FVCOM 3D¶
# 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
import xarray_subset_grid.accessor # noqa: F401
# open dataset as zarr object using fsspec reference file system and xarray
fs = fsspec.filesystem(
"reference",
fo="s3://nextgen-dmac-cloud-ingest/nos/ngofs2/nos.ngofs2.fields.best.nc.zarr",
remote_protocol="s3",
remote_options={"anon": True},
target_protocol="s3",
target_options={"anon": True},
)
m = fs.get_mapper("")
ds = xr.open_dataset(
m,
engine="zarr",
backend_kwargs=dict(consolidated=False),
chunks={'time': 1},
drop_variables=["Itime2"],
)
ds
<xarray.Dataset> Size: 113GB
Dimensions: (time: 255, four: 4, nele: 569405, node: 303714,
three: 3, maxnode: 10, maxelem: 8, siglev: 41,
siglay: 40)
Coordinates:
lat (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
latc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
lon (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
lonc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
siglay (siglay, node) float32 49MB dask.array<chunksize=(40, 303714), meta=np.ndarray>
siglev (siglev, node) float32 50MB dask.array<chunksize=(41, 303714), meta=np.ndarray>
* time (time) datetime64[ns] 2kB 2024-06-18T21:00:00 ... 202...
Dimensions without coordinates: four, nele, node, three, maxnode, maxelem
Data variables: (12/42)
Itime (time) datetime64[ns] 2kB dask.array<chunksize=(1,), meta=np.ndarray>
Times (time) |S26 7kB dask.array<chunksize=(1,), meta=np.ndarray>
a1u (four, nele) float32 9MB dask.array<chunksize=(4, 569405), meta=np.ndarray>
a2u (four, nele) float32 9MB dask.array<chunksize=(4, 569405), meta=np.ndarray>
art1 (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
art2 (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
... ...
ww (time, siglay, nele) float32 23GB dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
x (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
xc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
y (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
yc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
zeta (time, node) float32 310MB dask.array<chunksize=(1, 303714), meta=np.ndarray>
Attributes: (12/14)
Conventions: CF-1.0
CoordinateProjection: init=nad83:4205
CoordinateSystem: GeoReferenced
GroundWater_Forcing: GROUND WATER FORCING IS OFF!
River_Forcing: THERE ARE 63 RIVERS IN THIS MODEL.\nRIVER IN...
Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\nF...
... ...
Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!
history: model started at: 18/06/2024 21:15
institution: School for Marine Science and Technology
references: http://fvcom.smast.umassd.edu, http://codfis...
source: FVCOM_4.3
title: NGOFS2xarray.Dataset
- time: 255
- four: 4
- nele: 569405
- node: 303714
- three: 3
- maxnode: 10
- maxelem: 8
- siglev: 41
- siglay: 40
- lat(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - latc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lonc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - siglay(siglay, node)float32dask.array<chunksize=(40, 303714), meta=np.ndarray>
- formula_terms :
- sigma: siglay eta: zeta depth: h
- long_name :
- Sigma Layers
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 46.34 MiB 46.34 MiB Shape (40, 303714) (40, 303714) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - siglev(siglev, node)float32dask.array<chunksize=(41, 303714), meta=np.ndarray>
- formula_terms :
- sigma:siglay eta: zeta depth: h
- long_name :
- Sigma Levels
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 47.50 MiB 47.50 MiB Shape (41, 303714) (41, 303714) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]2024-06-18T21:00:00 ... 2024-07-...
- format :
- defined reference date
- long_name :
- time
- time_zone :
- UTC
array(['2024-06-18T21:00:00.000000000', '2024-06-19T00:00:00.000000000', '2024-06-19T03:00:00.000000000', ..., '2024-07-20T09:00:00.000000000', '2024-07-20T12:00:00.000000000', '2024-07-20T15:00:00.000000000'], dtype='datetime64[ns]')
- Itime(time)datetime64[ns]dask.array<chunksize=(1,), meta=np.ndarray>
- format :
- defined reference date
- time_zone :
- UTC
Array Chunk Bytes 1.99 kiB 8 B Shape (255,) (1,) Dask graph 255 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray - Times(time)|S26dask.array<chunksize=(1,), meta=np.ndarray>
- time_zone :
- UTC
Array Chunk Bytes 6.47 kiB 26 B Shape (255,) (1,) Dask graph 255 chunks in 2 graph layers Data type |S26 numpy.ndarray - a1u(four, nele)float32dask.array<chunksize=(4, 569405), meta=np.ndarray>
- long_name :
- a1u
Array Chunk Bytes 8.69 MiB 8.69 MiB Shape (4, 569405) (4, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - a2u(four, nele)float32dask.array<chunksize=(4, 569405), meta=np.ndarray>
- long_name :
- a2u
Array Chunk Bytes 8.69 MiB 8.69 MiB Shape (4, 569405) (4, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - art1(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- Area of Node-Base Control volume
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - art2(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- Area of elements around a node
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - atmos_press(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Atmospheric Pressure
- type :
- data
- units :
- pascals
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - aw0(three, nele)float32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- aw0
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - awx(three, nele)float32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- awx
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - awy(three, nele)float32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- awy
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - fvcom_mesh()int32...
- cf_role :
- mesh_topology
- topology_dimension :
- 2
- node_coordinates :
- lon lat
- face_coordinates :
- lonc latc
- face_node_connectivity :
- nv
- face_face_connectivity :
- nbe
[1 values with dtype=int32]
- h(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- grid :
- Bathymetry_Mesh
- long_name :
- Bathymetry
- positive :
- down
- standard_name :
- sea_floor_depth_below_geoid
- type :
- data
- units :
- m
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - iint(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- internal mode iteration number
Array Chunk Bytes 1.00 kiB 4 B Shape (255,) (1,) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - nbe(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- elements surrounding each element
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nbsn(maxnode, node)int32dask.array<chunksize=(10, 303714), meta=np.ndarray>
- long_name :
- nodes surrounding each node
Array Chunk Bytes 11.59 MiB 11.59 MiB Shape (10, 303714) (10, 303714) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nbve(maxelem, node)int32dask.array<chunksize=(8, 303714), meta=np.ndarray>
- long_name :
- elems surrounding each node
Array Chunk Bytes 9.27 MiB 9.27 MiB Shape (8, 303714) (8, 303714) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - net_heat_flux(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Surface Net Heat Flux
- mesh :
- fvcom_mesh
- type :
- data
- units :
- W m-2
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - nprocs()int32...
- long_name :
- number of processors
[1 values with dtype=int32]
- ntsn(node)int32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- #nodes surrounding each node
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - ntve(node)int32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- #elems surrounding each node
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nv(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- nodes surrounding element
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - omega(time, siglev, node)float32dask.array<chunksize=(1, 41, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Vertical Sigma Coordinate Velocity
- type :
- data
- units :
- s-1
Array Chunk Bytes 11.83 GiB 47.50 MiB Shape (255, 41, 303714) (1, 41, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - partition(nele)int32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- partition
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - salinity(time, siglay, node)float32dask.array<chunksize=(1, 40, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- salinity
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_salinity
- type :
- data
- units :
- 1e-3
Array Chunk Bytes 11.54 GiB 46.34 MiB Shape (255, 40, 303714) (1, 40, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - short_wave(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Short Wave Radiation
- mesh :
- fvcom_mesh
- type :
- data
- units :
- W m-2
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - tauc(time, nele)float32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- bed stress magnitude from currents
- mesh :
- fvcom_mesh
- note1 :
- this stress is bottom boundary condtion on velocity field
- note2 :
- dimensions are stress/rho
- type :
- data
- units :
- m^2 s^-2
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - temp(time, siglay, node)float32dask.array<chunksize=(1, 40, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- temperature
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_temperature
- type :
- data
- units :
- degrees_C
Array Chunk Bytes 11.54 GiB 46.34 MiB Shape (255, 40, 303714) (1, 40, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - u(time, siglay, nele)float32dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Eastward Water Velocity
- mesh :
- fvcom_mesh
- standard_name :
- eastward_sea_water_velocity
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 21.64 GiB 86.88 MiB Shape (255, 40, 569405) (1, 40, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - uwind_speed(time, nele)float32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Eastward Wind Velocity
- mesh :
- fvcom_mesh
- standard_name :
- eastward wind
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - v(time, siglay, nele)float32dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Northward Water Velocity
- mesh :
- fvcom_mesh
- standard_name :
- Northward_sea_water_velocity
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 21.64 GiB 86.88 MiB Shape (255, 40, 569405) (1, 40, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - vwind_speed(time, nele)float32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Northward Wind Velocity
- mesh :
- fvcom_mesh
- standard_name :
- northward wind
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - wet_cells(time, nele)int32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Wet_Cells
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_cells_prev_ext(time, nele)int32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Wet_Cells_At_Previous_External_Step
- type :
- data
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_cells_prev_int(time, nele)int32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Wet_Cells_At_Previous_Internal_Step
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_nodes(time, node)int32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Wet_Nodes
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_nodes_prev_int(time, node)int32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Wet_Nodes_At_Previous_Internal_Step
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - ww(time, siglay, nele)float32dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Upward Water Velocity
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 21.64 GiB 86.88 MiB Shape (255, 40, 569405) (1, 40, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - x(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal x-coordinate
- units :
- meters
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - xc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal x-coordinate
- units :
- meters
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - y(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal y-coordinate
- units :
- meters
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - yc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal y-coordinate
- units :
- meters
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - zeta(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- Bathymetry_Mesh
- location :
- node
- long_name :
- Water Surface Elevation
- positive :
- up
- standard_name :
- sea_surface_height_above_geoid
- type :
- data
- units :
- meters
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray
- timePandasIndex
PandasIndex(DatetimeIndex(['2024-06-18 21:00:00', '2024-06-19 00:00:00', '2024-06-19 03:00:00', '2024-06-19 06:00:00', '2024-06-19 09:00:00', '2024-06-19 12:00:00', '2024-06-19 15:00:00', '2024-06-19 18:00:00', '2024-06-19 21:00:00', '2024-06-20 00:00:00', ... '2024-07-19 12:00:00', '2024-07-19 15:00:00', '2024-07-19 18:00:00', '2024-07-19 21:00:00', '2024-07-20 00:00:00', '2024-07-20 03:00:00', '2024-07-20 06:00:00', '2024-07-20 09:00:00', '2024-07-20 12:00:00', '2024-07-20 15:00:00'], dtype='datetime64[ns]', name='time', length=255, freq=None))
- Conventions :
- CF-1.0
- CoordinateProjection :
- init=nad83:4205
- CoordinateSystem :
- GeoReferenced
- GroundWater_Forcing :
- GROUND WATER FORCING IS OFF!
- River_Forcing :
- THERE ARE 63 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: 08012000 02365500 02375500 02489500 02492000 08211200 08211200 08211200 02481510 02481510 02481510 02479000 02479000 02479000 02479000 08164800 08164800 08164000 08164000 08188800 08188800 08188800 08066500 08066500 02479560 02479560 02479560 02479560 07375500 07375500 08030500 08030500 08069000 08069000 08069000 07381600 07381600 07381600 07381600 02470629 02470629 02470629 02470629 02471019 02471019 02471019 02471019 02471019 08076000 08076000 08075400 08041780 08015500 08075000 08075000 07374000 07374000 07374000 07374000 08162500 08116650 02376500 02368000
- Surface_Heat_Forcing :
- FVCOM variable surface heat forcing file: FILE NAME:nos.ngofs2.hflux.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Surface_PrecipEvap_Forcing :
- SURFACE PRECIPITATION FORCING IS OFF
- Surface_Wind_Forcing :
- FVCOM variable surface Wind forcing: FILE NAME:nos.ngofs2.met.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Tidal_Forcing :
- TIDAL ELEVATION FORCING IS OFF!
- history :
- model started at: 18/06/2024 21:15
- institution :
- School for Marine Science and Technology
- references :
- http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu
- source :
- FVCOM_4.3
- title :
- NGOFS2
Fixup any inconsistencies with regards to the datasets UGRID specification
ds = xarray_subset_grid.grids.ugrid.assign_ugrid_topology(ds)
ds
<xarray.Dataset> Size: 113GB
Dimensions: (time: 255, four: 4, nele: 569405, node: 303714,
three: 3, maxnode: 10, maxelem: 8, siglev: 41,
siglay: 40)
Coordinates:
lat (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
latc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
lon (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
lonc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
siglay (siglay, node) float32 49MB dask.array<chunksize=(40, 303714), meta=np.ndarray>
siglev (siglev, node) float32 50MB dask.array<chunksize=(41, 303714), meta=np.ndarray>
* time (time) datetime64[ns] 2kB 2024-06-18T21:00:00 ... 202...
Dimensions without coordinates: four, nele, node, three, maxnode, maxelem
Data variables: (12/42)
Itime (time) datetime64[ns] 2kB dask.array<chunksize=(1,), meta=np.ndarray>
Times (time) |S26 7kB dask.array<chunksize=(1,), meta=np.ndarray>
a1u (four, nele) float32 9MB dask.array<chunksize=(4, 569405), meta=np.ndarray>
a2u (four, nele) float32 9MB dask.array<chunksize=(4, 569405), meta=np.ndarray>
art1 (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
art2 (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
... ...
ww (time, siglay, nele) float32 23GB dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
x (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
xc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
y (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
yc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
zeta (time, node) float32 310MB dask.array<chunksize=(1, 303714), meta=np.ndarray>
Attributes: (12/14)
Conventions: CF-1.0
CoordinateProjection: init=nad83:4205
CoordinateSystem: GeoReferenced
GroundWater_Forcing: GROUND WATER FORCING IS OFF!
River_Forcing: THERE ARE 63 RIVERS IN THIS MODEL.\nRIVER IN...
Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\nF...
... ...
Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!
history: model started at: 18/06/2024 21:15
institution: School for Marine Science and Technology
references: http://fvcom.smast.umassd.edu, http://codfis...
source: FVCOM_4.3
title: NGOFS2xarray.Dataset
- time: 255
- four: 4
- nele: 569405
- node: 303714
- three: 3
- maxnode: 10
- maxelem: 8
- siglev: 41
- siglay: 40
- lat(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - latc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lonc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - siglay(siglay, node)float32dask.array<chunksize=(40, 303714), meta=np.ndarray>
- formula_terms :
- sigma: siglay eta: zeta depth: h
- long_name :
- Sigma Layers
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 46.34 MiB 46.34 MiB Shape (40, 303714) (40, 303714) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - siglev(siglev, node)float32dask.array<chunksize=(41, 303714), meta=np.ndarray>
- formula_terms :
- sigma:siglay eta: zeta depth: h
- long_name :
- Sigma Levels
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 47.50 MiB 47.50 MiB Shape (41, 303714) (41, 303714) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]2024-06-18T21:00:00 ... 2024-07-...
- format :
- defined reference date
- long_name :
- time
- time_zone :
- UTC
array(['2024-06-18T21:00:00.000000000', '2024-06-19T00:00:00.000000000', '2024-06-19T03:00:00.000000000', ..., '2024-07-20T09:00:00.000000000', '2024-07-20T12:00:00.000000000', '2024-07-20T15:00:00.000000000'], dtype='datetime64[ns]')
- Itime(time)datetime64[ns]dask.array<chunksize=(1,), meta=np.ndarray>
- format :
- defined reference date
- time_zone :
- UTC
Array Chunk Bytes 1.99 kiB 8 B Shape (255,) (1,) Dask graph 255 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray - Times(time)|S26dask.array<chunksize=(1,), meta=np.ndarray>
- time_zone :
- UTC
Array Chunk Bytes 6.47 kiB 26 B Shape (255,) (1,) Dask graph 255 chunks in 2 graph layers Data type |S26 numpy.ndarray - a1u(four, nele)float32dask.array<chunksize=(4, 569405), meta=np.ndarray>
- long_name :
- a1u
Array Chunk Bytes 8.69 MiB 8.69 MiB Shape (4, 569405) (4, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - a2u(four, nele)float32dask.array<chunksize=(4, 569405), meta=np.ndarray>
- long_name :
- a2u
Array Chunk Bytes 8.69 MiB 8.69 MiB Shape (4, 569405) (4, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - art1(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- Area of Node-Base Control volume
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - art2(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- Area of elements around a node
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - atmos_press(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Atmospheric Pressure
- type :
- data
- units :
- pascals
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - aw0(three, nele)float32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- aw0
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - awx(three, nele)float32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- awx
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - awy(three, nele)float32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- awy
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - fvcom_mesh()int32...
- cf_role :
- mesh_topology
- topology_dimension :
- 2
- node_coordinates :
- lon lat
- face_coordinates :
- lonc latc
- face_node_connectivity :
- nv
- face_face_connectivity :
- nbe
- face_dimension :
- nele
[1 values with dtype=int32]
- h(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- grid :
- Bathymetry_Mesh
- long_name :
- Bathymetry
- positive :
- down
- standard_name :
- sea_floor_depth_below_geoid
- type :
- data
- units :
- m
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - iint(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- internal mode iteration number
Array Chunk Bytes 1.00 kiB 4 B Shape (255,) (1,) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - nbe(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- elements surrounding each element
- start_index :
- 1
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nbsn(maxnode, node)int32dask.array<chunksize=(10, 303714), meta=np.ndarray>
- long_name :
- nodes surrounding each node
Array Chunk Bytes 11.59 MiB 11.59 MiB Shape (10, 303714) (10, 303714) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nbve(maxelem, node)int32dask.array<chunksize=(8, 303714), meta=np.ndarray>
- long_name :
- elems surrounding each node
Array Chunk Bytes 9.27 MiB 9.27 MiB Shape (8, 303714) (8, 303714) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - net_heat_flux(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Surface Net Heat Flux
- mesh :
- fvcom_mesh
- type :
- data
- units :
- W m-2
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - nprocs()int32...
- long_name :
- number of processors
[1 values with dtype=int32]
- ntsn(node)int32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- #nodes surrounding each node
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - ntve(node)int32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- #elems surrounding each node
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nv(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- nodes surrounding element
- start_index :
- 1
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - omega(time, siglev, node)float32dask.array<chunksize=(1, 41, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Vertical Sigma Coordinate Velocity
- type :
- data
- units :
- s-1
Array Chunk Bytes 11.83 GiB 47.50 MiB Shape (255, 41, 303714) (1, 41, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - partition(nele)int32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- partition
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - salinity(time, siglay, node)float32dask.array<chunksize=(1, 40, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- salinity
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_salinity
- type :
- data
- units :
- 1e-3
Array Chunk Bytes 11.54 GiB 46.34 MiB Shape (255, 40, 303714) (1, 40, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - short_wave(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Short Wave Radiation
- mesh :
- fvcom_mesh
- type :
- data
- units :
- W m-2
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - tauc(time, nele)float32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- bed stress magnitude from currents
- mesh :
- fvcom_mesh
- note1 :
- this stress is bottom boundary condtion on velocity field
- note2 :
- dimensions are stress/rho
- type :
- data
- units :
- m^2 s^-2
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - temp(time, siglay, node)float32dask.array<chunksize=(1, 40, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- temperature
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_temperature
- type :
- data
- units :
- degrees_C
Array Chunk Bytes 11.54 GiB 46.34 MiB Shape (255, 40, 303714) (1, 40, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - u(time, siglay, nele)float32dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Eastward Water Velocity
- mesh :
- fvcom_mesh
- standard_name :
- eastward_sea_water_velocity
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 21.64 GiB 86.88 MiB Shape (255, 40, 569405) (1, 40, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - uwind_speed(time, nele)float32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Eastward Wind Velocity
- mesh :
- fvcom_mesh
- standard_name :
- eastward wind
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - v(time, siglay, nele)float32dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Northward Water Velocity
- mesh :
- fvcom_mesh
- standard_name :
- Northward_sea_water_velocity
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 21.64 GiB 86.88 MiB Shape (255, 40, 569405) (1, 40, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - vwind_speed(time, nele)float32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Northward Wind Velocity
- mesh :
- fvcom_mesh
- standard_name :
- northward wind
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - wet_cells(time, nele)int32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Wet_Cells
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_cells_prev_ext(time, nele)int32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Wet_Cells_At_Previous_External_Step
- type :
- data
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_cells_prev_int(time, nele)int32dask.array<chunksize=(1, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- face
- long_name :
- Wet_Cells_At_Previous_Internal_Step
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 553.89 MiB 2.17 MiB Shape (255, 569405) (1, 569405) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_nodes(time, node)int32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Wet_Nodes
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - wet_nodes_prev_int(time, node)int32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- Wet_Nodes_At_Previous_Internal_Step
- mesh :
- fvcom_mesh
- type :
- data
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type int32 numpy.ndarray - ww(time, siglay, nele)float32dask.array<chunksize=(1, 40, 569405), meta=np.ndarray>
- grid :
- fvcom_grid
- long_name :
- Upward Water Velocity
- type :
- data
- units :
- meters s-1
Array Chunk Bytes 21.64 GiB 86.88 MiB Shape (255, 40, 569405) (1, 40, 569405) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray - x(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal x-coordinate
- units :
- meters
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - xc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal x-coordinate
- units :
- meters
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - y(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal y-coordinate
- units :
- meters
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - yc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal y-coordinate
- units :
- meters
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - zeta(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- Bathymetry_Mesh
- location :
- node
- long_name :
- Water Surface Elevation
- positive :
- up
- standard_name :
- sea_surface_height_above_geoid
- type :
- data
- units :
- meters
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray
- timePandasIndex
PandasIndex(DatetimeIndex(['2024-06-18 21:00:00', '2024-06-19 00:00:00', '2024-06-19 03:00:00', '2024-06-19 06:00:00', '2024-06-19 09:00:00', '2024-06-19 12:00:00', '2024-06-19 15:00:00', '2024-06-19 18:00:00', '2024-06-19 21:00:00', '2024-06-20 00:00:00', ... '2024-07-19 12:00:00', '2024-07-19 15:00:00', '2024-07-19 18:00:00', '2024-07-19 21:00:00', '2024-07-20 00:00:00', '2024-07-20 03:00:00', '2024-07-20 06:00:00', '2024-07-20 09:00:00', '2024-07-20 12:00:00', '2024-07-20 15:00:00'], dtype='datetime64[ns]', name='time', length=255, freq=None))
- Conventions :
- CF-1.0
- CoordinateProjection :
- init=nad83:4205
- CoordinateSystem :
- GeoReferenced
- GroundWater_Forcing :
- GROUND WATER FORCING IS OFF!
- River_Forcing :
- THERE ARE 63 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: 08012000 02365500 02375500 02489500 02492000 08211200 08211200 08211200 02481510 02481510 02481510 02479000 02479000 02479000 02479000 08164800 08164800 08164000 08164000 08188800 08188800 08188800 08066500 08066500 02479560 02479560 02479560 02479560 07375500 07375500 08030500 08030500 08069000 08069000 08069000 07381600 07381600 07381600 07381600 02470629 02470629 02470629 02470629 02471019 02471019 02471019 02471019 02471019 08076000 08076000 08075400 08041780 08015500 08075000 08075000 07374000 07374000 07374000 07374000 08162500 08116650 02376500 02368000
- Surface_Heat_Forcing :
- FVCOM variable surface heat forcing file: FILE NAME:nos.ngofs2.hflux.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Surface_PrecipEvap_Forcing :
- SURFACE PRECIPITATION FORCING IS OFF
- Surface_Wind_Forcing :
- FVCOM variable surface Wind forcing: FILE NAME:nos.ngofs2.met.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Tidal_Forcing :
- TIDAL ELEVATION FORCING IS OFF!
- history :
- model started at: 18/06/2024 21:15
- institution :
- School for Marine Science and Technology
- references :
- http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu
- source :
- FVCOM_4.3
- title :
- NGOFS2
f"Dataset size: {ds.nbytes * 1.0e-9} Gb"
'Dataset size: 112.718031706 Gb'
Example Polygon¶
Drawn with: https://geojson.io
polygon = np.array(
[
[-88.90580394001934, 30.29241252023394],
[-89.19516286474344, 30.472177580997183],
[-89.68564387438431, 30.353800554455674],
[-90.10008710994322, 30.07406870382799],
[-90.02651730481438, 29.72115942943863],
[-89.61697872293067, 29.347768286358786],
[-89.48700540053677, 28.869972870160396],
[-89.03822958925102, 28.876415308359796],
[-88.6164293731791, 29.163769289372496],
[-88.54531189488806, 29.682817158850725],
[-88.31234084531368, 29.836098340764792],
[-88.3491257478779, 29.967902324410517],
[-88.90580394001934, 30.29241252023394],
]
)
polygon
array([[-88.90580394, 30.29241252],
[-89.19516286, 30.47217758],
[-89.68564387, 30.35380055],
[-90.10008711, 30.0740687 ],
[-90.0265173 , 29.72115943],
[-89.61697872, 29.34776829],
[-89.4870054 , 28.86997287],
[-89.03822959, 28.87641531],
[-88.61642937, 29.16376929],
[-88.54531189, 29.68281716],
[-88.31234085, 29.83609834],
[-88.34912575, 29.96790232],
[-88.90580394, 30.29241252]])
We can subset down to the variables we care about, while keeping the grid information for later analysis
ds_salt = ds.xsg.subset_vars(['salinity'])
ds_salt
<xarray.Dataset> Size: 12GB
Dimensions: (nele: 569405, node: 303714, three: 3, time: 255, siglay: 40)
Coordinates:
lonc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
latc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
lat (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
lon (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
siglay (siglay, node) float32 49MB dask.array<chunksize=(40, 303714), meta=np.ndarray>
* time (time) datetime64[ns] 2kB 2024-06-18T21:00:00 ... 2024-07-20T...
Dimensions without coordinates: nele, node, three
Data variables:
fvcom_mesh int32 4B ...
nv (three, nele) int32 7MB dask.array<chunksize=(3, 569405), meta=np.ndarray>
nbe (three, nele) int32 7MB dask.array<chunksize=(3, 569405), meta=np.ndarray>
salinity (time, siglay, node) float32 12GB dask.array<chunksize=(1, 40, 303714), meta=np.ndarray>
Attributes: (12/14)
Conventions: CF-1.0
CoordinateProjection: init=nad83:4205
CoordinateSystem: GeoReferenced
GroundWater_Forcing: GROUND WATER FORCING IS OFF!
River_Forcing: THERE ARE 63 RIVERS IN THIS MODEL.\nRIVER IN...
Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\nF...
... ...
Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!
history: model started at: 18/06/2024 21:15
institution: School for Marine Science and Technology
references: http://fvcom.smast.umassd.edu, http://codfis...
source: FVCOM_4.3
title: NGOFS2xarray.Dataset
- nele: 569405
- node: 303714
- three: 3
- time: 255
- siglay: 40
- lonc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - latc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lat(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - siglay(siglay, node)float32dask.array<chunksize=(40, 303714), meta=np.ndarray>
- formula_terms :
- sigma: siglay eta: zeta depth: h
- long_name :
- Sigma Layers
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 46.34 MiB 46.34 MiB Shape (40, 303714) (40, 303714) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]2024-06-18T21:00:00 ... 2024-07-...
- format :
- defined reference date
- long_name :
- time
- time_zone :
- UTC
array(['2024-06-18T21:00:00.000000000', '2024-06-19T00:00:00.000000000', '2024-06-19T03:00:00.000000000', ..., '2024-07-20T09:00:00.000000000', '2024-07-20T12:00:00.000000000', '2024-07-20T15:00:00.000000000'], dtype='datetime64[ns]')
- fvcom_mesh()int32...
- cf_role :
- mesh_topology
- topology_dimension :
- 2
- node_coordinates :
- lon lat
- face_coordinates :
- lonc latc
- face_node_connectivity :
- nv
- face_face_connectivity :
- nbe
- face_dimension :
- nele
[1 values with dtype=int32]
- nv(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- nodes surrounding element
- start_index :
- 1
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nbe(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- elements surrounding each element
- start_index :
- 1
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - salinity(time, siglay, node)float32dask.array<chunksize=(1, 40, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- salinity
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_salinity
- type :
- data
- units :
- 1e-3
Array Chunk Bytes 11.54 GiB 46.34 MiB Shape (255, 40, 303714) (1, 40, 303714) Dask graph 255 chunks in 2 graph layers Data type float32 numpy.ndarray
- timePandasIndex
PandasIndex(DatetimeIndex(['2024-06-18 21:00:00', '2024-06-19 00:00:00', '2024-06-19 03:00:00', '2024-06-19 06:00:00', '2024-06-19 09:00:00', '2024-06-19 12:00:00', '2024-06-19 15:00:00', '2024-06-19 18:00:00', '2024-06-19 21:00:00', '2024-06-20 00:00:00', ... '2024-07-19 12:00:00', '2024-07-19 15:00:00', '2024-07-19 18:00:00', '2024-07-19 21:00:00', '2024-07-20 00:00:00', '2024-07-20 03:00:00', '2024-07-20 06:00:00', '2024-07-20 09:00:00', '2024-07-20 12:00:00', '2024-07-20 15:00:00'], dtype='datetime64[ns]', name='time', length=255, freq=None))
- Conventions :
- CF-1.0
- CoordinateProjection :
- init=nad83:4205
- CoordinateSystem :
- GeoReferenced
- GroundWater_Forcing :
- GROUND WATER FORCING IS OFF!
- River_Forcing :
- THERE ARE 63 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: 08012000 02365500 02375500 02489500 02492000 08211200 08211200 08211200 02481510 02481510 02481510 02479000 02479000 02479000 02479000 08164800 08164800 08164000 08164000 08188800 08188800 08188800 08066500 08066500 02479560 02479560 02479560 02479560 07375500 07375500 08030500 08030500 08069000 08069000 08069000 07381600 07381600 07381600 07381600 02470629 02470629 02470629 02470629 02471019 02471019 02471019 02471019 02471019 08076000 08076000 08075400 08041780 08015500 08075000 08075000 07374000 07374000 07374000 07374000 08162500 08116650 02376500 02368000
- Surface_Heat_Forcing :
- FVCOM variable surface heat forcing file: FILE NAME:nos.ngofs2.hflux.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Surface_PrecipEvap_Forcing :
- SURFACE PRECIPITATION FORCING IS OFF
- Surface_Wind_Forcing :
- FVCOM variable surface Wind forcing: FILE NAME:nos.ngofs2.met.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Tidal_Forcing :
- TIDAL ELEVATION FORCING IS OFF!
- history :
- model started at: 18/06/2024 21:15
- institution :
- School for Marine Science and Technology
- references :
- http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu
- source :
- FVCOM_4.3
- title :
- NGOFS2
Then we can subset to only the surface
ds_salt_surface = ds_salt.xsg.subset_vertical_level(level=0, method='nearest')
ds_salt_surface
<xarray.Dataset> Size: 332MB
Dimensions: (nele: 569405, node: 303714, three: 3, time: 255)
Coordinates:
lonc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
latc (nele) float32 2MB dask.array<chunksize=(569405,), meta=np.ndarray>
lat (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
lon (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
siglay (node) float32 1MB dask.array<chunksize=(303714,), meta=np.ndarray>
* time (time) datetime64[ns] 2kB 2024-06-18T21:00:00 ... 2024-07-20T...
Dimensions without coordinates: nele, node, three
Data variables:
fvcom_mesh int32 4B ...
nv (three, nele) int32 7MB dask.array<chunksize=(3, 569405), meta=np.ndarray>
nbe (three, nele) int32 7MB dask.array<chunksize=(3, 569405), meta=np.ndarray>
salinity (time, node) float32 310MB dask.array<chunksize=(1, 303714), meta=np.ndarray>
Attributes: (12/14)
Conventions: CF-1.0
CoordinateProjection: init=nad83:4205
CoordinateSystem: GeoReferenced
GroundWater_Forcing: GROUND WATER FORCING IS OFF!
River_Forcing: THERE ARE 63 RIVERS IN THIS MODEL.\nRIVER IN...
Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\nF...
... ...
Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!
history: model started at: 18/06/2024 21:15
institution: School for Marine Science and Technology
references: http://fvcom.smast.umassd.edu, http://codfis...
source: FVCOM_4.3
title: NGOFS2xarray.Dataset
- nele: 569405
- node: 303714
- three: 3
- time: 255
- lonc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - latc(nele)float32dask.array<chunksize=(569405,), meta=np.ndarray>
- long_name :
- zonal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 2.17 MiB 2.17 MiB Shape (569405,) (569405,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lat(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- long_name :
- nodal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - siglay(node)float32dask.array<chunksize=(303714,), meta=np.ndarray>
- formula_terms :
- sigma: siglay eta: zeta depth: h
- long_name :
- Sigma Layers
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 1.16 MiB 1.16 MiB Shape (303714,) (303714,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]2024-06-18T21:00:00 ... 2024-07-...
- format :
- defined reference date
- long_name :
- time
- time_zone :
- UTC
array(['2024-06-18T21:00:00.000000000', '2024-06-19T00:00:00.000000000', '2024-06-19T03:00:00.000000000', ..., '2024-07-20T09:00:00.000000000', '2024-07-20T12:00:00.000000000', '2024-07-20T15:00:00.000000000'], dtype='datetime64[ns]')
- fvcom_mesh()int32...
- cf_role :
- mesh_topology
- topology_dimension :
- 2
- node_coordinates :
- lon lat
- face_coordinates :
- lonc latc
- face_node_connectivity :
- nv
- face_face_connectivity :
- nbe
- face_dimension :
- nele
[1 values with dtype=int32]
- nv(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- nodes surrounding element
- start_index :
- 1
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - nbe(three, nele)int32dask.array<chunksize=(3, 569405), meta=np.ndarray>
- long_name :
- elements surrounding each element
- start_index :
- 1
Array Chunk Bytes 6.52 MiB 6.52 MiB Shape (3, 569405) (3, 569405) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray - salinity(time, node)float32dask.array<chunksize=(1, 303714), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- salinity
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_salinity
- type :
- data
- units :
- 1e-3
Array Chunk Bytes 295.44 MiB 1.16 MiB Shape (255, 303714) (1, 303714) Dask graph 255 chunks in 3 graph layers Data type float32 numpy.ndarray
- timePandasIndex
PandasIndex(DatetimeIndex(['2024-06-18 21:00:00', '2024-06-19 00:00:00', '2024-06-19 03:00:00', '2024-06-19 06:00:00', '2024-06-19 09:00:00', '2024-06-19 12:00:00', '2024-06-19 15:00:00', '2024-06-19 18:00:00', '2024-06-19 21:00:00', '2024-06-20 00:00:00', ... '2024-07-19 12:00:00', '2024-07-19 15:00:00', '2024-07-19 18:00:00', '2024-07-19 21:00:00', '2024-07-20 00:00:00', '2024-07-20 03:00:00', '2024-07-20 06:00:00', '2024-07-20 09:00:00', '2024-07-20 12:00:00', '2024-07-20 15:00:00'], dtype='datetime64[ns]', name='time', length=255, freq=None))
- Conventions :
- CF-1.0
- CoordinateProjection :
- init=nad83:4205
- CoordinateSystem :
- GeoReferenced
- GroundWater_Forcing :
- GROUND WATER FORCING IS OFF!
- River_Forcing :
- THERE ARE 63 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: 08012000 02365500 02375500 02489500 02492000 08211200 08211200 08211200 02481510 02481510 02481510 02479000 02479000 02479000 02479000 08164800 08164800 08164000 08164000 08188800 08188800 08188800 08066500 08066500 02479560 02479560 02479560 02479560 07375500 07375500 08030500 08030500 08069000 08069000 08069000 07381600 07381600 07381600 07381600 02470629 02470629 02470629 02470629 02471019 02471019 02471019 02471019 02471019 08076000 08076000 08075400 08041780 08015500 08075000 08075000 07374000 07374000 07374000 07374000 08162500 08116650 02376500 02368000
- Surface_Heat_Forcing :
- FVCOM variable surface heat forcing file: FILE NAME:nos.ngofs2.hflux.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Surface_PrecipEvap_Forcing :
- SURFACE PRECIPITATION FORCING IS OFF
- Surface_Wind_Forcing :
- FVCOM variable surface Wind forcing: FILE NAME:nos.ngofs2.met.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Tidal_Forcing :
- TIDAL ELEVATION FORCING IS OFF!
- history :
- model started at: 18/06/2024 21:15
- institution :
- School for Marine Science and Technology
- references :
- http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu
- source :
- FVCOM_4.3
- title :
- NGOFS2
Then we can subset the grid down to the target area
ds_subset = ds_salt_surface.xsg.subset_polygon(polygon)
assert(ds_subset is not None)
ds_subset
<xarray.Dataset> Size: 59MB
Dimensions: (nele: 99246, node: 54024, three: 3, time: 255)
Coordinates:
lonc (nele) float32 397kB dask.array<chunksize=(99246,), meta=np.ndarray>
latc (nele) float32 397kB dask.array<chunksize=(99246,), meta=np.ndarray>
lat (node) float32 216kB dask.array<chunksize=(54024,), meta=np.ndarray>
lon (node) float32 216kB dask.array<chunksize=(54024,), meta=np.ndarray>
siglay (node) float32 216kB dask.array<chunksize=(54024,), meta=np.ndarray>
* time (time) datetime64[ns] 2kB 2024-06-18T21:00:00 ... 2024-07-20T...
Dimensions without coordinates: nele, node, three
Data variables:
fvcom_mesh int32 4B ...
nv (three, nele) int32 1MB dask.array<chunksize=(3, 99246), meta=np.ndarray>
nbe (three, nele) int32 1MB dask.array<chunksize=(3, 99246), meta=np.ndarray>
salinity (time, node) float32 55MB dask.array<chunksize=(1, 54024), meta=np.ndarray>
Attributes: (12/14)
Conventions: CF-1.0
CoordinateProjection: init=nad83:4205
CoordinateSystem: GeoReferenced
GroundWater_Forcing: GROUND WATER FORCING IS OFF!
River_Forcing: THERE ARE 63 RIVERS IN THIS MODEL.\nRIVER IN...
Surface_Heat_Forcing: FVCOM variable surface heat forcing file:\nF...
... ...
Tidal_Forcing: TIDAL ELEVATION FORCING IS OFF!
history: model started at: 18/06/2024 21:15
institution: School for Marine Science and Technology
references: http://fvcom.smast.umassd.edu, http://codfis...
source: FVCOM_4.3
title: NGOFS2xarray.Dataset
- nele: 99246
- node: 54024
- three: 3
- time: 255
- lonc(nele)float32dask.array<chunksize=(99246,), meta=np.ndarray>
- long_name :
- zonal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 387.68 kiB 387.68 kiB Shape (99246,) (99246,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - latc(nele)float32dask.array<chunksize=(99246,), meta=np.ndarray>
- long_name :
- zonal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 387.68 kiB 387.68 kiB Shape (99246,) (99246,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - lat(node)float32dask.array<chunksize=(54024,), meta=np.ndarray>
- long_name :
- nodal latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 211.03 kiB 211.03 kiB Shape (54024,) (54024,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - lon(node)float32dask.array<chunksize=(54024,), meta=np.ndarray>
- long_name :
- nodal longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 211.03 kiB 211.03 kiB Shape (54024,) (54024,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - siglay(node)float32dask.array<chunksize=(54024,), meta=np.ndarray>
- formula_terms :
- sigma: siglay eta: zeta depth: h
- long_name :
- Sigma Layers
- positive :
- up
- standard_name :
- ocean_sigma/general_coordinate
- valid_max :
- 0.0
- valid_min :
- -1.0
Array Chunk Bytes 211.03 kiB 211.03 kiB Shape (54024,) (54024,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]2024-06-18T21:00:00 ... 2024-07-...
- format :
- defined reference date
- long_name :
- time
- time_zone :
- UTC
array(['2024-06-18T21:00:00.000000000', '2024-06-19T00:00:00.000000000', '2024-06-19T03:00:00.000000000', ..., '2024-07-20T09:00:00.000000000', '2024-07-20T12:00:00.000000000', '2024-07-20T15:00:00.000000000'], dtype='datetime64[ns]')
- fvcom_mesh()int32...
- cf_role :
- mesh_topology
- topology_dimension :
- 2
- node_coordinates :
- lon lat
- face_coordinates :
- lonc latc
- face_node_connectivity :
- nv
- face_face_connectivity :
- nbe
- face_dimension :
- nele
[1 values with dtype=int32]
- nv(three, nele)int32dask.array<chunksize=(3, 99246), meta=np.ndarray>
- long_name :
- nodes surrounding element
- start_index :
- 1
Array Chunk Bytes 1.14 MiB 1.14 MiB Shape (3, 99246) (3, 99246) Dask graph 1 chunks in 4 graph layers Data type int32 numpy.ndarray - nbe(three, nele)int32dask.array<chunksize=(3, 99246), meta=np.ndarray>
- long_name :
- elements surrounding each element
- start_index :
- 1
Array Chunk Bytes 1.14 MiB 1.14 MiB Shape (3, 99246) (3, 99246) Dask graph 1 chunks in 4 graph layers Data type int32 numpy.ndarray - salinity(time, node)float32dask.array<chunksize=(1, 54024), meta=np.ndarray>
- grid :
- fvcom_grid
- location :
- node
- long_name :
- salinity
- mesh :
- fvcom_mesh
- standard_name :
- sea_water_salinity
- type :
- data
- units :
- 1e-3
Array Chunk Bytes 52.55 MiB 211.03 kiB Shape (255, 54024) (1, 54024) Dask graph 255 chunks in 4 graph layers Data type float32 numpy.ndarray
- timePandasIndex
PandasIndex(DatetimeIndex(['2024-06-18 21:00:00', '2024-06-19 00:00:00', '2024-06-19 03:00:00', '2024-06-19 06:00:00', '2024-06-19 09:00:00', '2024-06-19 12:00:00', '2024-06-19 15:00:00', '2024-06-19 18:00:00', '2024-06-19 21:00:00', '2024-06-20 00:00:00', ... '2024-07-19 12:00:00', '2024-07-19 15:00:00', '2024-07-19 18:00:00', '2024-07-19 21:00:00', '2024-07-20 00:00:00', '2024-07-20 03:00:00', '2024-07-20 06:00:00', '2024-07-20 09:00:00', '2024-07-20 12:00:00', '2024-07-20 15:00:00'], dtype='datetime64[ns]', name='time', length=255, freq=None))
- Conventions :
- CF-1.0
- CoordinateProjection :
- init=nad83:4205
- CoordinateSystem :
- GeoReferenced
- GroundWater_Forcing :
- GROUND WATER FORCING IS OFF!
- River_Forcing :
- THERE ARE 63 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: 08012000 02365500 02375500 02489500 02492000 08211200 08211200 08211200 02481510 02481510 02481510 02479000 02479000 02479000 02479000 08164800 08164800 08164000 08164000 08188800 08188800 08188800 08066500 08066500 02479560 02479560 02479560 02479560 07375500 07375500 08030500 08030500 08069000 08069000 08069000 07381600 07381600 07381600 07381600 02470629 02470629 02470629 02470629 02471019 02471019 02471019 02471019 02471019 08076000 08076000 08075400 08041780 08015500 08075000 08075000 07374000 07374000 07374000 07374000 08162500 08116650 02376500 02368000
- Surface_Heat_Forcing :
- FVCOM variable surface heat forcing file: FILE NAME:nos.ngofs2.hflux.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Surface_PrecipEvap_Forcing :
- SURFACE PRECIPITATION FORCING IS OFF
- Surface_Wind_Forcing :
- FVCOM variable surface Wind forcing: FILE NAME:nos.ngofs2.met.forecast.20240618.t21z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format
- Tidal_Forcing :
- TIDAL ELEVATION FORCING IS OFF!
- history :
- model started at: 18/06/2024 21:15
- institution :
- School for Marine Science and Technology
- references :
- http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu
- source :
- FVCOM_4.3
- title :
- NGOFS2
import matplotlib.pyplot as plt
import matplotlib.tri as tri
salinity = ds_subset.salinity.isel(time=0)
tris = tri.Triangulation(
salinity.cf["longitude"],
salinity.cf["latitude"],
ds_subset[ds_subset.fvcom_mesh.face_node_connectivity].T - 1,
)
plt.tripcolor(tris, salinity, shading="flat")
<matplotlib.collections.PolyCollection at 0x31edf2dd0>
f"Subset dataset size: {ds_subset.nbytes * 1.0e-6} Mb"
'Subset dataset size: 58.930684 Mb'