Reading ROMS Output#
[1]:
from roms_tools import Grid, ROMSOutput
For any type of visualization or analysis, we require information about the grid used in the model. We retrieve the grid data using the Grid.from_file method.
[2]:
grid = Grid(filename=
"/anvil/projects/x-ees250129/Datasets/ROMSOutput/eastpac25km/epac25km_grd.nc"
)
2026-06-26 19:44:41 - WARNING - Vertical coordinates (Cs_r, Cs_w) not found in grid file and were not provided, using defaults.
2026-06-26 19:44:41 - INFO - === Preparing the vertical coordinate system using N = 100, theta_s = 5.0, theta_b = 2.0, hc = 300.0 ===
2026-06-26 19:44:41 - INFO - Total time: 0.004 seconds
2026-06-26 19:44:41 - INFO - ================================================================================================
The ROMSOutput class provides a flexible way to load ROMS output files using the path parameter, which can be specified in the following ways:
Single file: If
pathis a single file, only that file will be loaded.List of files: If
pathis a list of file paths, the specified files will be loaded in the given order.Wildcards: If
pathcontains wildcards (e.g.,*rst*.nc), matching files are loaded in lexicographic order, assuming this reflects the correct temporal sequence (as is the case for standard ROMS output).
For cases 2 and 3, ROMS-Tools will attempt to concatenate the files along the time dimension. If this is not possible (e.g., due to inconsistent dimensions or metadata), an error may be thrown.
In the following example, we will read restart files generated during a ROMS simulation.
Reading a single file#
[3]:
%%time
roms_output_from_single_file = ROMSOutput(
grid=grid,
path="/anvil/projects/x-ees250129/Datasets/ROMSOutput/eastpac25km/eastpac25km_rst.19980106000000.nc",
use_dask=True,
)
CPU times: user 188 ms, sys: 36.2 ms, total: 224 ms
Wall time: 305 ms
Note
In the cell above, we read our ROMS output files with use_dask = True. This enables Dask, a Python library designated to facilitate scalable, out-of-memory data processing by distributing computations across multiple threads or processes. Here you can learn more about using Dask with ROMS-Tools.
The .ds attribute contains an xarray.Dataset with the data that was read in. As you can see, the restart file contains two time stamps (10 minutes apart).
[4]:
roms_output_from_single_file.ds
[4]:
<xarray.Dataset> Size: 1GB
Dimensions: (time: 2, auxil: 6, eta_rho: 162, xi_rho: 122,
xi_u: 121, eta_v: 161, s_rho: 100)
Coordinates:
* time (time) datetime64[ns] 16B 1998-01-05T23:50:00 1998...
lon_rho (eta_rho, xi_rho) float64 158kB ...
lat_rho (eta_rho, xi_rho) float64 158kB ...
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
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
Dimensions without coordinates: auxil, eta_rho, xi_rho, xi_u, eta_v, s_rho
Data variables: (12/58)
ocean_time (time) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
time_step (time, auxil) int32 48B dask.array<chunksize=(1, 6), meta=np.ndarray>
zeta (time, eta_rho, xi_rho) float64 316kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
ubar (time, eta_rho, xi_u) float64 314kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
vbar (time, eta_v, xi_rho) float64 314kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
MARBL_PH_3D (time, s_rho, eta_rho, xi_rho) float64 32MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
... ...
u_slow (time, s_rho, eta_rho, xi_u) float64 31MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
v_slow (time, s_rho, eta_v, xi_rho) float64 31MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
p_slow (time, s_rho, eta_rho, xi_rho) float64 32MB dask.array<chunksize=(1, 50, 50, 50), 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 filexarray.Dataset
- time: 2
- auxil: 6
- eta_rho: 162
- xi_rho: 122
- xi_u: 121
- eta_v: 161
- s_rho: 100
- time(time)datetime64[ns]1998-01-05T23:50:00 1998-01-06
array(['1998-01-05T23:50:00.000000000', '1998-01-06T00:00:00.000000000'], dtype='datetime64[ns]') - lon_rho(eta_rho, xi_rho)float64...
- Long_name :
- longitude of rho-points
- units :
- degree East
[19764 values with dtype=float64]
- lat_rho(eta_rho, xi_rho)float64...
- Long_name :
- latitude of rho-points
- units :
- degree North
[19764 values with dtype=float64]
- lat_v(eta_v, xi_rho)float647.758 7.87 7.981 ... 52.08 52.18
- long_name :
- latitude of v-points
- units :
- degrees North
array([[ 7.75840846, 7.86968505, 7.98097613, ..., 20.23743283, 20.32853099, 20.41929115], [ 7.94654137, 8.05803956, 8.16955047, ..., 20.43654939, 20.52758743, 20.61828471], [ 8.13454206, 8.24626301, 8.35799495, ..., 20.63569259, 20.72667168, 20.81730724], ..., [34.88184053, 35.04408237, 35.20620594, ..., 51.59028295, 51.69192452, 51.79264323], [35.02809386, 35.19075973, 35.35330797, ..., 51.78398355, 51.88588867, 51.98686607], [35.17387447, 35.33696536, 35.49993927, ..., 51.97754248, 52.07971517, 52.18095524]], shape=(161, 122)) - lon_v(eta_v, xi_rho)float64231.8 232.0 232.2 ... 237.3 237.6
- long_name :
- longitude of v-points
- units :
- degrees East
array([[231.77294597, 231.96302429, 232.15337591, ..., 255.95043605, 256.16267385, 256.37497025], [231.66051421, 231.85054689, 232.04085548, ..., 255.85313541, 256.06567675, 256.27827928], [231.54780566, 231.73779358, 231.92806005, ..., 255.75577079, 255.96861847, 256.18152994], ..., [208.52050721, 208.69951783, 208.87940243, ..., 237.30044119, 237.61289958, 237.92647577], [208.32223022, 208.50098705, 208.68062238, ..., 237.13553756, 237.44911519, 237.76382812], [208.12308024, 208.30157779, 208.48095836, ..., 236.96949051, 237.2841955 , 237.6000536 ]], shape=(161, 122)) - lat_u(eta_rho, xi_u)float647.72 7.831 7.942 ... 52.13 52.23
- long_name :
- latitude of u-points
- units :
- degrees North
array([[ 7.71989212, 7.83106589, 7.94225351, ..., 20.09214977, 20.18344553, 20.27440573], [ 7.90820139, 8.01959529, 8.13100127, ..., 20.29128185, 20.38251829, 20.4734164 ], [ 8.09637954, 8.20799474, 8.31962027, ..., 20.49044026, 20.58161854, 20.67245574], ..., [35.0363117 , 35.19870633, 35.3609802 , ..., 51.6358225 , 51.73805451, 51.83936422], [35.18254189, 35.34536138, 35.50806074, ..., 51.82931774, 51.9318177 , 52.03339052], [35.32829793, 35.49154325, 35.65466912, ..., 52.022668 , 52.12543995, 52.22727989]], shape=(162, 121)) - lon_u(eta_rho, xi_u)float64231.9 232.1 232.3 ... 237.0 237.4
- long_name :
- longitude of u-points
- units :
- degrees East
array([[231.92414366, 232.11438102, 232.30488992, ..., 255.89305738, 256.10511406, 256.31722944], [231.8118266 , 232.00201918, 232.19248591, ..., 255.79563831, 256.00799584, 256.22041465], [231.6992345 , 231.88938319, 232.07980867, ..., 255.69815517, 255.91081633, 256.12354138], ..., [208.51102895, 208.69035216, 208.87055434, ..., 237.06233232, 237.37478101, 237.68836023], [208.31218832, 208.49125727, 208.67120977, ..., 236.89630858, 237.20987174, 237.52458308], [208.11246972, 208.29127888, 208.47097613, ..., 236.72912868, 237.04381427, 237.35966602]], shape=(162, 121))
- ocean_time(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- Time since 1995/01/01
- units :
- second
Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 3 graph layers Data type float64 numpy.ndarray - time_step(time, auxil)int32dask.array<chunksize=(1, 6), meta=np.ndarray>
- long_name :
- time step and record numbers from initialization
Array Chunk Bytes 48 B 24 B Shape (2, 6) (1, 6) Dask graph 2 chunks in 3 graph layers Data type int32 numpy.ndarray - zeta(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- free-surface elevation
- units :
- meter
Array Chunk Bytes 308.81 kiB 19.53 kiB Shape (2, 162, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - ubar(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- vertically averaged u-momentum component
- units :
- meter second-1
Array Chunk Bytes 306.28 kiB 19.53 kiB Shape (2, 162, 121) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - vbar(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- vertically averaged v-momentum component
- units :
- meter second-1
Array Chunk Bytes 306.91 kiB 19.53 kiB Shape (2, 161, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- 3D pH
- units :
- pH
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- 3D pH (alternate CO2)
- units :
- pH
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers
- units :
- pH
Array Chunk Bytes 308.81 kiB 19.53 kiB Shape (2, 162, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - MARBL_ABIO_PH_SURF(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for abiotic tracers
- units :
- pH
Array Chunk Bytes 308.81 kiB 19.53 kiB Shape (2, 162, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF_ALT_CO2(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers (alternate CO2)
- units :
- pH
Array Chunk Bytes 308.81 kiB 19.53 kiB Shape (2, 162, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - u(time, s_rho, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- u-momentum component
- units :
- meter second-1
Array Chunk Bytes 29.91 MiB 0.95 MiB Shape (2, 100, 162, 121) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - v(time, s_rho, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- v-momentum component
- units :
- meter second-1
Array Chunk Bytes 29.97 MiB 0.95 MiB Shape (2, 100, 161, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - temp(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- potential temperature
- units :
- Celsius
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - salt(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- salinity
- units :
- PSU
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - PO4(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Phosphate
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - NO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Nitrate
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - SiO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Silicate
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - NH4(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Ammonia
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - Fe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Iron
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - Lig(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Iron Binding Ligand
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - O2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Oxygen
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DIC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DIC_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon, Alternative CO2
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - ALK(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Alkalinity
- units :
- meq/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - ALK_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Alkalinity, Alternative CO2
- units :
- meq/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DOC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Organic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DON(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Dissolved Organic Nitrogen
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DOP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Organic Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DOPr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DOP
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DONr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DON
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DOCr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DOC
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - zooC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Zooplankton Carbon
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - spChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - spC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Carbon
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - spP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - spFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Iron
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - spCaCO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto CaCO3
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diatChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diatC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Diatom Carbon
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diatP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diatFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Iron
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diatSi(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Silicon
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diazChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diazC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Carbon
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diazP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - diazFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Iron
- units :
- mmol/m^3
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - DU_avg2(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <<fast-time averaged ubar(:,:,n+1/2)>>
Array Chunk Bytes 306.28 kiB 19.53 kiB Shape (2, 162, 121) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - DV_avg2(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <<fast-time-averaged vbar(:,:,n+1/2)>>
Array Chunk Bytes 306.91 kiB 19.53 kiB Shape (2, 161, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - DU_avg_bak(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged ubar(:,:,n-1)>
Array Chunk Bytes 306.28 kiB 19.53 kiB Shape (2, 162, 121) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - DV_avg_bak(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged vbar(:,:,n-1)>
Array Chunk Bytes 306.91 kiB 19.53 kiB Shape (2, 161, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - hbls(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- Thickness of KPP surface boundary layer
- units :
- meter
Array Chunk Bytes 308.81 kiB 19.53 kiB Shape (2, 162, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - hbbl(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- Thickness of KPP bottom boundary layer
- units :
- meter
Array Chunk Bytes 308.81 kiB 19.53 kiB Shape (2, 162, 122) (1, 50, 50) Dask graph 24 chunks in 3 graph layers Data type float64 numpy.ndarray - u_slow(time, s_rho, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered u
- units :
- m/s
Array Chunk Bytes 29.91 MiB 0.95 MiB Shape (2, 100, 162, 121) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - v_slow(time, s_rho, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered v
- units :
- m/s
Array Chunk Bytes 29.97 MiB 0.95 MiB Shape (2, 100, 161, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - p_slow(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered pressure
- units :
- Pa??
Array Chunk Bytes 30.16 MiB 0.95 MiB Shape (2, 100, 162, 122) (1, 50, 50, 50) Dask graph 48 chunks in 3 graph layers Data type float64 numpy.ndarray - mask_rho(eta_rho, xi_rho)float64...
- Long_name :
- mask at rho-points
- units :
- land/water (0/1)
- Notes :
- Mask has been modified to match the parent grid Mask at the boundaries
[19764 values with dtype=float64]
- mask_u(eta_rho, xi_u)int321 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0
- long_name :
- Mask at u-points
- units :
- land/water (0/1)
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], shape=(162, 121), dtype=int32) - mask_v(eta_v, xi_rho)int321 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0
- long_name :
- Mask at v-points
- units :
- land/water (0/1)
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], shape=(161, 122), dtype=int32)
- title :
- eastpac25km , 25km resolution
- grid_file :
- /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/epac25km_grd.000.nc
- init_file :
- /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastpac25km_rst.19980101000000.000.nc
- ntimes :
- 4610
- ndtfast :
- 45
- dt :
- 600.0
- dtfast :
- 13.333333333333334
- theta_s :
- 5.0
- theta_b :
- 2.0
- hc :
- 300.0
- Cs_w :
- [-1.00000000e+00 -9.83735238e-01 -9.66697847e-01 -9.48934833e-01 -9.30497932e-01 -9.11442824e-01 -8.91828326e-01 -8.71715602e-01 -8.51167398e-01 -8.30247303e-01 -8.09019067e-01 -7.87545970e-01 -7.65890248e-01 -7.44112585e-01 -7.22271672e-01 -7.00423829e-01 -6.78622689e-01 -6.56918954e-01 -6.35360192e-01 -6.13990705e-01 -5.92851439e-01 -5.71979937e-01 -5.51410339e-01 -5.31173415e-01 -5.11296624e-01 -4.91804203e-01 -4.72717281e-01 -4.54054004e-01 -4.35829676e-01 -4.18056911e-01 -4.00745794e-01 -3.83904042e-01 -3.67537172e-01 -3.51648664e-01 -3.36240125e-01 -3.21311453e-01 -3.06860989e-01 -2.92885669e-01 -2.79381169e-01 -2.66342043e-01 -2.53761851e-01 -2.41633283e-01 -2.29948277e-01 -2.18698121e-01 -2.07873557e-01 -1.97464874e-01 -1.87461989e-01 -1.77854528e-01 -1.68631899e-01 -1.59783353e-01 -1.51298043e-01 -1.43165082e-01 -1.35373585e-01 -1.27912713e-01 -1.20771713e-01 -1.13939947e-01 -1.07406924e-01 -1.01162327e-01 -9.51960296e-02 -8.94981213e-02 -8.40589181e-02 -7.88689787e-02 -7.39191144e-02 -6.92003983e-02 -6.47041721e-02 -6.04220511e-02 -5.63459281e-02 -5.24679753e-02 -4.87806460e-02 -4.52766740e-02 -4.19490733e-02 -3.87911360e-02 -3.57964303e-02 -3.29587975e-02 -3.02723487e-02 -2.77314609e-02 -2.53307733e-02 -2.30651826e-02 -2.09298389e-02 -1.89201410e-02 -1.70317316e-02 -1.52604930e-02 -1.36025420e-02 -1.20542257e-02 -1.06121170e-02 -9.27300977e-03 -8.03391530e-03 -6.89205773e-03 -5.84487036e-03 -4.88999201e-03 -4.02526351e-03 -3.24872452e-03 -2.55861056e-03 -1.95335031e-03 -1.43156315e-03 -9.92056980e-04 -6.33826341e-04 -3.56050802e-04 -1.58093625e-04 -3.95007397e-05 0.00000000e+00]
- Cs_r :
- [-9.91966929e-01 -9.75310303e-01 -9.57903911e-01 -9.39797221e-01 -9.21044043e-01 -9.01701722e-01 -8.81830349e-01 -8.61491972e-01 -8.40749848e-01 -8.19667729e-01 -7.98309206e-01 -7.76737103e-01 -7.55012940e-01 -7.33196455e-01 -7.11345198e-01 -6.89514181e-01 -6.67755598e-01 -6.46118605e-01 -6.24649151e-01 -6.03389868e-01 -5.82380004e-01 -5.61655398e-01 -5.41248500e-01 -5.21188414e-01 -5.01500975e-01 -4.82208852e-01 -4.63331666e-01 -4.44886122e-01 -4.26886160e-01 -4.09343110e-01 -3.92265853e-01 -3.75660984e-01 -3.59532980e-01 -3.43884367e-01 -3.28715875e-01 -3.14026605e-01 -2.99814178e-01 -2.86074882e-01 -2.72803815e-01 -2.59995018e-01 -2.47641602e-01 -2.35735865e-01 -2.24269409e-01 -2.13233237e-01 -2.02617853e-01 -1.92413349e-01 -1.82609488e-01 -1.73195779e-01 -1.64161542e-01 -1.55495973e-01 -1.47188200e-01 -1.39227330e-01 -1.31602496e-01 -1.24302897e-01 -1.17317835e-01 -1.10636741e-01 -1.04249210e-01 -9.81450152e-02 -9.23141376e-02 -8.67467775e-02 -8.14333708e-02 -7.63646013e-02 -7.15314104e-02 -6.69250045e-02 -6.25368617e-02 -5.83587357e-02 -5.43826587e-02 -5.06009434e-02 -4.70061835e-02 -4.35912530e-02 -4.03493052e-02 -3.72737705e-02 -3.43583542e-02 -3.15970327e-02 -2.89840508e-02 -2.65139170e-02 -2.41813998e-02 -2.19815232e-02 -1.99095622e-02 -1.79610382e-02 -1.61317141e-02 -1.44175902e-02 -1.28148990e-02 -1.13201009e-02 -9.92988001e-03 -8.64113938e-03 -7.45099718e-03 -6.35678262e-03 -5.35603221e-03 -4.44648617e-03 -3.62608516e-03 -2.89296712e-03 -2.24546446e-03 -1.68210150e-03 -1.20159214e-03 -8.02837815e-04 -4.84925772e-04 -2.47127569e-04 -8.88979122e-05 -9.87376857e-06]
- rho0 :
- 1027.4
- rho0_units :
- kg/m^3
- visc2 :
- 0.0
- visc2_units :
- m^2/s
- gamma2 :
- 1.0
- tnu2 :
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- tnu2_units :
- m^2/s
- ubind :
- 0.2
- ubind_units :
- m/s
- v_sponge :
- 2500.0
- v_sponge_units :
- m^2/s
- rdrg :
- 0.0
- rdrg_units :
- m/s
- rdrg2 :
- 0.0
- rdrg2_units :
- nondimensional
- Zob :
- 0.02
- Zob_units :
- m
- SRCS :
- SRCS $(shell ls *$(UPF77_ext)) SRCS : $(filter-out $(EXCL), $(SRCS)) SRCS : $(SRCS) $(INCL)
- CPPS :
- <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV_ISONEUTRAL NONLIN_EOS SPLIT_EOS SALINITY BULK_FRC T_FRC_BRY Z_FRC_BRY M3_FRC_BRY M2_FRC_BRY SPONGE UV_VIS2 TS_DIF2 LMD_MIXING LMD_KPP LMD_NONLOCAL LMD_RIMIX LMD_CONVEC LMD_BKPP CURVGRID SPHERICAL MASKING MASK_LAND_DATA OBC_M2FLATHER OBC_M3ORLANSKI OBC_TORLANSKI OBC_WEST OBC_NORTH OBC_SOUTH AVERAGES DIAGNOSTICS MARBL MARBL_DIAGS NOX_FORCING NHY_FORCING ALK_SOURCE PCO2AIR_FORCING TIDES POT_TIDES SSH_TIDES UV_TIDES <pre_step3d4S.F> SPLINE_UV SPLINE_TS <step3d_uv1.F> UPSTREAM_UV SPLINE_UV <step3d_uv2.F> DELTA=0.28000000000000003 EPSIL=0.35999999999999999 GAMMA=8.3333333333299994E-002 ALPHA_MAX=2.0 <step3d_t_ISO.F> SPLINE_TS <set_depth.F> NOW=3.63 MID=4.47 BAK=2.05 (N-M+B-1)/B=0.102439024 <lmd_kpp.F> INT_AT_RHO_POINTS SMOOTH_HBL <set_global_definitions.h> CORR_COUPLED_MODE EXTRAP_BAR_FLUXES IMPLCT_NO_SLIP_BTTM_BC VAR_RHO_2D
- surf_forcing_strings :
- bc_options :
- OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, OBC_TORLANSKI,
- git_version :
- type :
- ROMS restart file
Reading multiple files#
[5]:
%%time
roms_output_from_two_files = ROMSOutput(
grid=grid,
path=[
"/anvil/projects/x-ees250129/Datasets/ROMSOutput/eastpac25km/eastpac25km_rst.19980106000000.nc",
"/anvil/projects/x-ees250129/Datasets/ROMSOutput/eastpac25km/eastpac25km_rst.19990201000000.nc",
],
use_dask=True,
)
CPU times: user 115 ms, sys: 17 ms, total: 132 ms
Wall time: 180 ms
The two specified restart files were concatenated into a single xarray.Dataset, which now contains 4 time stamps.
[6]:
roms_output_from_two_files.ds
[6]:
<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_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
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
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, 50, 50), meta=np.ndarray>
ubar (time, eta_rho, xi_u) float64 627kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
vbar (time, eta_v, xi_rho) float64 629kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
MARBL_PH_3D (time, s_rho, eta_rho, xi_rho) float64 63MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
... ...
u_slow (time, s_rho, eta_rho, xi_u) float64 63MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
v_slow (time, s_rho, eta_v, xi_rho) float64 63MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
p_slow (time, s_rho, eta_rho, xi_rho) float64 63MB dask.array<chunksize=(1, 50, 50, 50), 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 filexarray.Dataset
- time: 4
- auxil: 6
- eta_rho: 162
- xi_rho: 122
- xi_u: 121
- eta_v: 161
- s_rho: 100
- time(time)datetime64[ns]1998-01-05T23:50:00 ... 1999-02-01
array(['1998-01-05T23:50:00.000000000', '1998-01-06T00:00:00.000000000', '1999-01-31T23:50:00.000000000', '1999-02-01T00:00:00.000000000'], dtype='datetime64[ns]') - lon_rho(eta_rho, xi_rho)float64...
- Long_name :
- longitude of rho-points
- units :
- degree East
[19764 values with dtype=float64]
- lat_rho(eta_rho, xi_rho)float64...
- Long_name :
- latitude of rho-points
- units :
- degree North
[19764 values with dtype=float64]
- lat_v(eta_v, xi_rho)float647.758 7.87 7.981 ... 52.08 52.18
- long_name :
- latitude of v-points
- units :
- degrees North
array([[ 7.75840846, 7.86968505, 7.98097613, ..., 20.23743283, 20.32853099, 20.41929115], [ 7.94654137, 8.05803956, 8.16955047, ..., 20.43654939, 20.52758743, 20.61828471], [ 8.13454206, 8.24626301, 8.35799495, ..., 20.63569259, 20.72667168, 20.81730724], ..., [34.88184053, 35.04408237, 35.20620594, ..., 51.59028295, 51.69192452, 51.79264323], [35.02809386, 35.19075973, 35.35330797, ..., 51.78398355, 51.88588867, 51.98686607], [35.17387447, 35.33696536, 35.49993927, ..., 51.97754248, 52.07971517, 52.18095524]], shape=(161, 122)) - lon_v(eta_v, xi_rho)float64231.8 232.0 232.2 ... 237.3 237.6
- long_name :
- longitude of v-points
- units :
- degrees East
array([[231.77294597, 231.96302429, 232.15337591, ..., 255.95043605, 256.16267385, 256.37497025], [231.66051421, 231.85054689, 232.04085548, ..., 255.85313541, 256.06567675, 256.27827928], [231.54780566, 231.73779358, 231.92806005, ..., 255.75577079, 255.96861847, 256.18152994], ..., [208.52050721, 208.69951783, 208.87940243, ..., 237.30044119, 237.61289958, 237.92647577], [208.32223022, 208.50098705, 208.68062238, ..., 237.13553756, 237.44911519, 237.76382812], [208.12308024, 208.30157779, 208.48095836, ..., 236.96949051, 237.2841955 , 237.6000536 ]], shape=(161, 122)) - lat_u(eta_rho, xi_u)float647.72 7.831 7.942 ... 52.13 52.23
- long_name :
- latitude of u-points
- units :
- degrees North
array([[ 7.71989212, 7.83106589, 7.94225351, ..., 20.09214977, 20.18344553, 20.27440573], [ 7.90820139, 8.01959529, 8.13100127, ..., 20.29128185, 20.38251829, 20.4734164 ], [ 8.09637954, 8.20799474, 8.31962027, ..., 20.49044026, 20.58161854, 20.67245574], ..., [35.0363117 , 35.19870633, 35.3609802 , ..., 51.6358225 , 51.73805451, 51.83936422], [35.18254189, 35.34536138, 35.50806074, ..., 51.82931774, 51.9318177 , 52.03339052], [35.32829793, 35.49154325, 35.65466912, ..., 52.022668 , 52.12543995, 52.22727989]], shape=(162, 121)) - lon_u(eta_rho, xi_u)float64231.9 232.1 232.3 ... 237.0 237.4
- long_name :
- longitude of u-points
- units :
- degrees East
array([[231.92414366, 232.11438102, 232.30488992, ..., 255.89305738, 256.10511406, 256.31722944], [231.8118266 , 232.00201918, 232.19248591, ..., 255.79563831, 256.00799584, 256.22041465], [231.6992345 , 231.88938319, 232.07980867, ..., 255.69815517, 255.91081633, 256.12354138], ..., [208.51102895, 208.69035216, 208.87055434, ..., 237.06233232, 237.37478101, 237.68836023], [208.31218832, 208.49125727, 208.67120977, ..., 236.89630858, 237.20987174, 237.52458308], [208.11246972, 208.29127888, 208.47097613, ..., 236.72912868, 237.04381427, 237.35966602]], shape=(162, 121))
- ocean_time(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- Time since 1995/01/01
- units :
- second
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Dask graph 4 chunks in 6 graph layers Data type float64 numpy.ndarray - time_step(time, auxil)int32dask.array<chunksize=(1, 6), meta=np.ndarray>
- long_name :
- time step and record numbers from initialization
Array Chunk Bytes 96 B 24 B Shape (4, 6) (1, 6) Dask graph 4 chunks in 6 graph layers Data type int32 numpy.ndarray - zeta(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- free-surface elevation
- units :
- meter
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - ubar(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- vertically averaged u-momentum component
- units :
- meter second-1
Array Chunk Bytes 612.56 kiB 19.53 kiB Shape (4, 162, 121) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - vbar(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- vertically averaged v-momentum component
- units :
- meter second-1
Array Chunk Bytes 613.81 kiB 19.53 kiB Shape (4, 161, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- 3D pH
- units :
- pH
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- 3D pH (alternate CO2)
- units :
- pH
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers
- units :
- pH
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_ABIO_PH_SURF(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for abiotic tracers
- units :
- pH
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF_ALT_CO2(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers (alternate CO2)
- units :
- pH
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - u(time, s_rho, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- u-momentum component
- units :
- meter second-1
Array Chunk Bytes 59.82 MiB 0.95 MiB Shape (4, 100, 162, 121) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - v(time, s_rho, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- v-momentum component
- units :
- meter second-1
Array Chunk Bytes 59.94 MiB 0.95 MiB Shape (4, 100, 161, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - temp(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- potential temperature
- units :
- Celsius
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - salt(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- salinity
- units :
- PSU
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - PO4(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Phosphate
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - NO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Nitrate
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - SiO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Silicate
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - NH4(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Ammonia
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - Fe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - Lig(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Iron Binding Ligand
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - O2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Oxygen
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DIC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DIC_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon, Alternative CO2
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - ALK(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Alkalinity
- units :
- meq/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - ALK_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Alkalinity, Alternative CO2
- units :
- meq/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Organic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DON(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Dissolved Organic Nitrogen
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Organic Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOPr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DOP
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DONr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DON
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOCr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DOC
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - zooC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Zooplankton Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spCaCO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto CaCO3
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Diatom Carbon
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatSi(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Silicon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DU_avg2(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <<fast-time averaged ubar(:,:,n+1/2)>>
Array Chunk Bytes 612.56 kiB 19.53 kiB Shape (4, 162, 121) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - DV_avg2(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <<fast-time-averaged vbar(:,:,n+1/2)>>
Array Chunk Bytes 613.81 kiB 19.53 kiB Shape (4, 161, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - DU_avg_bak(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged ubar(:,:,n-1)>
Array Chunk Bytes 612.56 kiB 19.53 kiB Shape (4, 162, 121) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - DV_avg_bak(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged vbar(:,:,n-1)>
Array Chunk Bytes 613.81 kiB 19.53 kiB Shape (4, 161, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - hbls(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- Thickness of KPP surface boundary layer
- units :
- meter
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - hbbl(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- Thickness of KPP bottom boundary layer
- units :
- meter
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - u_slow(time, s_rho, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered u
- units :
- m/s
Array Chunk Bytes 59.82 MiB 0.95 MiB Shape (4, 100, 162, 121) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - v_slow(time, s_rho, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered v
- units :
- m/s
Array Chunk Bytes 59.94 MiB 0.95 MiB Shape (4, 100, 161, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - p_slow(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered pressure
- units :
- Pa??
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - mask_rho(eta_rho, xi_rho)float64...
- Long_name :
- mask at rho-points
- units :
- land/water (0/1)
- Notes :
- Mask has been modified to match the parent grid Mask at the boundaries
[19764 values with dtype=float64]
- mask_u(eta_rho, xi_u)int321 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0
- long_name :
- Mask at u-points
- units :
- land/water (0/1)
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], shape=(162, 121), dtype=int32) - mask_v(eta_v, xi_rho)int321 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0
- long_name :
- Mask at v-points
- units :
- land/water (0/1)
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], shape=(161, 122), dtype=int32)
- title :
- eastpac25km , 25km resolution
- grid_file :
- /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/epac25km_grd.000.nc
- init_file :
- /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastpac25km_rst.19980101000000.000.nc
- ntimes :
- 4610
- ndtfast :
- 45
- dt :
- 600.0
- dtfast :
- 13.333333333333334
- theta_s :
- 5.0
- theta_b :
- 2.0
- hc :
- 300.0
- Cs_w :
- [-1.00000000e+00 -9.83735238e-01 -9.66697847e-01 -9.48934833e-01 -9.30497932e-01 -9.11442824e-01 -8.91828326e-01 -8.71715602e-01 -8.51167398e-01 -8.30247303e-01 -8.09019067e-01 -7.87545970e-01 -7.65890248e-01 -7.44112585e-01 -7.22271672e-01 -7.00423829e-01 -6.78622689e-01 -6.56918954e-01 -6.35360192e-01 -6.13990705e-01 -5.92851439e-01 -5.71979937e-01 -5.51410339e-01 -5.31173415e-01 -5.11296624e-01 -4.91804203e-01 -4.72717281e-01 -4.54054004e-01 -4.35829676e-01 -4.18056911e-01 -4.00745794e-01 -3.83904042e-01 -3.67537172e-01 -3.51648664e-01 -3.36240125e-01 -3.21311453e-01 -3.06860989e-01 -2.92885669e-01 -2.79381169e-01 -2.66342043e-01 -2.53761851e-01 -2.41633283e-01 -2.29948277e-01 -2.18698121e-01 -2.07873557e-01 -1.97464874e-01 -1.87461989e-01 -1.77854528e-01 -1.68631899e-01 -1.59783353e-01 -1.51298043e-01 -1.43165082e-01 -1.35373585e-01 -1.27912713e-01 -1.20771713e-01 -1.13939947e-01 -1.07406924e-01 -1.01162327e-01 -9.51960296e-02 -8.94981213e-02 -8.40589181e-02 -7.88689787e-02 -7.39191144e-02 -6.92003983e-02 -6.47041721e-02 -6.04220511e-02 -5.63459281e-02 -5.24679753e-02 -4.87806460e-02 -4.52766740e-02 -4.19490733e-02 -3.87911360e-02 -3.57964303e-02 -3.29587975e-02 -3.02723487e-02 -2.77314609e-02 -2.53307733e-02 -2.30651826e-02 -2.09298389e-02 -1.89201410e-02 -1.70317316e-02 -1.52604930e-02 -1.36025420e-02 -1.20542257e-02 -1.06121170e-02 -9.27300977e-03 -8.03391530e-03 -6.89205773e-03 -5.84487036e-03 -4.88999201e-03 -4.02526351e-03 -3.24872452e-03 -2.55861056e-03 -1.95335031e-03 -1.43156315e-03 -9.92056980e-04 -6.33826341e-04 -3.56050802e-04 -1.58093625e-04 -3.95007397e-05 0.00000000e+00]
- Cs_r :
- [-9.91966929e-01 -9.75310303e-01 -9.57903911e-01 -9.39797221e-01 -9.21044043e-01 -9.01701722e-01 -8.81830349e-01 -8.61491972e-01 -8.40749848e-01 -8.19667729e-01 -7.98309206e-01 -7.76737103e-01 -7.55012940e-01 -7.33196455e-01 -7.11345198e-01 -6.89514181e-01 -6.67755598e-01 -6.46118605e-01 -6.24649151e-01 -6.03389868e-01 -5.82380004e-01 -5.61655398e-01 -5.41248500e-01 -5.21188414e-01 -5.01500975e-01 -4.82208852e-01 -4.63331666e-01 -4.44886122e-01 -4.26886160e-01 -4.09343110e-01 -3.92265853e-01 -3.75660984e-01 -3.59532980e-01 -3.43884367e-01 -3.28715875e-01 -3.14026605e-01 -2.99814178e-01 -2.86074882e-01 -2.72803815e-01 -2.59995018e-01 -2.47641602e-01 -2.35735865e-01 -2.24269409e-01 -2.13233237e-01 -2.02617853e-01 -1.92413349e-01 -1.82609488e-01 -1.73195779e-01 -1.64161542e-01 -1.55495973e-01 -1.47188200e-01 -1.39227330e-01 -1.31602496e-01 -1.24302897e-01 -1.17317835e-01 -1.10636741e-01 -1.04249210e-01 -9.81450152e-02 -9.23141376e-02 -8.67467775e-02 -8.14333708e-02 -7.63646013e-02 -7.15314104e-02 -6.69250045e-02 -6.25368617e-02 -5.83587357e-02 -5.43826587e-02 -5.06009434e-02 -4.70061835e-02 -4.35912530e-02 -4.03493052e-02 -3.72737705e-02 -3.43583542e-02 -3.15970327e-02 -2.89840508e-02 -2.65139170e-02 -2.41813998e-02 -2.19815232e-02 -1.99095622e-02 -1.79610382e-02 -1.61317141e-02 -1.44175902e-02 -1.28148990e-02 -1.13201009e-02 -9.92988001e-03 -8.64113938e-03 -7.45099718e-03 -6.35678262e-03 -5.35603221e-03 -4.44648617e-03 -3.62608516e-03 -2.89296712e-03 -2.24546446e-03 -1.68210150e-03 -1.20159214e-03 -8.02837815e-04 -4.84925772e-04 -2.47127569e-04 -8.88979122e-05 -9.87376857e-06]
- rho0 :
- 1027.4
- rho0_units :
- kg/m^3
- visc2 :
- 0.0
- visc2_units :
- m^2/s
- gamma2 :
- 1.0
- tnu2 :
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- tnu2_units :
- m^2/s
- ubind :
- 0.2
- ubind_units :
- m/s
- v_sponge :
- 2500.0
- v_sponge_units :
- m^2/s
- rdrg :
- 0.0
- rdrg_units :
- m/s
- rdrg2 :
- 0.0
- rdrg2_units :
- nondimensional
- Zob :
- 0.02
- Zob_units :
- m
- SRCS :
- SRCS $(shell ls *$(UPF77_ext)) SRCS : $(filter-out $(EXCL), $(SRCS)) SRCS : $(SRCS) $(INCL)
- CPPS :
- <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV_ISONEUTRAL NONLIN_EOS SPLIT_EOS SALINITY BULK_FRC T_FRC_BRY Z_FRC_BRY M3_FRC_BRY M2_FRC_BRY SPONGE UV_VIS2 TS_DIF2 LMD_MIXING LMD_KPP LMD_NONLOCAL LMD_RIMIX LMD_CONVEC LMD_BKPP CURVGRID SPHERICAL MASKING MASK_LAND_DATA OBC_M2FLATHER OBC_M3ORLANSKI OBC_TORLANSKI OBC_WEST OBC_NORTH OBC_SOUTH AVERAGES DIAGNOSTICS MARBL MARBL_DIAGS NOX_FORCING NHY_FORCING ALK_SOURCE PCO2AIR_FORCING TIDES POT_TIDES SSH_TIDES UV_TIDES <pre_step3d4S.F> SPLINE_UV SPLINE_TS <step3d_uv1.F> UPSTREAM_UV SPLINE_UV <step3d_uv2.F> DELTA=0.28000000000000003 EPSIL=0.35999999999999999 GAMMA=8.3333333333299994E-002 ALPHA_MAX=2.0 <step3d_t_ISO.F> SPLINE_TS <set_depth.F> NOW=3.63 MID=4.47 BAK=2.05 (N-M+B-1)/B=0.102439024 <lmd_kpp.F> INT_AT_RHO_POINTS SMOOTH_HBL <set_global_definitions.h> CORR_COUPLED_MODE EXTRAP_BAR_FLUXES IMPLCT_NO_SLIP_BTTM_BC VAR_RHO_2D
- surf_forcing_strings :
- bc_options :
- OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, OBC_TORLANSKI,
- git_version :
- type :
- ROMS restart file
Reading files with wildcards#
[7]:
%%time
roms_output = ROMSOutput(
grid=grid,
path="/anvil/projects/x-ees250129/Datasets/ROMSOutput/eastpac25km/*rst*.nc",
use_dask=True,
)
CPU times: user 119 ms, sys: 11 ms, total: 130 ms
Wall time: 134 ms
The specified directory contains 222 restart files, all of which were concatenated into one dataset. (The concatenation takes some time, even though we used use_dask = True.)
[8]:
roms_output.ds
[8]:
<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_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
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
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, 50, 50), meta=np.ndarray>
ubar (time, eta_rho, xi_u) float64 627kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
vbar (time, eta_v, xi_rho) float64 629kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
MARBL_PH_3D (time, s_rho, eta_rho, xi_rho) float64 63MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
... ...
u_slow (time, s_rho, eta_rho, xi_u) float64 63MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
v_slow (time, s_rho, eta_v, xi_rho) float64 63MB dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
p_slow (time, s_rho, eta_rho, xi_rho) float64 63MB dask.array<chunksize=(1, 50, 50, 50), 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 filexarray.Dataset
- time: 4
- auxil: 6
- eta_rho: 162
- xi_rho: 122
- xi_u: 121
- eta_v: 161
- s_rho: 100
- time(time)datetime64[ns]1998-01-05T23:50:00 ... 1999-02-01
array(['1998-01-05T23:50:00.000000000', '1998-01-06T00:00:00.000000000', '1999-01-31T23:50:00.000000000', '1999-02-01T00:00:00.000000000'], dtype='datetime64[ns]') - lon_rho(eta_rho, xi_rho)float64...
- Long_name :
- longitude of rho-points
- units :
- degree East
[19764 values with dtype=float64]
- lat_rho(eta_rho, xi_rho)float64...
- Long_name :
- latitude of rho-points
- units :
- degree North
[19764 values with dtype=float64]
- lat_v(eta_v, xi_rho)float647.758 7.87 7.981 ... 52.08 52.18
- long_name :
- latitude of v-points
- units :
- degrees North
array([[ 7.75840846, 7.86968505, 7.98097613, ..., 20.23743283, 20.32853099, 20.41929115], [ 7.94654137, 8.05803956, 8.16955047, ..., 20.43654939, 20.52758743, 20.61828471], [ 8.13454206, 8.24626301, 8.35799495, ..., 20.63569259, 20.72667168, 20.81730724], ..., [34.88184053, 35.04408237, 35.20620594, ..., 51.59028295, 51.69192452, 51.79264323], [35.02809386, 35.19075973, 35.35330797, ..., 51.78398355, 51.88588867, 51.98686607], [35.17387447, 35.33696536, 35.49993927, ..., 51.97754248, 52.07971517, 52.18095524]], shape=(161, 122)) - lon_v(eta_v, xi_rho)float64231.8 232.0 232.2 ... 237.3 237.6
- long_name :
- longitude of v-points
- units :
- degrees East
array([[231.77294597, 231.96302429, 232.15337591, ..., 255.95043605, 256.16267385, 256.37497025], [231.66051421, 231.85054689, 232.04085548, ..., 255.85313541, 256.06567675, 256.27827928], [231.54780566, 231.73779358, 231.92806005, ..., 255.75577079, 255.96861847, 256.18152994], ..., [208.52050721, 208.69951783, 208.87940243, ..., 237.30044119, 237.61289958, 237.92647577], [208.32223022, 208.50098705, 208.68062238, ..., 237.13553756, 237.44911519, 237.76382812], [208.12308024, 208.30157779, 208.48095836, ..., 236.96949051, 237.2841955 , 237.6000536 ]], shape=(161, 122)) - lat_u(eta_rho, xi_u)float647.72 7.831 7.942 ... 52.13 52.23
- long_name :
- latitude of u-points
- units :
- degrees North
array([[ 7.71989212, 7.83106589, 7.94225351, ..., 20.09214977, 20.18344553, 20.27440573], [ 7.90820139, 8.01959529, 8.13100127, ..., 20.29128185, 20.38251829, 20.4734164 ], [ 8.09637954, 8.20799474, 8.31962027, ..., 20.49044026, 20.58161854, 20.67245574], ..., [35.0363117 , 35.19870633, 35.3609802 , ..., 51.6358225 , 51.73805451, 51.83936422], [35.18254189, 35.34536138, 35.50806074, ..., 51.82931774, 51.9318177 , 52.03339052], [35.32829793, 35.49154325, 35.65466912, ..., 52.022668 , 52.12543995, 52.22727989]], shape=(162, 121)) - lon_u(eta_rho, xi_u)float64231.9 232.1 232.3 ... 237.0 237.4
- long_name :
- longitude of u-points
- units :
- degrees East
array([[231.92414366, 232.11438102, 232.30488992, ..., 255.89305738, 256.10511406, 256.31722944], [231.8118266 , 232.00201918, 232.19248591, ..., 255.79563831, 256.00799584, 256.22041465], [231.6992345 , 231.88938319, 232.07980867, ..., 255.69815517, 255.91081633, 256.12354138], ..., [208.51102895, 208.69035216, 208.87055434, ..., 237.06233232, 237.37478101, 237.68836023], [208.31218832, 208.49125727, 208.67120977, ..., 236.89630858, 237.20987174, 237.52458308], [208.11246972, 208.29127888, 208.47097613, ..., 236.72912868, 237.04381427, 237.35966602]], shape=(162, 121))
- ocean_time(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- Time since 1995/01/01
- units :
- second
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Dask graph 4 chunks in 6 graph layers Data type float64 numpy.ndarray - time_step(time, auxil)int32dask.array<chunksize=(1, 6), meta=np.ndarray>
- long_name :
- time step and record numbers from initialization
Array Chunk Bytes 96 B 24 B Shape (4, 6) (1, 6) Dask graph 4 chunks in 6 graph layers Data type int32 numpy.ndarray - zeta(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- free-surface elevation
- units :
- meter
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - ubar(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- vertically averaged u-momentum component
- units :
- meter second-1
Array Chunk Bytes 612.56 kiB 19.53 kiB Shape (4, 162, 121) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - vbar(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- vertically averaged v-momentum component
- units :
- meter second-1
Array Chunk Bytes 613.81 kiB 19.53 kiB Shape (4, 161, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- 3D pH
- units :
- pH
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- 3D pH (alternate CO2)
- units :
- pH
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers
- units :
- pH
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_ABIO_PH_SURF(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for abiotic tracers
- units :
- pH
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF_ALT_CO2(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers (alternate CO2)
- units :
- pH
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - u(time, s_rho, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- u-momentum component
- units :
- meter second-1
Array Chunk Bytes 59.82 MiB 0.95 MiB Shape (4, 100, 162, 121) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - v(time, s_rho, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- v-momentum component
- units :
- meter second-1
Array Chunk Bytes 59.94 MiB 0.95 MiB Shape (4, 100, 161, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - temp(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- potential temperature
- units :
- Celsius
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - salt(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- salinity
- units :
- PSU
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - PO4(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Phosphate
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - NO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Nitrate
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - SiO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Silicate
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - NH4(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Ammonia
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - Fe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - Lig(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Iron Binding Ligand
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - O2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Oxygen
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DIC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DIC_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon, Alternative CO2
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - ALK(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Alkalinity
- units :
- meq/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - ALK_ALT_CO2(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Alkalinity, Alternative CO2
- units :
- meq/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Organic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DON(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Dissolved Organic Nitrogen
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Dissolved Organic Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOPr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DOP
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DONr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DON
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DOCr(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Refractory DOC
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - zooC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Zooplankton Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - spCaCO3(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Small Phyto CaCO3
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Diatom Carbon
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diatSi(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diatom Silicon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazChl(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazC(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Carbon
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazP(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - diazFe(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- Diazotroph Iron
- units :
- mmol/m^3
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - DU_avg2(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <<fast-time averaged ubar(:,:,n+1/2)>>
Array Chunk Bytes 612.56 kiB 19.53 kiB Shape (4, 162, 121) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - DV_avg2(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <<fast-time-averaged vbar(:,:,n+1/2)>>
Array Chunk Bytes 613.81 kiB 19.53 kiB Shape (4, 161, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - DU_avg_bak(time, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged ubar(:,:,n-1)>
Array Chunk Bytes 612.56 kiB 19.53 kiB Shape (4, 162, 121) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - DV_avg_bak(time, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged vbar(:,:,n-1)>
Array Chunk Bytes 613.81 kiB 19.53 kiB Shape (4, 161, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - hbls(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- Thickness of KPP surface boundary layer
- units :
- meter
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - hbbl(time, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50), meta=np.ndarray>
- long_name :
- Thickness of KPP bottom boundary layer
- units :
- meter
Array Chunk Bytes 617.62 kiB 19.53 kiB Shape (4, 162, 122) (1, 50, 50) Dask graph 48 chunks in 6 graph layers Data type float64 numpy.ndarray - u_slow(time, s_rho, eta_rho, xi_u)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered u
- units :
- m/s
Array Chunk Bytes 59.82 MiB 0.95 MiB Shape (4, 100, 162, 121) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - v_slow(time, s_rho, eta_v, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered v
- units :
- m/s
Array Chunk Bytes 59.94 MiB 0.95 MiB Shape (4, 100, 161, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - p_slow(time, s_rho, eta_rho, xi_rho)float64dask.array<chunksize=(1, 50, 50, 50), meta=np.ndarray>
- long_name :
- time filtered pressure
- units :
- Pa??
Array Chunk Bytes 60.31 MiB 0.95 MiB Shape (4, 100, 162, 122) (1, 50, 50, 50) Dask graph 96 chunks in 6 graph layers Data type float64 numpy.ndarray - mask_rho(eta_rho, xi_rho)float64...
- Long_name :
- mask at rho-points
- units :
- land/water (0/1)
- Notes :
- Mask has been modified to match the parent grid Mask at the boundaries
[19764 values with dtype=float64]
- mask_u(eta_rho, xi_u)int321 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0
- long_name :
- Mask at u-points
- units :
- land/water (0/1)
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], shape=(162, 121), dtype=int32) - mask_v(eta_v, xi_rho)int321 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0
- long_name :
- Mask at v-points
- units :
- land/water (0/1)
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], shape=(161, 122), dtype=int32)
- title :
- eastpac25km , 25km resolution
- grid_file :
- /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/epac25km_grd.000.nc
- init_file :
- /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastpac25km_rst.19980101000000.000.nc
- ntimes :
- 4610
- ndtfast :
- 45
- dt :
- 600.0
- dtfast :
- 13.333333333333334
- theta_s :
- 5.0
- theta_b :
- 2.0
- hc :
- 300.0
- Cs_w :
- [-1.00000000e+00 -9.83735238e-01 -9.66697847e-01 -9.48934833e-01 -9.30497932e-01 -9.11442824e-01 -8.91828326e-01 -8.71715602e-01 -8.51167398e-01 -8.30247303e-01 -8.09019067e-01 -7.87545970e-01 -7.65890248e-01 -7.44112585e-01 -7.22271672e-01 -7.00423829e-01 -6.78622689e-01 -6.56918954e-01 -6.35360192e-01 -6.13990705e-01 -5.92851439e-01 -5.71979937e-01 -5.51410339e-01 -5.31173415e-01 -5.11296624e-01 -4.91804203e-01 -4.72717281e-01 -4.54054004e-01 -4.35829676e-01 -4.18056911e-01 -4.00745794e-01 -3.83904042e-01 -3.67537172e-01 -3.51648664e-01 -3.36240125e-01 -3.21311453e-01 -3.06860989e-01 -2.92885669e-01 -2.79381169e-01 -2.66342043e-01 -2.53761851e-01 -2.41633283e-01 -2.29948277e-01 -2.18698121e-01 -2.07873557e-01 -1.97464874e-01 -1.87461989e-01 -1.77854528e-01 -1.68631899e-01 -1.59783353e-01 -1.51298043e-01 -1.43165082e-01 -1.35373585e-01 -1.27912713e-01 -1.20771713e-01 -1.13939947e-01 -1.07406924e-01 -1.01162327e-01 -9.51960296e-02 -8.94981213e-02 -8.40589181e-02 -7.88689787e-02 -7.39191144e-02 -6.92003983e-02 -6.47041721e-02 -6.04220511e-02 -5.63459281e-02 -5.24679753e-02 -4.87806460e-02 -4.52766740e-02 -4.19490733e-02 -3.87911360e-02 -3.57964303e-02 -3.29587975e-02 -3.02723487e-02 -2.77314609e-02 -2.53307733e-02 -2.30651826e-02 -2.09298389e-02 -1.89201410e-02 -1.70317316e-02 -1.52604930e-02 -1.36025420e-02 -1.20542257e-02 -1.06121170e-02 -9.27300977e-03 -8.03391530e-03 -6.89205773e-03 -5.84487036e-03 -4.88999201e-03 -4.02526351e-03 -3.24872452e-03 -2.55861056e-03 -1.95335031e-03 -1.43156315e-03 -9.92056980e-04 -6.33826341e-04 -3.56050802e-04 -1.58093625e-04 -3.95007397e-05 0.00000000e+00]
- Cs_r :
- [-9.91966929e-01 -9.75310303e-01 -9.57903911e-01 -9.39797221e-01 -9.21044043e-01 -9.01701722e-01 -8.81830349e-01 -8.61491972e-01 -8.40749848e-01 -8.19667729e-01 -7.98309206e-01 -7.76737103e-01 -7.55012940e-01 -7.33196455e-01 -7.11345198e-01 -6.89514181e-01 -6.67755598e-01 -6.46118605e-01 -6.24649151e-01 -6.03389868e-01 -5.82380004e-01 -5.61655398e-01 -5.41248500e-01 -5.21188414e-01 -5.01500975e-01 -4.82208852e-01 -4.63331666e-01 -4.44886122e-01 -4.26886160e-01 -4.09343110e-01 -3.92265853e-01 -3.75660984e-01 -3.59532980e-01 -3.43884367e-01 -3.28715875e-01 -3.14026605e-01 -2.99814178e-01 -2.86074882e-01 -2.72803815e-01 -2.59995018e-01 -2.47641602e-01 -2.35735865e-01 -2.24269409e-01 -2.13233237e-01 -2.02617853e-01 -1.92413349e-01 -1.82609488e-01 -1.73195779e-01 -1.64161542e-01 -1.55495973e-01 -1.47188200e-01 -1.39227330e-01 -1.31602496e-01 -1.24302897e-01 -1.17317835e-01 -1.10636741e-01 -1.04249210e-01 -9.81450152e-02 -9.23141376e-02 -8.67467775e-02 -8.14333708e-02 -7.63646013e-02 -7.15314104e-02 -6.69250045e-02 -6.25368617e-02 -5.83587357e-02 -5.43826587e-02 -5.06009434e-02 -4.70061835e-02 -4.35912530e-02 -4.03493052e-02 -3.72737705e-02 -3.43583542e-02 -3.15970327e-02 -2.89840508e-02 -2.65139170e-02 -2.41813998e-02 -2.19815232e-02 -1.99095622e-02 -1.79610382e-02 -1.61317141e-02 -1.44175902e-02 -1.28148990e-02 -1.13201009e-02 -9.92988001e-03 -8.64113938e-03 -7.45099718e-03 -6.35678262e-03 -5.35603221e-03 -4.44648617e-03 -3.62608516e-03 -2.89296712e-03 -2.24546446e-03 -1.68210150e-03 -1.20159214e-03 -8.02837815e-04 -4.84925772e-04 -2.47127569e-04 -8.88979122e-05 -9.87376857e-06]
- rho0 :
- 1027.4
- rho0_units :
- kg/m^3
- visc2 :
- 0.0
- visc2_units :
- m^2/s
- gamma2 :
- 1.0
- tnu2 :
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- tnu2_units :
- m^2/s
- ubind :
- 0.2
- ubind_units :
- m/s
- v_sponge :
- 2500.0
- v_sponge_units :
- m^2/s
- rdrg :
- 0.0
- rdrg_units :
- m/s
- rdrg2 :
- 0.0
- rdrg2_units :
- nondimensional
- Zob :
- 0.02
- Zob_units :
- m
- SRCS :
- SRCS $(shell ls *$(UPF77_ext)) SRCS : $(filter-out $(EXCL), $(SRCS)) SRCS : $(SRCS) $(INCL)
- CPPS :
- <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV_ISONEUTRAL NONLIN_EOS SPLIT_EOS SALINITY BULK_FRC T_FRC_BRY Z_FRC_BRY M3_FRC_BRY M2_FRC_BRY SPONGE UV_VIS2 TS_DIF2 LMD_MIXING LMD_KPP LMD_NONLOCAL LMD_RIMIX LMD_CONVEC LMD_BKPP CURVGRID SPHERICAL MASKING MASK_LAND_DATA OBC_M2FLATHER OBC_M3ORLANSKI OBC_TORLANSKI OBC_WEST OBC_NORTH OBC_SOUTH AVERAGES DIAGNOSTICS MARBL MARBL_DIAGS NOX_FORCING NHY_FORCING ALK_SOURCE PCO2AIR_FORCING TIDES POT_TIDES SSH_TIDES UV_TIDES <pre_step3d4S.F> SPLINE_UV SPLINE_TS <step3d_uv1.F> UPSTREAM_UV SPLINE_UV <step3d_uv2.F> DELTA=0.28000000000000003 EPSIL=0.35999999999999999 GAMMA=8.3333333333299994E-002 ALPHA_MAX=2.0 <step3d_t_ISO.F> SPLINE_TS <set_depth.F> NOW=3.63 MID=4.47 BAK=2.05 (N-M+B-1)/B=0.102439024 <lmd_kpp.F> INT_AT_RHO_POINTS SMOOTH_HBL <set_global_definitions.h> CORR_COUPLED_MODE EXTRAP_BAR_FLUXES IMPLCT_NO_SLIP_BTTM_BC VAR_RHO_2D
- surf_forcing_strings :
- bc_options :
- OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, OBC_TORLANSKI,
- git_version :
- type :
- ROMS restart file
Let’s verify that the time dimension has been concatenated in a linear sequence.
[9]:
roms_output.ds.time.plot()
[9]:
[<matplotlib.lines.Line2D at 0x1518165b8910>]
Adjusting the depth for sea surface height#
ROMS uses a terrain-following vertical coordinate system. To visualize or regrid ROMS output data on a depth coordinate in later notebooks, ROMS-Tools internally computes the corresponding depth values. The treatment of sea surface height (SSH) is controlled by the adjust_depth_for_sea_surface_height parameter:
If
adjust_depth_for_sea_surface_height = False(default), a constant sea surface height is assumed: \(\zeta(x,y,t) = 0\), which corresponds to measuring depth relative to the surface.If
adjust_depth_for_sea_surface_height = True, depth calculations account for spatial and temporal variations in SSH, making the depths time-dependent. This approach corresponds to measuring depth as elevation above the bottom.
For simplicity, the default setting is adjust_depth_for_sea_surface_height = False, which has been used so far in this notebook.
Next, we will enable adjust_depth_for_sea_surface_height = True.
[10]:
%%time
roms_output_adjusted_for_ssh = ROMSOutput(
grid=grid,
path="/anvil/projects/x-ees250129/Datasets/ROMSOutput/eastpac25km/*rst*.nc",
adjust_depth_for_sea_surface_height=True,
use_dask=True,
)
CPU times: user 118 ms, sys: 11 ms, total: 129 ms
Wall time: 129 ms
The adjust_depth_for_sea_surface_height parameter will affect:
Plots of fields with a vertical dimension, such as those in this notebook.
Regridding onto a lat-lon-z grid, affecting the regridded 3D variables.
Let’s explore the second point further by performing two regridding operations: one with adjust_depth_for_sea_surface_height = True and one with adjust_depth_for_sea_surface_height = False. For more details on regridding, see this notebook.
[11]:
ds_regridded = roms_output.regrid()
[12]:
ds_regridded
[12]:
<xarray.Dataset> Size: 6GB
Dimensions: (time: 4, lat: 185, lon: 197, depth: 100,
eta_v: 161, xi_u: 121, 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: eta_v, xi_u, auxil
Data variables: (12/58)
NH4 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
DOCr (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>
diatFe (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
DV_avg2 (time, eta_v, lat, lon) float64 188MB dask.array<chunksize=(1, 161, 185, 197), meta=np.ndarray>
mask_u (xi_u, lat, lon) float64 35MB dask.array<chunksize=(121, 185, 197), meta=np.ndarray>
... ...
salt (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
diatC (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
diatChl (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
NO3 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
diatP (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
MARBL_PH_SURF_ALT_CO2 (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), 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: bilinearxarray.Dataset
- time: 4
- lat: 185
- lon: 197
- depth: 100
- eta_v: 161
- xi_u: 121
- auxil: 6
- time(time)datetime64[ns]1998-01-05T23:50:00 ... 1999-02-01
- long_name :
- Time
array(['1998-01-05T23:50:00.000000000', '1998-01-06T00:00:00.000000000', '1999-01-31T23:50:00.000000000', '1999-02-01T00:00:00.000000000'], dtype='datetime64[ns]') - lat(lat)float327.0 7.25 7.5 ... 52.5 52.75 53.0
- long_name :
- Latitude
- units :
- Degrees North
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) - lon(lon)float32208.0 208.2 208.5 ... 256.8 257.0
- long_name :
- Longitude
- units :
- Degrees East
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) - depth(depth)float321.46 4.45 ... 5.281e+03 5.528e+03
- long_name :
- Depth
- units :
- m
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)
- NH4(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Ammonia
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DOCr(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Refractory DOC
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - zooC(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Zooplankton Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diatFe(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diatom Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DV_avg2(time, eta_v, lat, lon)float64dask.array<chunksize=(1, 161, 185, 197), meta=np.ndarray>
- long_name :
- <<fast-time-averaged vbar(:,:,n+1/2)>>
Array Chunk Bytes 179.07 MiB 44.77 MiB Shape (4, 161, 185, 197) (1, 161, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - mask_u(xi_u, lat, lon)float64dask.array<chunksize=(121, 185, 197), meta=np.ndarray>
- long_name :
- Mask at u-points
- units :
- land/water (0/1)
Array Chunk Bytes 33.64 MiB 33.64 MiB Shape (121, 185, 197) (121, 185, 197) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray - mask_v(eta_v, lat, lon)float64dask.array<chunksize=(161, 185, 197), meta=np.ndarray>
- long_name :
- Mask at v-points
- units :
- land/water (0/1)
Array Chunk Bytes 44.77 MiB 44.77 MiB Shape (161, 185, 197) (161, 185, 197) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray - DV_avg_bak(time, eta_v, lat, lon)float64dask.array<chunksize=(1, 161, 185, 197), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged vbar(:,:,n-1)>
Array Chunk Bytes 179.07 MiB 44.77 MiB Shape (4, 161, 185, 197) (1, 161, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - O2(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Oxygen
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - SiO3(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Silicate
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - temp(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- potential temperature
- units :
- Celsius
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - PO4(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Phosphate
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diazFe(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diazotroph Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - Fe(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diatSi(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diatom Silicon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diazC(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diazotroph Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DOP(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Organic Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - u_slow(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- time filtered u, rotated to zonal component
- units :
- m/s
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 58 graph layers Data type float64 numpy.ndarray - spCaCO3(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Small Phyto CaCO3
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - ocean_time(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- Time since 1995/01/01
- units :
- second
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 15 graph layers Data type float64 numpy.ndarray - spP(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Small Phyto Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D_ALT_CO2(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- 3D pH (alternate CO2)
- units :
- pH
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - Lig(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Iron Binding Ligand
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DU_avg2(time, xi_u, lat, lon)float64dask.array<chunksize=(1, 121, 185, 197), meta=np.ndarray>
- long_name :
- <<fast-time averaged ubar(:,:,n+1/2)>>
Array Chunk Bytes 134.58 MiB 33.64 MiB Shape (4, 121, 185, 197) (1, 121, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - v_slow(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- time filtered v, rotated to meridional component
- units :
- m/s
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 58 graph layers Data type float64 numpy.ndarray - ALK(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Alkalinity
- units :
- meq/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - ubar(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- vertically averaged u-momentum component, rotated to zonal component
- units :
- meter second-1
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 50 graph layers Data type float64 numpy.ndarray - u(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- u-momentum component, rotated to zonal component
- units :
- meter second-1
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 58 graph layers Data type float64 numpy.ndarray - spC(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Small Phyto Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diazChl(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diazotroph Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DONr(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Refractory DON
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - time_step(time, auxil, lat, lon)float64dask.array<chunksize=(1, 6, 185, 197), meta=np.ndarray>
- long_name :
- time step and record numbers from initialization
Array Chunk Bytes 6.67 MiB 1.67 MiB Shape (4, 6, 185, 197) (1, 6, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - diazP(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diazotroph Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - spChl(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Small Phyto Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DU_avg_bak(time, xi_u, lat, lon)float64dask.array<chunksize=(1, 121, 185, 197), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged ubar(:,:,n-1)>
Array Chunk Bytes 134.58 MiB 33.64 MiB Shape (4, 121, 185, 197) (1, 121, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers
- units :
- pH
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - DOPr(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Refractory DOP
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - ALK_ALT_CO2(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Alkalinity, Alternative CO2
- units :
- meq/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - MARBL_ABIO_PH_SURF(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- surface pH for abiotic tracers
- units :
- pH
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - hbls(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- Thickness of KPP surface boundary layer
- units :
- meter
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - DIC(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DIC_ALT_CO2(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon, Alternative CO2
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DOC(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Organic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - DON(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Dissolved Organic Nitrogen
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - zeta(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- free-surface elevation
- units :
- meter
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - v(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- v-momentum component, rotated to meridional component
- units :
- meter second-1
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 58 graph layers Data type float64 numpy.ndarray - p_slow(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- time filtered pressure
- units :
- Pa??
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - spFe(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Small Phyto Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- 3D pH
- units :
- pH
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - mask_rho(lat, lon)float64dask.array<chunksize=(185, 197), meta=np.ndarray>
- Long_name :
- mask at rho-points
- units :
- land/water (0/1)
- Notes :
- Mask has been modified to match the parent grid Mask at the boundaries
Array Chunk Bytes 284.73 kiB 284.73 kiB Shape (185, 197) (185, 197) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray - vbar(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- vertically averaged v-momentum component, rotated to meridional component
- units :
- meter second-1
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 50 graph layers Data type float64 numpy.ndarray - hbbl(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- Thickness of KPP bottom boundary layer
- units :
- meter
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - salt(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- salinity
- units :
- PSU
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diatC(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Diatom Carbon
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diatChl(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diatom Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - NO3(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Nitrate
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - diatP(time, lat, lon, depth)float64dask.array<chunksize=(1, 185, 197, 100), meta=np.ndarray>
- long_name :
- Diatom Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 27.81 MiB Shape (4, 185, 197, 100) (1, 185, 197, 100) Dask graph 4 chunks in 24 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF_ALT_CO2(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers (alternate CO2)
- units :
- pH
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray
- title :
- eastpac25km , 25km resolution
- grid_file :
- /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/epac25km_grd.000.nc
- init_file :
- /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastpac25km_rst.19980101000000.000.nc
- ntimes :
- 4610
- ndtfast :
- 45
- dt :
- 600.0
- dtfast :
- 13.333333333333334
- theta_s :
- 5.0
- theta_b :
- 2.0
- hc :
- 300.0
- Cs_w :
- [-1.00000000e+00 -9.83735238e-01 -9.66697847e-01 -9.48934833e-01 -9.30497932e-01 -9.11442824e-01 -8.91828326e-01 -8.71715602e-01 -8.51167398e-01 -8.30247303e-01 -8.09019067e-01 -7.87545970e-01 -7.65890248e-01 -7.44112585e-01 -7.22271672e-01 -7.00423829e-01 -6.78622689e-01 -6.56918954e-01 -6.35360192e-01 -6.13990705e-01 -5.92851439e-01 -5.71979937e-01 -5.51410339e-01 -5.31173415e-01 -5.11296624e-01 -4.91804203e-01 -4.72717281e-01 -4.54054004e-01 -4.35829676e-01 -4.18056911e-01 -4.00745794e-01 -3.83904042e-01 -3.67537172e-01 -3.51648664e-01 -3.36240125e-01 -3.21311453e-01 -3.06860989e-01 -2.92885669e-01 -2.79381169e-01 -2.66342043e-01 -2.53761851e-01 -2.41633283e-01 -2.29948277e-01 -2.18698121e-01 -2.07873557e-01 -1.97464874e-01 -1.87461989e-01 -1.77854528e-01 -1.68631899e-01 -1.59783353e-01 -1.51298043e-01 -1.43165082e-01 -1.35373585e-01 -1.27912713e-01 -1.20771713e-01 -1.13939947e-01 -1.07406924e-01 -1.01162327e-01 -9.51960296e-02 -8.94981213e-02 -8.40589181e-02 -7.88689787e-02 -7.39191144e-02 -6.92003983e-02 -6.47041721e-02 -6.04220511e-02 -5.63459281e-02 -5.24679753e-02 -4.87806460e-02 -4.52766740e-02 -4.19490733e-02 -3.87911360e-02 -3.57964303e-02 -3.29587975e-02 -3.02723487e-02 -2.77314609e-02 -2.53307733e-02 -2.30651826e-02 -2.09298389e-02 -1.89201410e-02 -1.70317316e-02 -1.52604930e-02 -1.36025420e-02 -1.20542257e-02 -1.06121170e-02 -9.27300977e-03 -8.03391530e-03 -6.89205773e-03 -5.84487036e-03 -4.88999201e-03 -4.02526351e-03 -3.24872452e-03 -2.55861056e-03 -1.95335031e-03 -1.43156315e-03 -9.92056980e-04 -6.33826341e-04 -3.56050802e-04 -1.58093625e-04 -3.95007397e-05 0.00000000e+00]
- Cs_r :
- [-9.91966929e-01 -9.75310303e-01 -9.57903911e-01 -9.39797221e-01 -9.21044043e-01 -9.01701722e-01 -8.81830349e-01 -8.61491972e-01 -8.40749848e-01 -8.19667729e-01 -7.98309206e-01 -7.76737103e-01 -7.55012940e-01 -7.33196455e-01 -7.11345198e-01 -6.89514181e-01 -6.67755598e-01 -6.46118605e-01 -6.24649151e-01 -6.03389868e-01 -5.82380004e-01 -5.61655398e-01 -5.41248500e-01 -5.21188414e-01 -5.01500975e-01 -4.82208852e-01 -4.63331666e-01 -4.44886122e-01 -4.26886160e-01 -4.09343110e-01 -3.92265853e-01 -3.75660984e-01 -3.59532980e-01 -3.43884367e-01 -3.28715875e-01 -3.14026605e-01 -2.99814178e-01 -2.86074882e-01 -2.72803815e-01 -2.59995018e-01 -2.47641602e-01 -2.35735865e-01 -2.24269409e-01 -2.13233237e-01 -2.02617853e-01 -1.92413349e-01 -1.82609488e-01 -1.73195779e-01 -1.64161542e-01 -1.55495973e-01 -1.47188200e-01 -1.39227330e-01 -1.31602496e-01 -1.24302897e-01 -1.17317835e-01 -1.10636741e-01 -1.04249210e-01 -9.81450152e-02 -9.23141376e-02 -8.67467775e-02 -8.14333708e-02 -7.63646013e-02 -7.15314104e-02 -6.69250045e-02 -6.25368617e-02 -5.83587357e-02 -5.43826587e-02 -5.06009434e-02 -4.70061835e-02 -4.35912530e-02 -4.03493052e-02 -3.72737705e-02 -3.43583542e-02 -3.15970327e-02 -2.89840508e-02 -2.65139170e-02 -2.41813998e-02 -2.19815232e-02 -1.99095622e-02 -1.79610382e-02 -1.61317141e-02 -1.44175902e-02 -1.28148990e-02 -1.13201009e-02 -9.92988001e-03 -8.64113938e-03 -7.45099718e-03 -6.35678262e-03 -5.35603221e-03 -4.44648617e-03 -3.62608516e-03 -2.89296712e-03 -2.24546446e-03 -1.68210150e-03 -1.20159214e-03 -8.02837815e-04 -4.84925772e-04 -2.47127569e-04 -8.88979122e-05 -9.87376857e-06]
- rho0 :
- 1027.4
- rho0_units :
- kg/m^3
- visc2 :
- 0.0
- visc2_units :
- m^2/s
- gamma2 :
- 1.0
- tnu2 :
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- tnu2_units :
- m^2/s
- ubind :
- 0.2
- ubind_units :
- m/s
- v_sponge :
- 2500.0
- v_sponge_units :
- m^2/s
- rdrg :
- 0.0
- rdrg_units :
- m/s
- rdrg2 :
- 0.0
- rdrg2_units :
- nondimensional
- Zob :
- 0.02
- Zob_units :
- m
- SRCS :
- SRCS $(shell ls *$(UPF77_ext)) SRCS : $(filter-out $(EXCL), $(SRCS)) SRCS : $(SRCS) $(INCL)
- CPPS :
- <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV_ISONEUTRAL NONLIN_EOS SPLIT_EOS SALINITY BULK_FRC T_FRC_BRY Z_FRC_BRY M3_FRC_BRY M2_FRC_BRY SPONGE UV_VIS2 TS_DIF2 LMD_MIXING LMD_KPP LMD_NONLOCAL LMD_RIMIX LMD_CONVEC LMD_BKPP CURVGRID SPHERICAL MASKING MASK_LAND_DATA OBC_M2FLATHER OBC_M3ORLANSKI OBC_TORLANSKI OBC_WEST OBC_NORTH OBC_SOUTH AVERAGES DIAGNOSTICS MARBL MARBL_DIAGS NOX_FORCING NHY_FORCING ALK_SOURCE PCO2AIR_FORCING TIDES POT_TIDES SSH_TIDES UV_TIDES <pre_step3d4S.F> SPLINE_UV SPLINE_TS <step3d_uv1.F> UPSTREAM_UV SPLINE_UV <step3d_uv2.F> DELTA=0.28000000000000003 EPSIL=0.35999999999999999 GAMMA=8.3333333333299994E-002 ALPHA_MAX=2.0 <step3d_t_ISO.F> SPLINE_TS <set_depth.F> NOW=3.63 MID=4.47 BAK=2.05 (N-M+B-1)/B=0.102439024 <lmd_kpp.F> INT_AT_RHO_POINTS SMOOTH_HBL <set_global_definitions.h> CORR_COUPLED_MODE EXTRAP_BAR_FLUXES IMPLCT_NO_SLIP_BTTM_BC VAR_RHO_2D
- surf_forcing_strings :
- bc_options :
- OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, OBC_TORLANSKI,
- git_version :
- type :
- ROMS restart file
- regrid_method :
- bilinear
[13]:
ds_regridded_adjusted_for_ssh = roms_output_adjusted_for_ssh.regrid()
/home/x-kthyng/.conda/envs/romstools-test/lib/python3.13/site-packages/xarray/computation/apply_ufunc.py:312: PerformanceWarning: Regridding is increasing the number of chunks by a factor of 16.0, you might want to specify sizes in `output_chunks` in the regridder call. Default behaviour is to preserve the chunk sizes from the input (50, 50).
result_var = func(*data_vars)
/home/x-kthyng/.conda/envs/romstools-test/lib/python3.13/site-packages/dask/array/routines.py:331: PerformanceWarning: Increasing number of chunks by factor of 11
intermediate = blockwise(
[14]:
ds_regridded_adjusted_for_ssh
[14]:
<xarray.Dataset> Size: 6GB
Dimensions: (time: 4, lat: 185, lon: 197, depth: 100,
eta_v: 161, xi_u: 121, 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: eta_v, xi_u, auxil
Data variables: (12/58)
NH4 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
DOCr (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
zooC (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
diatFe (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
DV_avg2 (time, eta_v, lat, lon) float64 188MB dask.array<chunksize=(1, 161, 185, 197), meta=np.ndarray>
mask_u (xi_u, lat, lon) float64 35MB dask.array<chunksize=(121, 185, 197), meta=np.ndarray>
... ...
salt (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
diatC (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
diatChl (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
NO3 (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
diatP (time, lat, lon, depth) float64 117MB dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
MARBL_PH_SURF_ALT_CO2 (time, lat, lon) float64 1MB dask.array<chunksize=(1, 185, 197), 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: bilinearxarray.Dataset
- time: 4
- lat: 185
- lon: 197
- depth: 100
- eta_v: 161
- xi_u: 121
- auxil: 6
- time(time)datetime64[ns]1998-01-05T23:50:00 ... 1999-02-01
- long_name :
- Time
array(['1998-01-05T23:50:00.000000000', '1998-01-06T00:00:00.000000000', '1999-01-31T23:50:00.000000000', '1999-02-01T00:00:00.000000000'], dtype='datetime64[ns]') - lat(lat)float327.0 7.25 7.5 ... 52.5 52.75 53.0
- long_name :
- Latitude
- units :
- Degrees North
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) - lon(lon)float32208.0 208.2 208.5 ... 256.8 257.0
- long_name :
- Longitude
- units :
- Degrees East
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) - depth(depth)float321.46 4.45 ... 5.281e+03 5.528e+03
- long_name :
- Depth
- units :
- m
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)
- NH4(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Ammonia
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DOCr(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Refractory DOC
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - zooC(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Zooplankton Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diatFe(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diatom Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DV_avg2(time, eta_v, lat, lon)float64dask.array<chunksize=(1, 161, 185, 197), meta=np.ndarray>
- long_name :
- <<fast-time-averaged vbar(:,:,n+1/2)>>
Array Chunk Bytes 179.07 MiB 44.77 MiB Shape (4, 161, 185, 197) (1, 161, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - mask_u(xi_u, lat, lon)float64dask.array<chunksize=(121, 185, 197), meta=np.ndarray>
- long_name :
- Mask at u-points
- units :
- land/water (0/1)
Array Chunk Bytes 33.64 MiB 33.64 MiB Shape (121, 185, 197) (121, 185, 197) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray - mask_v(eta_v, lat, lon)float64dask.array<chunksize=(161, 185, 197), meta=np.ndarray>
- long_name :
- Mask at v-points
- units :
- land/water (0/1)
Array Chunk Bytes 44.77 MiB 44.77 MiB Shape (161, 185, 197) (161, 185, 197) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray - DV_avg_bak(time, eta_v, lat, lon)float64dask.array<chunksize=(1, 161, 185, 197), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged vbar(:,:,n-1)>
Array Chunk Bytes 179.07 MiB 44.77 MiB Shape (4, 161, 185, 197) (1, 161, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - O2(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Oxygen
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - SiO3(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Silicate
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - temp(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- potential temperature
- units :
- Celsius
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - PO4(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Phosphate
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diazFe(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diazotroph Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - Fe(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diatSi(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diatom Silicon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diazC(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diazotroph Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DOP(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Organic Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - u_slow(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- time filtered u, rotated to zonal component
- units :
- m/s
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 89 graph layers Data type float64 numpy.ndarray - spCaCO3(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Small Phyto CaCO3
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - ocean_time(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- Time since 1995/01/01
- units :
- second
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 15 graph layers Data type float64 numpy.ndarray - spP(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Small Phyto Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D_ALT_CO2(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- 3D pH (alternate CO2)
- units :
- pH
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - Lig(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Iron Binding Ligand
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DU_avg2(time, xi_u, lat, lon)float64dask.array<chunksize=(1, 121, 185, 197), meta=np.ndarray>
- long_name :
- <<fast-time averaged ubar(:,:,n+1/2)>>
Array Chunk Bytes 134.58 MiB 33.64 MiB Shape (4, 121, 185, 197) (1, 121, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - v_slow(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- time filtered v, rotated to meridional component
- units :
- m/s
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 89 graph layers Data type float64 numpy.ndarray - ALK(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Alkalinity
- units :
- meq/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - ubar(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- vertically averaged u-momentum component, rotated to zonal component
- units :
- meter second-1
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 50 graph layers Data type float64 numpy.ndarray - u(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- u-momentum component, rotated to zonal component
- units :
- meter second-1
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 89 graph layers Data type float64 numpy.ndarray - spC(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Small Phyto Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diazChl(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diazotroph Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DONr(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Refractory DON
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - time_step(time, auxil, lat, lon)float64dask.array<chunksize=(1, 6, 185, 197), meta=np.ndarray>
- long_name :
- time step and record numbers from initialization
Array Chunk Bytes 6.67 MiB 1.67 MiB Shape (4, 6, 185, 197) (1, 6, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - diazP(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diazotroph Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - spChl(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Small Phyto Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DU_avg_bak(time, xi_u, lat, lon)float64dask.array<chunksize=(1, 121, 185, 197), meta=np.ndarray>
- long_name :
- <back-step mixed fast-time-averaged ubar(:,:,n-1)>
Array Chunk Bytes 134.58 MiB 33.64 MiB Shape (4, 121, 185, 197) (1, 121, 185, 197) Dask graph 4 chunks in 18 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers
- units :
- pH
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - DOPr(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Refractory DOP
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - ALK_ALT_CO2(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Alkalinity, Alternative CO2
- units :
- meq/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - MARBL_ABIO_PH_SURF(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- surface pH for abiotic tracers
- units :
- pH
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - hbls(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- Thickness of KPP surface boundary layer
- units :
- meter
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - DIC(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DIC_ALT_CO2(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Carbon, Alternative CO2
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DOC(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Organic Carbon
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - DON(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Dissolved Organic Nitrogen
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - zeta(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- free-surface elevation
- units :
- meter
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - v(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- v-momentum component, rotated to meridional component
- units :
- meter second-1
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 89 graph layers Data type float64 numpy.ndarray - p_slow(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- time filtered pressure
- units :
- Pa??
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - spFe(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Small Phyto Iron
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - MARBL_PH_3D(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- 3D pH
- units :
- pH
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - mask_rho(lat, lon)float64dask.array<chunksize=(185, 197), meta=np.ndarray>
- Long_name :
- mask at rho-points
- units :
- land/water (0/1)
- Notes :
- Mask has been modified to match the parent grid Mask at the boundaries
Array Chunk Bytes 284.73 kiB 284.73 kiB Shape (185, 197) (185, 197) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray - vbar(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- Long_name :
- Angle between xi axis and east
- long_name :
- vertically averaged v-momentum component, rotated to meridional component
- units :
- meter second-1
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 50 graph layers Data type float64 numpy.ndarray - hbbl(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- Thickness of KPP bottom boundary layer
- units :
- meter
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray - salt(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- salinity
- units :
- PSU
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diatC(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- units :
- mmol/m^3
- long_name :
- Diatom Carbon
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diatChl(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diatom Chlorophyll
- units :
- mg/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - NO3(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Dissolved Inorganic Nitrate
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - diatP(time, lat, lon, depth)float64dask.array<chunksize=(1, 50, 50, 100), meta=np.ndarray>
- long_name :
- Diatom Phosphorus
- units :
- mmol/m^3
Array Chunk Bytes 111.22 MiB 1.91 MiB Shape (4, 185, 197, 100) (1, 50, 50, 100) Dask graph 64 chunks in 55 graph layers Data type float64 numpy.ndarray - MARBL_PH_SURF_ALT_CO2(time, lat, lon)float64dask.array<chunksize=(1, 185, 197), meta=np.ndarray>
- long_name :
- surface pH for base biotic tracers (alternate CO2)
- units :
- pH
Array Chunk Bytes 1.11 MiB 284.73 kiB Shape (4, 185, 197) (1, 185, 197) Dask graph 4 chunks in 16 graph layers Data type float64 numpy.ndarray
- title :
- eastpac25km , 25km resolution
- grid_file :
- /pscratch/sd/e/eay/EASTPAC25KM/INPUT_FIXED_TOPO/epac25km_grd.000.nc
- init_file :
- /pscratch/sd/e/eay/EASTPAC25KM_spinup/output/eastpac25km_rst.19980101000000.000.nc
- ntimes :
- 4610
- ndtfast :
- 45
- dt :
- 600.0
- dtfast :
- 13.333333333333334
- theta_s :
- 5.0
- theta_b :
- 2.0
- hc :
- 300.0
- Cs_w :
- [-1.00000000e+00 -9.83735238e-01 -9.66697847e-01 -9.48934833e-01 -9.30497932e-01 -9.11442824e-01 -8.91828326e-01 -8.71715602e-01 -8.51167398e-01 -8.30247303e-01 -8.09019067e-01 -7.87545970e-01 -7.65890248e-01 -7.44112585e-01 -7.22271672e-01 -7.00423829e-01 -6.78622689e-01 -6.56918954e-01 -6.35360192e-01 -6.13990705e-01 -5.92851439e-01 -5.71979937e-01 -5.51410339e-01 -5.31173415e-01 -5.11296624e-01 -4.91804203e-01 -4.72717281e-01 -4.54054004e-01 -4.35829676e-01 -4.18056911e-01 -4.00745794e-01 -3.83904042e-01 -3.67537172e-01 -3.51648664e-01 -3.36240125e-01 -3.21311453e-01 -3.06860989e-01 -2.92885669e-01 -2.79381169e-01 -2.66342043e-01 -2.53761851e-01 -2.41633283e-01 -2.29948277e-01 -2.18698121e-01 -2.07873557e-01 -1.97464874e-01 -1.87461989e-01 -1.77854528e-01 -1.68631899e-01 -1.59783353e-01 -1.51298043e-01 -1.43165082e-01 -1.35373585e-01 -1.27912713e-01 -1.20771713e-01 -1.13939947e-01 -1.07406924e-01 -1.01162327e-01 -9.51960296e-02 -8.94981213e-02 -8.40589181e-02 -7.88689787e-02 -7.39191144e-02 -6.92003983e-02 -6.47041721e-02 -6.04220511e-02 -5.63459281e-02 -5.24679753e-02 -4.87806460e-02 -4.52766740e-02 -4.19490733e-02 -3.87911360e-02 -3.57964303e-02 -3.29587975e-02 -3.02723487e-02 -2.77314609e-02 -2.53307733e-02 -2.30651826e-02 -2.09298389e-02 -1.89201410e-02 -1.70317316e-02 -1.52604930e-02 -1.36025420e-02 -1.20542257e-02 -1.06121170e-02 -9.27300977e-03 -8.03391530e-03 -6.89205773e-03 -5.84487036e-03 -4.88999201e-03 -4.02526351e-03 -3.24872452e-03 -2.55861056e-03 -1.95335031e-03 -1.43156315e-03 -9.92056980e-04 -6.33826341e-04 -3.56050802e-04 -1.58093625e-04 -3.95007397e-05 0.00000000e+00]
- Cs_r :
- [-9.91966929e-01 -9.75310303e-01 -9.57903911e-01 -9.39797221e-01 -9.21044043e-01 -9.01701722e-01 -8.81830349e-01 -8.61491972e-01 -8.40749848e-01 -8.19667729e-01 -7.98309206e-01 -7.76737103e-01 -7.55012940e-01 -7.33196455e-01 -7.11345198e-01 -6.89514181e-01 -6.67755598e-01 -6.46118605e-01 -6.24649151e-01 -6.03389868e-01 -5.82380004e-01 -5.61655398e-01 -5.41248500e-01 -5.21188414e-01 -5.01500975e-01 -4.82208852e-01 -4.63331666e-01 -4.44886122e-01 -4.26886160e-01 -4.09343110e-01 -3.92265853e-01 -3.75660984e-01 -3.59532980e-01 -3.43884367e-01 -3.28715875e-01 -3.14026605e-01 -2.99814178e-01 -2.86074882e-01 -2.72803815e-01 -2.59995018e-01 -2.47641602e-01 -2.35735865e-01 -2.24269409e-01 -2.13233237e-01 -2.02617853e-01 -1.92413349e-01 -1.82609488e-01 -1.73195779e-01 -1.64161542e-01 -1.55495973e-01 -1.47188200e-01 -1.39227330e-01 -1.31602496e-01 -1.24302897e-01 -1.17317835e-01 -1.10636741e-01 -1.04249210e-01 -9.81450152e-02 -9.23141376e-02 -8.67467775e-02 -8.14333708e-02 -7.63646013e-02 -7.15314104e-02 -6.69250045e-02 -6.25368617e-02 -5.83587357e-02 -5.43826587e-02 -5.06009434e-02 -4.70061835e-02 -4.35912530e-02 -4.03493052e-02 -3.72737705e-02 -3.43583542e-02 -3.15970327e-02 -2.89840508e-02 -2.65139170e-02 -2.41813998e-02 -2.19815232e-02 -1.99095622e-02 -1.79610382e-02 -1.61317141e-02 -1.44175902e-02 -1.28148990e-02 -1.13201009e-02 -9.92988001e-03 -8.64113938e-03 -7.45099718e-03 -6.35678262e-03 -5.35603221e-03 -4.44648617e-03 -3.62608516e-03 -2.89296712e-03 -2.24546446e-03 -1.68210150e-03 -1.20159214e-03 -8.02837815e-04 -4.84925772e-04 -2.47127569e-04 -8.88979122e-05 -9.87376857e-06]
- rho0 :
- 1027.4
- rho0_units :
- kg/m^3
- visc2 :
- 0.0
- visc2_units :
- m^2/s
- gamma2 :
- 1.0
- tnu2 :
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- tnu2_units :
- m^2/s
- ubind :
- 0.2
- ubind_units :
- m/s
- v_sponge :
- 2500.0
- v_sponge_units :
- m^2/s
- rdrg :
- 0.0
- rdrg_units :
- m/s
- rdrg2 :
- 0.0
- rdrg2_units :
- nondimensional
- Zob :
- 0.02
- Zob_units :
- m
- SRCS :
- SRCS $(shell ls *$(UPF77_ext)) SRCS : $(filter-out $(EXCL), $(SRCS)) SRCS : $(SRCS) $(INCL)
- CPPS :
- <cppdefs.opt> PACIFIC_PD SOLVE3D UV_ADV UV_COR ADV_ISONEUTRAL NONLIN_EOS SPLIT_EOS SALINITY BULK_FRC T_FRC_BRY Z_FRC_BRY M3_FRC_BRY M2_FRC_BRY SPONGE UV_VIS2 TS_DIF2 LMD_MIXING LMD_KPP LMD_NONLOCAL LMD_RIMIX LMD_CONVEC LMD_BKPP CURVGRID SPHERICAL MASKING MASK_LAND_DATA OBC_M2FLATHER OBC_M3ORLANSKI OBC_TORLANSKI OBC_WEST OBC_NORTH OBC_SOUTH AVERAGES DIAGNOSTICS MARBL MARBL_DIAGS NOX_FORCING NHY_FORCING ALK_SOURCE PCO2AIR_FORCING TIDES POT_TIDES SSH_TIDES UV_TIDES <pre_step3d4S.F> SPLINE_UV SPLINE_TS <step3d_uv1.F> UPSTREAM_UV SPLINE_UV <step3d_uv2.F> DELTA=0.28000000000000003 EPSIL=0.35999999999999999 GAMMA=8.3333333333299994E-002 ALPHA_MAX=2.0 <step3d_t_ISO.F> SPLINE_TS <set_depth.F> NOW=3.63 MID=4.47 BAK=2.05 (N-M+B-1)/B=0.102439024 <lmd_kpp.F> INT_AT_RHO_POINTS SMOOTH_HBL <set_global_definitions.h> CORR_COUPLED_MODE EXTRAP_BAR_FLUXES IMPLCT_NO_SLIP_BTTM_BC VAR_RHO_2D
- surf_forcing_strings :
- bc_options :
- OBC_WEST, OBC_NORTH, OBC_SOUTH, OBC_M3ORLANSKI, OBC_TORLANSKI,
- git_version :
- type :
- ROMS restart file
- regrid_method :
- bilinear
[15]:
import matplotlib.pyplot as plt
[16]:
indexers = {"time": 0, "depth": 0}
cbar_kwargs = {"label": r"meq/m$^3$"}
fig, axs = plt.subplots(1, 3, figsize=(17, 4))
ds_regridded["ALK"].isel(**indexers).plot(ax=axs[0], cbar_kwargs=cbar_kwargs)
axs[0].set_title("no adjustment for SSH")
ds_regridded_adjusted_for_ssh["ALK"].isel(**indexers).plot(
ax=axs[1], cbar_kwargs=cbar_kwargs
)
axs[1].set_title("with adjustment for SSH")
(ds_regridded["ALK"] - ds_regridded_adjusted_for_ssh["ALK"]).isel(**indexers).plot(
ax=axs[2], cbar_kwargs=cbar_kwargs
)
axs[2].set_title("Difference")
fig.suptitle("Surface Alkalinity", y=1.05)
[16]:
Text(0.5, 1.05, 'Surface Alkalinity')
The plot above shows a difference between adjusting for sea surface height (SSH) and not, albeit a small one.
Computing the mixed layer depth#
There is a public-facing function available for calculating the mixed layer depth for post-processing. Here we demo the use.
[21]:
from roms_tools.setup.utils import compute_potential_density, compute_mld
sigma0 = compute_potential_density(roms_output.ds["temp"], roms_output.ds["salt"])
mld = compute_mld(sigma0, roms_output.ds_depth_coords["layer_depth_rho"], "s_rho")
mld.isel(time=0).plot(x="lon_rho", y="lat_rho")
[21]:
<matplotlib.collections.QuadMesh at 0x1517c6716210>
