import xarray as xr
import xroms
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cartopy

How to select data

The input/output notebook demonstrates how to load in data, but now how to select and slice it apart? Much of this is accomplished with the sel and isel methods in xarray, which are demonstrated in detail in this notebook.

Use sel to select/slice a Dataset or DataArray by dimension values; the best example of this for ROMS output is selecting certain time using a string representation of a datetime.

ds.salt.sel(ocean_time='2010-1-1 12:00')
ds.salt.sel(ocean_time=slice('2010-1-1', '2010-2-1'))

Use isel to subdivide a Dataset or DataArray by dimension indices:

ds.salt.isel(eta_rho=20, xi_rho=100)
ds.salt.isel(eta_rho=slice(20,100,10), xi_rho=slice(None,None,5))

cf-xarray

xroms includes the cf-xarray accessor, which allows you to use xarray sel and isel commands for a DataArray without needing to input the exact grid – just the axes.

With xarray alone:

ds.salt.isel(xi_rho=20, eta_rho=10, s_rho=20, ocean_time=10)

With cf-xarray accessor:

ds.salt.cf.isel(X=20, Y=10, Z=20, T=10)

and get the same thing back. Same for sel. The T, Z, Y, X names can be mixed and matched with the actual dimension names. Some of the attribute wrangling in xroms is dedicated to making sure that cf-xarray can always identify dimensions and coordinates for DataArrays.

You can always check what cf-xarray understands about a Dataset or DataArray with

ds.salt.cf.describe()

Load in data

More information at in input/output page

ds = xroms.datasets.fetch_ROMS_example_full_grid()
ds, xgrid = xroms.roms_dataset(ds, include_cell_volume=True)
ds.xroms.set_grid(xgrid)
ds

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.Dataset> Size: 734MB
Dimensions:     (eta_rho: 191, xi_rho: 300, s_rho: 30, s_w: 31, ocean_time: 2,
                 xi_u: 299, eta_v: 190)
Coordinates: (12/21)
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    ...          ...
    z_w_v       (ocean_time, s_w, eta_v, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>
    z_w_psi     (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
    z_rho_u     (ocean_time, s_rho, eta_rho, xi_u) float64 27MB dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>
    z_rho_v     (ocean_time, s_rho, eta_v, xi_rho) float64 27MB dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>
    z_rho_psi   (ocean_time, s_rho, eta_v, xi_u) float64 27MB dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>
Data variables: (12/45)
    angle       (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    hc          float64 8B ...
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    zeta        (ocean_time, eta_rho, xi_rho) float32 458kB dask.array<chunksize=(2, 191, 300), meta=np.ndarray>
    h           (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    Cs_w        (s_w) float64 248B dask.array<chunksize=(31,), meta=np.ndarray>
    ...          ...
    dV_w_u      (ocean_time, s_w, eta_rho, xi_u) float64 28MB dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>
    dV_v        (ocean_time, s_rho, eta_v, xi_rho) float64 27MB dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>
    dV_w_v      (ocean_time, s_w, eta_v, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>
    dV_psi      (ocean_time, s_rho, eta_v, xi_u) float64 27MB dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>
    dV_w_psi    (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
    rho0        int64 8B 1025
Attributes: (12/29)
    file:              ocean_his_0150.nc
    format:            netCDF-3 classic file
    Conventions:       CF-1.4
    type:              ROMS/TOMS history file
    title:             Texas and Louisiana Shelf case (Nesting)
    rst_file:          ocean_rst.nc
    ...                ...
    compiler_command:  /g/software/openmpi/1.4.3/intel/bin/mpif90
    compiler_flags:    -heap-arrays -fp-model precise -assume 2underscores -c...
    tiling:            016x032
    history:           ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 -  7...
    ana_file:          /scratch/zhangxq/projects/txla_nesting6/Functionals/an...
    CPP_options:       TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUX...

xarray.Dataset

• Dimensions:
  • eta_rho: 191
  • xi_rho: 300
  • s_rho: 30
  • s_w: 31
  • ocean_time: 2
  • xi_u: 299
  • eta_v: 190
• Coordinates: (21)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • eta_v
    (eta_v)
    int64
    0 1 2 3 4 5 ... 185 186 187 188 189

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • z_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • z_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • z_rho_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • z_rho_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_rho_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

• Data variables: (45)
  • angle
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    angle between XI-axis and EAST

    units :

    radians

    field :

    angle, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    field :

    bath, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Cs_w
    (s_w)
    float64
    dask.array<chunksize=(31,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_w, scalar

    Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    mask on RHO-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    mask on U-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    mask on V-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • u
    (ocean_time, s_rho, eta_rho, xi_u)
    float32
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    u-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    u-velocity, scalar, series

    Array Chunk Bytes 13.07 MiB 13.07 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v
    (ocean_time, s_rho, eta_v, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    v-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    v-velocity, scalar, series

    Array Chunk Bytes 13.05 MiB 13.05 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temp
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    potential temperature

    units :

    Celsius

    time :

    ocean_time

    field :

    temperature, scalar, series

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • pm
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in XI

    units :

    meter-1

    field :

    pm, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pn
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in ETA

    units :

    meter-1

    field :

    pn, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • f
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    Coriolis parameter at RHO-points

    units :

    second-1

    field :

    coriolis, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • spherical
    ()
    int64
    1

    array(1)

  • 3d
    ()
    bool
    True

    array(True)

  • dx
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI

    units :

    meter

    field :

    dx, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dx_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on U grid

    units :

    meter

    field :

    dx_u, scalar

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray

  • dx_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on V grid

    units :

    meter

    field :

    dx_v, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dx_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(190, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on PSI grid

    units :

    meter

    field :

    dx_psi, scalar

    Array Chunk Bytes 443.83 kiB 443.83 kiB Shape (190, 299) (190, 299) Dask graph 1 chunks in 11 graph layers Data type float64 numpy.ndarray

  • dy
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA

    units :

    meter

    field :

    dy, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dy_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on U grid

    units :

    meter

    field :

    dy_u, scalar

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray

  • dy_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on V grid

    units :

    meter

    field :

    dy_v, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dy_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(190, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on PSI grid

    units :

    meter

    field :

    dy_psi, scalar

    Array Chunk Bytes 443.83 kiB 443.83 kiB Shape (190, 299) (190, 299) Dask graph 1 chunks in 11 graph layers Data type float64 numpy.ndarray

  • dz
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid

    time :

    ocean_time

    field :

    dz, scalar, series

    units :

    m

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • dz_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid

    time :

    ocean_time

    field :

    dz_w, scalar, series

    units :

    m

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 38 graph layers Data type float64 numpy.ndarray

  • dz_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on U grid

    time :

    ocean_time

    field :

    dz_u, scalar, series

    units :

    m

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

  • dz_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on U grid

    time :

    ocean_time

    field :

    dz_w_u, scalar, series

    units :

    m

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 41 graph layers Data type float64 numpy.ndarray

  • dz_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on V grid

    time :

    ocean_time

    field :

    dz_v, scalar, series

    units :

    m

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 39 graph layers Data type float64 numpy.ndarray

  • dz_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on V grid

    time :

    ocean_time

    field :

    dz_w_v, scalar, series

    units :

    m

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 43 graph layers Data type float64 numpy.ndarray

  • dz_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on PSI grid

    time :

    ocean_time

    field :

    dz_psi, scalar, series

    units :

    m

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 42 graph layers Data type float64 numpy.ndarray

  • dz_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on PSI grid

    time :

    ocean_time

    field :

    dz_w_psi, scalar, series

    units :

    m

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 46 graph layers Data type float64 numpy.ndarray

  • dA
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    area metric in XI and ETA on RHO grid

    units :

    meter2

    field :

    dA, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray

  • dV
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on RHO/RHO grids

    units :

    meter3

    field :

    dV, scalar

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 44 graph layers Data type float64 numpy.ndarray

  • dV_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on RHO/W grids

    units :

    meter3

    field :

    dV_w, scalar

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 48 graph layers Data type float64 numpy.ndarray

  • dV_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on U/RHO grids

    units :

    meter3

    field :

    dV_u, scalar

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 53 graph layers Data type float64 numpy.ndarray

  • dV_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on U/W grids

    units :

    meter3

    field :

    dV_w_u, scalar

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 57 graph layers Data type float64 numpy.ndarray

  • dV_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on V/RHO grids

    units :

    meter3

    field :

    dV_v, scalar

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 59 graph layers Data type float64 numpy.ndarray

  • dV_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on V/W grids

    units :

    meter3

    field :

    dV_w_v, scalar

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 63 graph layers Data type float64 numpy.ndarray

  • dV_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on PSI/RHO grids

    units :

    meter3

    field :

    dV_psi, scalar

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 68 graph layers Data type float64 numpy.ndarray

  • dV_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on PSI/W grids

    units :

    meter3

    field :

    dV_w_psi, scalar

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 72 graph layers Data type float64 numpy.ndarray

  • rho0
    ()
    int64
    1025

    array(1025)

• Indexes: (7)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

  • eta_v
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           180, 181, 182, 183, 184, 185, 186, 187, 188, 189],
          dtype='int64', name='eta_v', length=190))

• Attributes: (29)

  file :

  ocean_his_0150.nc

  format :

  netCDF-3 classic file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  Texas and Louisiana Shelf case (Nesting)

  rst_file :

  ocean_rst.nc

  his_base :

  ocean_his

  grd_file :

  txla_grd_v4_new.nc

  ini_file :

  ocean_rst.nc

  frc_file_01 :

  txla_blk_narr_2009.nc

  frc_file_02 :

  TXLA_river_4dyes_2011.nc

  frc_file_03 :

  txla_flx_frc_9911.nc

  bry_file :

  txla_bry_2009_new.nc

  clm_file :

  txla_clm_2009_new.nc

  script_file :

  External/ocean_txla.in

  svn_url :

  https://www.myroms.org/svn/src/trunk

  svn_rev :

  exported

  code_dir :

  /scratch/zhangxq/ROMS

  header_dir :

  /scratch/zhangxq/projects/txla_nesting6

  header_file :

  txla.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /g/software/openmpi/1.4.3/intel/bin/mpif90

  compiler_flags :

  -heap-arrays -fp-model precise -assume 2underscores -convert big_endian -ip -O3 -free

  tiling :

  016x032

  history :

  ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 - 7:17:45 AM

  ana_file :

  /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_btflux.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_hmixcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_nudgcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_stflux.h

  CPP_options :

  TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUXES, CURVGRID, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EAST_FSCHAPMAN, EAST_M2FLATHER, EAST_M3NUDGING, EAST_M3RADIATION, EAST_TNUDGING, EAST_TRADIATION, EMINUSP, LONGWAVE, M3CLIMATOLOGY, M3CLM_NUDGING, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, NORTHERN_WALL, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SOUTH_FSCHAPMAN, SOUTH_M2FLATHER, SOUTH_M3NUDGING, SOUTH_M3RADIATION, SOUTH_TNUDGING, SOUTH_TRADIATION, SPLINES, TCLIMATOLOGY, TCLM_NUDGING, THREE_GHOST, T_PASSIVE, TS_MPDATA, TS_DIF2, TS_PSOURCE, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_PSOURCE, UV_VIS2, VAR_RHO_2D, VISC_GRID, WEST_FSCHAPMAN, WEST_M2FLATHER, WEST_M3NUDGING, WEST_M3RADIATION, WEST_TNUDGING, WEST_TRADIATION

ds.salt.cf.describe()

Coordinates:
             CF Axes: * X: ['xi_rho']
                      * Y: ['eta_rho']
                      * Z: ['s_rho']
                      * T: ['ocean_time']

      CF Coordinates:   longitude: ['lon_rho']
                        latitude: ['lat_rho']
                        vertical: ['z_rho']
                      * time: ['ocean_time']

       Cell Measures:   area, volume: n/a

      Standard Names:   depth: ['z_rho']
                        latitude: ['lat_rho']
                        longitude: ['lon_rho']
                      * ocean_s_coordinate_g1: ['s_rho']
                      * time: ['ocean_time']

              Bounds:   n/a

       Grid Mappings:   n/a

/tmp/ipykernel_2936/990108105.py:1: DeprecationWarning: 'obj.cf.describe()' will be removed in a future version. Use instead 'repr(obj.cf)' or 'obj.cf' in a Jupyter environment.
  ds.salt.cf.describe()

Select

Surface layer slice

The surface in ROMS is given by the last index in the vertical dimension. The easiest way to access this is by indexing into s_rho. While normally it is better to access coordinates through keywords to be human-readable, it’s not easy to tell what value of s_rho gives the surface. In this instance, it’s easier to just go by index.

ds.salt.isel(s_rho=-1)

ds.salt.cf.isel(Z=-1)  # with cf-xarray

You can also grab the Z level that is “nearest” to 0, the surface, which will give the same vertical level as the other options:

ds.salt.cf.sel(Z=0, method="nearest")

ds.salt.cf.sel(Z=0, method="nearest")

<xarray.DataArray 'salt' (ocean_time: 2, eta_rho: 191, xi_rho: 300)> Size: 458kB
dask.array<getitem, shape=(2, 191, 300), dtype=float32, chunksize=(2, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    s_rho       float64 8B -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, eta_rho, xi_rho) float64 917kB dask.array<chunksize=(2, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  salinity
    time:       ocean_time
    field:      salinity, scalar, series

xarray.DataArray

'salt'

• ocean_time: 2
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    ()
    float64
    -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array(-0.01666667)

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 895.31 kiB 895.31 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 30 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  long_name :

  salinity

  time :

  ocean_time

  field :

  salinity, scalar, series

x/y index slice

For a curvilinear ROMS grid, selecting by the dimensions xi_rho or eta_rho (or for whichever is the relevant grid) is not very meaningful because they are given by index. Thus the following is possible to get a slice along the index, but it cannot be used to find a slice based on the lon/lat values. For the eta and xi grids, sel is equivalent to isel.

ds.temp.sel(xi_rho=20)

ds.temp.cf.sel(X=20);  # same with cf-xarray accessor

ds.temp.cf.sel(X=20)

<xarray.DataArray 'temp' (ocean_time: 2, s_rho: 30, eta_rho: 191)> Size: 46kB
dask.array<getitem, shape=(2, 30, 191), dtype=float32, chunksize=(2, 30, 191), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho) float64 2kB dask.array<chunksize=(191,), meta=np.ndarray>
    lat_rho     (eta_rho) float64 2kB dask.array<chunksize=(191,), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    xi_rho      int64 8B 20
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho) float64 92kB dask.array<chunksize=(2, 30, 191), meta=np.ndarray>
Attributes:
    long_name:  potential temperature
    units:      Celsius
    time:       ocean_time
    field:      temperature, scalar, series

xarray.DataArray

'temp'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191

• dask.array<chunksize=(2, 30, 191), meta=np.ndarray>

  Array Chunk Bytes 44.77 kiB 44.77 kiB Shape (2, 30, 191) (2, 30, 191) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho)
    float64
    dask.array<chunksize=(191,), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 1.49 kiB 1.49 kiB Shape (191,) (191,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho)
    float64
    dask.array<chunksize=(191,), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 1.49 kiB 1.49 kiB Shape (191,) (191,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    ()
    int64
    20

    axis :

    X

    array(20)

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho)
    float64
    dask.array<chunksize=(2, 30, 191), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 89.53 kiB 89.53 kiB Shape (2, 30, 191) (2, 30, 191) Dask graph 1 chunks in 30 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (4)

  long_name :

  potential temperature

  units :

  Celsius

  time :

  ocean_time

  field :

  temperature, scalar, series

Single time

Find the forecast model output available that is closest to now. Note that the method keyword argument is not necessary if the desired date/time is exactly a model output time. You can daisy-chain together different sel and isel calls.

date = "2009-11-19T13:00"

ds.salt.isel(s_rho=-1).sel(ocean_time=date, method='nearest')

ds.salt.cf.isel(Z=-1).cf.sel(T=date, method='nearest')  # with cf-xarray

date = "2009-11-19T13:00"
ds.salt.cf.sel(Z=0, T=date, method='nearest')

<xarray.DataArray 'salt' (eta_rho: 191, xi_rho: 300)> Size: 229kB
dask.array<getitem, shape=(191, 300), dtype=float32, chunksize=(191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    s_rho       float64 8B -0.01667
    ocean_time  datetime64[ns] 8B 2009-11-19T12:00:00
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
Attributes:
    long_name:  salinity
    time:       ocean_time
    field:      salinity, scalar, series

xarray.DataArray

'salt'

• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(191, 300), meta=np.ndarray>

  Array Chunk Bytes 223.83 kiB 223.83 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    ()
    float64
    -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array(-0.01666667)

  • ocean_time
    ()
    datetime64[ns]
    2009-11-19T12:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array('2009-11-19T12:00:00.000000000', dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 30 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  long_name :

  salinity

  time :

  ocean_time

  field :

  salinity, scalar, series

Range of time

time_range = slice(date, pd.Timestamp(date)+pd.Timedelta('3 hours'))

ds.salt.sel(ocean_time=time_range)

ds.salt.cf.sel(T=time_range)  # cf-xarray

time_range = slice(date, pd.Timestamp(date)+pd.Timedelta('3 hours'))
ds.salt.cf.sel(T=time_range)  # cf-xarray

<xarray.DataArray 'salt' (ocean_time: 1, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 7MB
dask.array<getitem, shape=(1, 30, 191, 300), dtype=float32, chunksize=(1, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 8B 2009-11-19T16:00:00
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 14MB dask.array<chunksize=(1, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  salinity
    time:       ocean_time
    field:      salinity, scalar, series

xarray.DataArray

'salt'

• ocean_time: 1
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(1, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 6.56 MiB 6.56 MiB Shape (1, 30, 191, 300) (1, 30, 191, 300) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T16:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T16:00:00.000000000'], dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (1, 30, 191, 300) (1, 30, 191, 300) Dask graph 1 chunks in 30 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  long_name :

  salinity

  time :

  ocean_time

  field :

  salinity, scalar, series

Select region

Select a boxed region by min/max lon and lat values.

# want model output only within the box defined by these lat/lon values
lon = np.array([-92, -91])
lat = np.array([28, 29])

# this condition defines the region of interest
box = ((lon[0] < ds["salt"].cf["longitude"]) & (ds["salt"].cf["longitude"] < lon[1])
       & (lat[0] < ds["salt"].cf["latitude"]) & (ds["salt"].cf["latitude"] < lat[1])).compute()

Plot the model output in the box at the surface

dss = ds.where(box).salt.cf.isel(Z=-1, T=0)
dss.cf.plot(x='longitude', y='latitude')

<matplotlib.collections.QuadMesh at 0x7f4b442dce80>

If you don’t need the rest of the model output, you can drop it by using drop=True in the where call.

dss = ds.where(box, drop=True).salt.cf.isel(Z=-1, T=0)
dss.cf.plot(x='longitude', y='latitude')

<matplotlib.collections.QuadMesh at 0x7f4b44227670>

Can calculate a metric within the box:

dss.mean().values

array(28.308672, dtype=float32)

Subset model output

Subset Dataset of model output such that subsetted domain is as if the simulation was run on that size grid. That is, the rho grid is 1 larger than the psi grid in each of xi and eta.

ds.xroms.subset(X=slice(20,40), Y=slice(50,100))  # with accessor

xroms.subset(ds, X=slice(20,40), Y=slice(50,100))

ds.xroms.subset(X=slice(20,40), Y=slice(50,100))  # with accessor

<xarray.Dataset> Size: 12MB
Dimensions:     (eta_rho: 50, xi_rho: 20, s_rho: 30, s_w: 31, ocean_time: 2,
                 xi_u: 19, eta_v: 49)
Coordinates: (12/21)
    lon_rho     (eta_rho, xi_rho) float64 8kB dask.array<chunksize=(50, 20), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 8kB dask.array<chunksize=(50, 20), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_rho, xi_u) float64 8kB dask.array<chunksize=(50, 19), meta=np.ndarray>
    ...          ...
    z_w_v       (ocean_time, s_w, eta_v, xi_rho) float64 486kB dask.array<chunksize=(2, 31, 49, 20), meta=np.ndarray>
    z_w_psi     (ocean_time, s_w, eta_v, xi_u) float64 462kB dask.array<chunksize=(2, 31, 49, 19), meta=np.ndarray>
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 480kB dask.array<chunksize=(2, 30, 50, 20), meta=np.ndarray>
    z_rho_u     (ocean_time, s_rho, eta_rho, xi_u) float64 456kB dask.array<chunksize=(2, 30, 50, 19), meta=np.ndarray>
    z_rho_v     (ocean_time, s_rho, eta_v, xi_rho) float64 470kB dask.array<chunksize=(2, 30, 49, 20), meta=np.ndarray>
    z_rho_psi   (ocean_time, s_rho, eta_v, xi_u) float64 447kB dask.array<chunksize=(2, 30, 49, 19), meta=np.ndarray>
Data variables: (12/45)
    angle       (eta_rho, xi_rho) float64 8kB dask.array<chunksize=(50, 20), meta=np.ndarray>
    hc          float64 8B ...
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    zeta        (ocean_time, eta_rho, xi_rho) float32 8kB dask.array<chunksize=(2, 50, 20), meta=np.ndarray>
    h           (eta_rho, xi_rho) float64 8kB dask.array<chunksize=(50, 20), meta=np.ndarray>
    Cs_w        (s_w) float64 248B dask.array<chunksize=(31,), meta=np.ndarray>
    ...          ...
    dV_w_u      (ocean_time, s_w, eta_rho, xi_u) float64 471kB dask.array<chunksize=(2, 31, 50, 19), meta=np.ndarray>
    dV_v        (ocean_time, s_rho, eta_v, xi_rho) float64 470kB dask.array<chunksize=(2, 30, 49, 20), meta=np.ndarray>
    dV_w_v      (ocean_time, s_w, eta_v, xi_rho) float64 486kB dask.array<chunksize=(2, 31, 49, 20), meta=np.ndarray>
    dV_psi      (ocean_time, s_rho, eta_v, xi_u) float64 447kB dask.array<chunksize=(2, 30, 49, 19), meta=np.ndarray>
    dV_w_psi    (ocean_time, s_w, eta_v, xi_u) float64 462kB dask.array<chunksize=(2, 31, 49, 19), meta=np.ndarray>
    rho0        int64 8B 1025
Attributes: (12/29)
    file:              ocean_his_0150.nc
    format:            netCDF-3 classic file
    Conventions:       CF-1.4
    type:              ROMS/TOMS history file
    title:             Texas and Louisiana Shelf case (Nesting)
    rst_file:          ocean_rst.nc
    ...                ...
    compiler_command:  /g/software/openmpi/1.4.3/intel/bin/mpif90
    compiler_flags:    -heap-arrays -fp-model precise -assume 2underscores -c...
    tiling:            016x032
    history:           ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 -  7...
    ana_file:          /scratch/zhangxq/projects/txla_nesting6/Functionals/an...
    CPP_options:       TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUX...

xarray.Dataset

• Dimensions:
  • eta_rho: 50
  • xi_rho: 20
  • s_rho: 30
  • s_w: 31
  • ocean_time: 2
  • xi_u: 19
  • eta_v: 49
• Coordinates: (21)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(50, 19), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 7.42 kiB 7.42 kiB Shape (50, 19) (50, 19) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(50, 19), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 7.42 kiB 7.42 kiB Shape (50, 19) (50, 19) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • lon_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(49, 20), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 7.66 kiB 7.66 kiB Shape (49, 20) (49, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • lat_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(49, 20), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 7.66 kiB 7.66 kiB Shape (49, 20) (49, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • xi_rho
    (xi_rho)
    int64
    20 21 22 23 24 ... 35 36 37 38 39

    axis :

    X

    array([20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
           38, 39])

  • xi_u
    (xi_u)
    int64
    20 21 22 23 24 ... 34 35 36 37 38

    axis :

    X

    array([20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
           38])

  • eta_rho
    (eta_rho)
    int64
    50 51 52 53 54 ... 95 96 97 98 99

    axis :

    Y

    array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
           68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
           86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

  • eta_v
    (eta_v)
    int64
    50 51 52 53 54 ... 94 95 96 97 98

    axis :

    Y

    array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
           68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
           86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 50, 20), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 484.38 kiB 484.38 kiB Shape (2, 31, 50, 20) (2, 31, 50, 20) Dask graph 1 chunks in 31 graph layers Data type float64 numpy.ndarray

  • z_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 50, 19), meta=np.ndarray>

    long_name :

    depth of U-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 460.16 kiB 460.16 kiB Shape (2, 31, 50, 19) (2, 31, 50, 19) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 49, 20), meta=np.ndarray>

    long_name :

    depth of V-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 474.69 kiB 474.69 kiB Shape (2, 31, 49, 20) (2, 31, 49, 20) Dask graph 1 chunks in 36 graph layers Data type float64 numpy.ndarray

  • z_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 49, 19), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 450.95 kiB 450.95 kiB Shape (2, 31, 49, 19) (2, 31, 49, 19) Dask graph 1 chunks in 39 graph layers Data type float64 numpy.ndarray

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 50, 20), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 468.75 kiB 468.75 kiB Shape (2, 30, 50, 20) (2, 30, 50, 20) Dask graph 1 chunks in 31 graph layers Data type float64 numpy.ndarray

  • z_rho_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 50, 19), meta=np.ndarray>

    long_name :

    depth of U-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (2, 30, 50, 19) (2, 30, 50, 19) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_rho_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 49, 20), meta=np.ndarray>

    long_name :

    depth of V-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 459.38 kiB 459.38 kiB Shape (2, 30, 49, 20) (2, 30, 49, 20) Dask graph 1 chunks in 36 graph layers Data type float64 numpy.ndarray

  • z_rho_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 49, 19), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 436.41 kiB 436.41 kiB Shape (2, 30, 49, 19) (2, 30, 49, 19) Dask graph 1 chunks in 39 graph layers Data type float64 numpy.ndarray

• Data variables: (45)
  • angle
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    angle between XI-axis and EAST

    units :

    radians

    field :

    angle, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 50, 20), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (2, 50, 20) (2, 50, 20) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    field :

    bath, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • Cs_w
    (s_w)
    float64
    dask.array<chunksize=(31,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_w, scalar

    Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    mask on RHO-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • mask_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(50, 19), meta=np.ndarray>

    long_name :

    mask on U-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 7.42 kiB 7.42 kiB Shape (50, 19) (50, 19) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • mask_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(49, 20), meta=np.ndarray>

    long_name :

    mask on V-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 7.66 kiB 7.66 kiB Shape (49, 20) (49, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • u
    (ocean_time, s_rho, eta_rho, xi_u)
    float32
    dask.array<chunksize=(2, 30, 50, 19), meta=np.ndarray>

    long_name :

    u-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    u-velocity, scalar, series

    Array Chunk Bytes 222.66 kiB 222.66 kiB Shape (2, 30, 50, 19) (2, 30, 50, 19) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

  • v
    (ocean_time, s_rho, eta_v, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 49, 20), meta=np.ndarray>

    long_name :

    v-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    v-velocity, scalar, series

    Array Chunk Bytes 229.69 kiB 229.69 kiB Shape (2, 30, 49, 20) (2, 30, 49, 20) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

  • temp
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 50, 20), meta=np.ndarray>

    long_name :

    potential temperature

    units :

    Celsius

    time :

    ocean_time

    field :

    temperature, scalar, series

    Array Chunk Bytes 234.38 kiB 234.38 kiB Shape (2, 30, 50, 20) (2, 30, 50, 20) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 50, 20), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 234.38 kiB 234.38 kiB Shape (2, 30, 50, 20) (2, 30, 50, 20) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • pm
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in XI

    units :

    meter-1

    field :

    pm, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • pn
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in ETA

    units :

    meter-1

    field :

    pn, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • f
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    Coriolis parameter at RHO-points

    units :

    second-1

    field :

    coriolis, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

  • spherical
    ()
    int64
    1

    array(1)

  • 3d
    ()
    bool
    True

    array(True)

  • dx
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI

    units :

    meter

    field :

    dx, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • dx_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(50, 19), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on U grid

    units :

    meter

    field :

    dx_u, scalar

    Array Chunk Bytes 7.42 kiB 7.42 kiB Shape (50, 19) (50, 19) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dx_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(49, 20), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on V grid

    units :

    meter

    field :

    dx_v, scalar

    Array Chunk Bytes 7.66 kiB 7.66 kiB Shape (49, 20) (49, 20) Dask graph 1 chunks in 10 graph layers Data type float64 numpy.ndarray

  • dx_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(49, 19), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on PSI grid

    units :

    meter

    field :

    dx_psi, scalar

    Array Chunk Bytes 7.27 kiB 7.27 kiB Shape (49, 19) (49, 19) Dask graph 1 chunks in 13 graph layers Data type float64 numpy.ndarray

  • dy
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA

    units :

    meter

    field :

    dy, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • dy_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(50, 19), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on U grid

    units :

    meter

    field :

    dy_u, scalar

    Array Chunk Bytes 7.42 kiB 7.42 kiB Shape (50, 19) (50, 19) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dy_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(49, 20), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on V grid

    units :

    meter

    field :

    dy_v, scalar

    Array Chunk Bytes 7.66 kiB 7.66 kiB Shape (49, 20) (49, 20) Dask graph 1 chunks in 10 graph layers Data type float64 numpy.ndarray

  • dy_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(49, 19), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on PSI grid

    units :

    meter

    field :

    dy_psi, scalar

    Array Chunk Bytes 7.27 kiB 7.27 kiB Shape (49, 19) (49, 19) Dask graph 1 chunks in 13 graph layers Data type float64 numpy.ndarray

  • dz
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 50, 20), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid

    time :

    ocean_time

    field :

    dz, scalar, series

    units :

    m

    Array Chunk Bytes 468.75 kiB 468.75 kiB Shape (2, 30, 50, 20) (2, 30, 50, 20) Dask graph 1 chunks in 36 graph layers Data type float64 numpy.ndarray

  • dz_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 50, 20), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid

    time :

    ocean_time

    field :

    dz_w, scalar, series

    units :

    m

    Array Chunk Bytes 484.38 kiB 484.38 kiB Shape (2, 31, 50, 20) (2, 31, 50, 20) Dask graph 1 chunks in 40 graph layers Data type float64 numpy.ndarray

  • dz_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 50, 19), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on U grid

    time :

    ocean_time

    field :

    dz_u, scalar, series

    units :

    m

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (2, 30, 50, 19) (2, 30, 50, 19) Dask graph 1 chunks in 39 graph layers Data type float64 numpy.ndarray

  • dz_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 50, 19), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on U grid

    time :

    ocean_time

    field :

    dz_w_u, scalar, series

    units :

    m

    Array Chunk Bytes 460.16 kiB 460.16 kiB Shape (2, 31, 50, 19) (2, 31, 50, 19) Dask graph 1 chunks in 43 graph layers Data type float64 numpy.ndarray

  • dz_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 49, 20), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on V grid

    time :

    ocean_time

    field :

    dz_v, scalar, series

    units :

    m

    Array Chunk Bytes 459.38 kiB 459.38 kiB Shape (2, 30, 49, 20) (2, 30, 49, 20) Dask graph 1 chunks in 41 graph layers Data type float64 numpy.ndarray

  • dz_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 49, 20), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on V grid

    time :

    ocean_time

    field :

    dz_w_v, scalar, series

    units :

    m

    Array Chunk Bytes 474.69 kiB 474.69 kiB Shape (2, 31, 49, 20) (2, 31, 49, 20) Dask graph 1 chunks in 45 graph layers Data type float64 numpy.ndarray

  • dz_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 49, 19), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical RHO grid on PSI grid

    time :

    ocean_time

    field :

    dz_psi, scalar, series

    units :

    m

    Array Chunk Bytes 436.41 kiB 436.41 kiB Shape (2, 30, 49, 19) (2, 30, 49, 19) Dask graph 1 chunks in 44 graph layers Data type float64 numpy.ndarray

  • dz_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 49, 19), meta=np.ndarray>

    long_name :

    vertical layer thickness on vertical W grid on PSI grid

    time :

    ocean_time

    field :

    dz_w_psi, scalar, series

    units :

    m

    Array Chunk Bytes 450.95 kiB 450.95 kiB Shape (2, 31, 49, 19) (2, 31, 49, 19) Dask graph 1 chunks in 48 graph layers Data type float64 numpy.ndarray

  • dA
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(50, 20), meta=np.ndarray>

    long_name :

    area metric in XI and ETA on RHO grid

    units :

    meter2

    field :

    dA, scalar

    Array Chunk Bytes 7.81 kiB 7.81 kiB Shape (50, 20) (50, 20) Dask graph 1 chunks in 9 graph layers Data type float64 numpy.ndarray

  • dV
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 50, 20), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on RHO/RHO grids

    units :

    meter3

    field :

    dV, scalar

    Array Chunk Bytes 468.75 kiB 468.75 kiB Shape (2, 30, 50, 20) (2, 30, 50, 20) Dask graph 1 chunks in 46 graph layers Data type float64 numpy.ndarray

  • dV_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 50, 20), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on RHO/W grids

    units :

    meter3

    field :

    dV_w, scalar

    Array Chunk Bytes 484.38 kiB 484.38 kiB Shape (2, 31, 50, 20) (2, 31, 50, 20) Dask graph 1 chunks in 50 graph layers Data type float64 numpy.ndarray

  • dV_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 50, 19), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on U/RHO grids

    units :

    meter3

    field :

    dV_u, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (2, 30, 50, 19) (2, 30, 50, 19) Dask graph 1 chunks in 55 graph layers Data type float64 numpy.ndarray

  • dV_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 50, 19), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on U/W grids

    units :

    meter3

    field :

    dV_w_u, scalar

    Array Chunk Bytes 460.16 kiB 460.16 kiB Shape (2, 31, 50, 19) (2, 31, 50, 19) Dask graph 1 chunks in 59 graph layers Data type float64 numpy.ndarray

  • dV_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 49, 20), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on V/RHO grids

    units :

    meter3

    field :

    dV_v, scalar

    Array Chunk Bytes 459.38 kiB 459.38 kiB Shape (2, 30, 49, 20) (2, 30, 49, 20) Dask graph 1 chunks in 61 graph layers Data type float64 numpy.ndarray

  • dV_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 49, 20), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on V/W grids

    units :

    meter3

    field :

    dV_w_v, scalar

    Array Chunk Bytes 474.69 kiB 474.69 kiB Shape (2, 31, 49, 20) (2, 31, 49, 20) Dask graph 1 chunks in 65 graph layers Data type float64 numpy.ndarray

  • dV_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 49, 19), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on PSI/RHO grids

    units :

    meter3

    field :

    dV_psi, scalar

    Array Chunk Bytes 436.41 kiB 436.41 kiB Shape (2, 30, 49, 19) (2, 30, 49, 19) Dask graph 1 chunks in 70 graph layers Data type float64 numpy.ndarray

  • dV_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 49, 19), meta=np.ndarray>

    long_name :

    volume metric in XI and ETA and S on PSI/W grids

    units :

    meter3

    field :

    dV_w_psi, scalar

    Array Chunk Bytes 450.95 kiB 450.95 kiB Shape (2, 31, 49, 19) (2, 31, 49, 19) Dask graph 1 chunks in 74 graph layers Data type float64 numpy.ndarray

  • rho0
    ()
    int64
    1025

    array(1025)

• Indexes: (7)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
           38, 39],
          dtype='int64', name='xi_rho'))

  • xi_u
    PandasIndex

    PandasIndex(Index([20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
           38],
          dtype='int64', name='xi_u'))

  • eta_rho
    PandasIndex

    PandasIndex(Index([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
           68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
           86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99],
          dtype='int64', name='eta_rho'))

  • eta_v
    PandasIndex

    PandasIndex(Index([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
           68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
           86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98],
          dtype='int64', name='eta_v'))

• Attributes: (29)

  file :

  ocean_his_0150.nc

  format :

  netCDF-3 classic file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  Texas and Louisiana Shelf case (Nesting)

  rst_file :

  ocean_rst.nc

  his_base :

  ocean_his

  grd_file :

  txla_grd_v4_new.nc

  ini_file :

  ocean_rst.nc

  frc_file_01 :

  txla_blk_narr_2009.nc

  frc_file_02 :

  TXLA_river_4dyes_2011.nc

  frc_file_03 :

  txla_flx_frc_9911.nc

  bry_file :

  txla_bry_2009_new.nc

  clm_file :

  txla_clm_2009_new.nc

  script_file :

  External/ocean_txla.in

  svn_url :

  https://www.myroms.org/svn/src/trunk

  svn_rev :

  exported

  code_dir :

  /scratch/zhangxq/ROMS

  header_dir :

  /scratch/zhangxq/projects/txla_nesting6

  header_file :

  txla.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /g/software/openmpi/1.4.3/intel/bin/mpif90

  compiler_flags :

  -heap-arrays -fp-model precise -assume 2underscores -convert big_endian -ip -O3 -free

  tiling :

  016x032

  history :

  ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 - 7:17:45 AM

  ana_file :

  /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_btflux.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_hmixcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_nudgcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_stflux.h

  CPP_options :

  TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUXES, CURVGRID, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EAST_FSCHAPMAN, EAST_M2FLATHER, EAST_M3NUDGING, EAST_M3RADIATION, EAST_TNUDGING, EAST_TRADIATION, EMINUSP, LONGWAVE, M3CLIMATOLOGY, M3CLM_NUDGING, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, NORTHERN_WALL, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SOUTH_FSCHAPMAN, SOUTH_M2FLATHER, SOUTH_M3NUDGING, SOUTH_M3RADIATION, SOUTH_TNUDGING, SOUTH_TRADIATION, SPLINES, TCLIMATOLOGY, TCLM_NUDGING, THREE_GHOST, T_PASSIVE, TS_MPDATA, TS_DIF2, TS_PSOURCE, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_PSOURCE, UV_VIS2, VAR_RHO_2D, VISC_GRID, WEST_FSCHAPMAN, WEST_M2FLATHER, WEST_M3NUDGING, WEST_M3RADIATION, WEST_TNUDGING, WEST_TRADIATION

Find nearest in lon/lat

This matters for a curvilinear grid.

Can’t use sel because it will only search in one dimension for the nearest value and the dimensions are indices which are not necessarily geographic distance. Instead need to use a search for distance and use that for the where condition from the previous example. This functionality has been wrapped into xroms.sel2d (and its partner function xroms.argsel2d).

lon0, lat0 = -91, 28
saltsel = ds.salt.xroms.sel2d(lon0, lat0)

Or, if you instead want the indices of the nearest grid node returned, you can call argsel2d:

ds.salt.xroms.argsel2d(lon0, lat0)

(16, 110)

Check this function, just to be sure:

dl = 0.05
box = (ds.lon_rho>lon0-dl) & (ds.lon_rho<lon0+dl) & (ds.lat_rho>lat0-dl) & (ds.lat_rho<lat0+dl)
dss = ds.where(box).salt.cf.isel(T=0, Z=-1)

vmin = dss.min().values
vmax = dss.max().values

dss.plot(x='lon_rho', y='lat_rho')
plt.scatter(lon0, lat0, c=saltsel.cf.isel(Z=-1, T=0), s=200, edgecolor='k', vmin=vmin, vmax=vmax)
plt.xlim(lon0-dl,lon0+dl)
plt.ylim(lat0-dl, lat0+dl)

(27.95, 28.05)