array<chunksize=(329, 553), meta=np.ndarray>

    long_name :

    mask

    standard_name :

    sea_binary_mask

    flag_values :

    [0. 1.]

    flag_meanings :

    land, water

    grid :

    grid

    location :

    face

    Array Chunk Bytes 1.39 MiB 1.39 MiB Shape (329, 553) (329, 553) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Times
    (time)
    |S26
    dask.array<chunksize=(1,), meta=np.ndarray>

    time_zone :

    UTC

    Array Chunk Bytes 26 B 26 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type |S26 numpy.ndarray

  • zeta
    (time, ny, nx)
    float32
    dask.array<chunksize=(1, 329, 553), meta=np.ndarray>

    long_name :

    free-surface referenced to mean sea level

    standard_name :

    sea_surface_elevation

    units :

    meters

    time :

    time

    grid :

    grid

    location :

    face

    field :

    free-surface, scalar, series

    Array Chunk Bytes 710.69 kiB 710.69 kiB Shape (1, 329, 553) (1, 329, 553) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • zetatomllw
    (time, ny, nx)
    float32
    dask.array<chunksize=(1, 329, 553), meta=np.ndarray>

    long_name :

    free-surface referenced to mean lower low water

    units :

    meters

    time :

    time

    grid :

    grid

    location :

    face

    field :

    free-surface, scalar, series

    Array Chunk Bytes 710.69 kiB 710.69 kiB Shape (1, 329, 553) (1, 329, 553) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_eastward
    (time, Depth, ny, nx)
    float32
    dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>

    long_name :

    eastward momentum component

    standard_name :

    eastward_sea_water_velocity

    units :

    meters second-1

    time :

    time

    grid :

    grid

    location :

    face

    field :

    u_eastward, scalar, series

    Array Chunk Bytes 14.57 MiB 1.92 MiB Shape (1, 21, 329, 553) (1, 11, 165, 277) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_northward
    (time, Depth, ny, nx)
    float32
    dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>

    long_name :

    northward momentum component

    standard_name :

    northward_sea_water_velocity

    units :

    meters second-1

    time :

    time

    grid :

    grid

    location :

    face

    field :

    v_northward, scalar, series

    Array Chunk Bytes 14.57 MiB 1.92 MiB Shape (1, 21, 329, 553) (1, 11, 165, 277) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temp
    (time, Depth, ny, nx)
    float32
    dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>

    long_name :

    potential temperature

    standard_name :

    sea_water_temperature

    units :

    Celsius

    time :

    time

    grid :

    grid

    location :

    face

    field :

    temperature, scalar, series

    Array Chunk Bytes 14.57 MiB 1.92 MiB Shape (1, 21, 329, 553) (1, 11, 165, 277) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • salt
    (time, Depth, ny, nx)
    float32
    dask.array<chunksize=(1, 11, 165, 277), meta=np.ndarray>

    long_name :

    salinity

    standard_name :

    sea_water_salinity

    units :

    PSU

    time :

    time

    grid :

    grid

    location :

    face

    field :

    salinity, scalar, series

    Array Chunk Bytes 14.57 MiB 1.92 MiB Shape (1, 21, 329, 553) (1, 11, 165, 277) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • Depth
    PandasIndex

    PandasIndex(Index([  0.0,   1.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  15.0,  20.0,
            25.0,  30.0,  35.0,  40.0,  45.0,  50.0,  60.0,  70.0,  80.0,  90.0,
           100.0],
          dtype='float64', name='Depth'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-07-24 12:59:59.999999744'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (14)

  title :

  SFBOFS

  institution :

  School for Marine Science and Technology

  source :

  FVCOM_4.4.7

  history :

  model started at: 22/07/2024 21:33

  references :

  http://fvcom.smast.umassd.edu, https://github.com/FVCOM-GitHub/FVCOM

  Conventions :

  CF-1.0

  CoordinateSystem :

  GeoReferenced

  CoordinateProjection :

  init=nad83:402

  Tidal_Forcing :

  Tidal Forcing Time Series Title: sfbofs lateral open boundary netCDF file

  River_Forcing :

  THERE ARE 9 RIVERS IN THIS MODEL. RIVER INFLOW IS ON THE nodes WHERE TEMPERATURE AND SALINITY ARE calculated IN THE MODEL. THE FOLLOWING RIVER NAMES ARE USED: 11459150 11180700 11458000 11458000 11458000 11458000 11458000 11172175 11169025

  GroundWater_Forcing :

  GROUND WATER FORCING IS OFF!

  Surface_Heat_Forcing :

  FVCOM variable surface heat forcing file: FILE NAME:sfbofs.t21z.20240722.hflux.forecast.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format

  Surface_Wind_Forcing :

  FVCOM variable surface Wind forcing: FILE NAME:sfbofs.t21z.20240722.met.forecast.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format

  Surface_PrecipEvap_Forcing :

  SURFACE PRECIPITATION FORCING IS OFF

First, check to make sure the dataset is recognized as a 2d regular grid

ds.xsg.grid.name

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[3], line 1
----> 1 ds.xsg.grid.name

File ~/Hazmat/ThirdPartyPackages/xarray-subset-grid/.pixi/envs/all/lib/python3.12/site-packages/xarray/core/common.py:300, in AttrAccessMixin.__getattr__(self, name)
    298         with suppress(KeyError):
    299             return source[name]
--> 300 raise AttributeError(
    301     f"{type(self).__name__!r} object has no attribute {name!r}"
    302 )

AttributeError: 'Dataset' object has no attribute 'xsg'

We can subset down to the variables we care about, while keeping the grid information for later analysis

ds_currents = ds.xsg.subset_vars(['u_eastward', 'v_northward'])
ds_currents

Now subset to the surface

ds_surface_currents = ds_currents.xsg.subset_vertical_level(0)
ds_surface_currents

Finally subset to the target region

polygon = np.array(
    [
        [-122.68750983577246, 37.8236386694975],
        [-122.68750983577246, 37.65884889475059],
        [-122.44817616264763, 37.65884889475059],
        [-122.44817616264763, 37.8236386694975],
        [-122.68750983577246, 37.8236386694975],
    ]
)

ds_currents_surface_subset = ds_surface_currents.xsg.subset_polygon(polygon)
ds_currents_surface_subset

Plot and see the result!

ds_currents_surface_subset.u_eastward.cf.isel(time=0).cf.plot(
    x="longitude", y="latitude", cmap="viridis"
)

f"Subset dataset size: {ds_currents_surface_subset.nbytes * 1.0e-6} Mb"