Demo Workflow

model_catalogs provides easy access to specific curated ocean models through Intake catalogs, with the ultimate product being an xarray Dataset.

An Intake catalog has one or more Sources listed in it; a Source is a distinct product that has a urlpath pointing to where the model output can be accessed. An Intake catalog can be nested catalogs that each have sources.

import model_catalogs as mc

import intake
import pandas as pd

import os

import xarray as xr

Main Catalog

The first step is to set up the main catalog. The main catalog provides access to all of the models and the various model_sources that are available for each model. The catalog is set up by reading in the transform.yaml catalog file, pointing it to the hard-wired original (“orig”) catalog file for each model, and reading and copying in previously-calculated boundary information for the model (also saved in the repository). Every model catalog in the main catalog has at least a single model_source, but may also have others. Each model_source model catalog has a urlpath that points to a location for the model output so that a user can look at some model output. If the model output is available at a static link, that link is provided by urlpath. If the model output is available as a series of files that need to be aggregated (often for NOAA OFS models), then two example files are provided. Later in the process, the urlpath for all unaggregated models will be filled in with the full listing of file locations that are requested by the user.

The main catalog is compiled from parts that are saved to the repository, but some of the model catalog files have Intake user parameters for today and/or yesterday that are filled in when the main catalog is run. To save time, the individual compiled catalog files are saved into a user application cache and are considered “fresh” for 6 hours (set in mc.FRESH['compiled'] in the __init__ file) since they need to be recreated for each day. When mc.setup() is run, the catalog will be put together fresh if either more than 6 hours have passed since last time it was run or override=True is input to force it to be recreated. Otherwise, the previously-written “compiled” catalog files will be reread.

Each model catalog nested in the main catalog points to an original model catalog as a “target” for an Intake transform in model_catalogs. This transform is run when model output is requested from the model with source.to_dask() and it applies metadata stored in the model catalogs. The original model information can also be revealed with source.target.

Sample main catalog

main_cat Nested Intake Catalog	cat Intake Catalog	source Intake Source	Description
	CIOFS
		coops-forecast-agg	static link to pre-aggregated forecast model output
		coops-forecast-noagg	unaggregated model output from recently through forecast
		ncei-archive-noagg	unaggregated nowcast model output from the past through not too long ago
		ncei-archive-agg	aggregated forecast files from the past
	WCOFS
		coops-forecast-noagg	unaggregated model output from recently through forecast
		ncei-archive-noagg	unaggregated nowcast model output from the past through not too long ago
	GOFS
		hycom-forecast-agg	static link to forecast model output
	RTOFS
		nomads-forecast-yesterday	known links to files, prepopulated with “yesterday” to get correct links
		nomads-forecast-today	known links to files, prepopulated with “today” to get correct links. These files are only available midday so aren’t as reliable to use as the forecast depending on the time of time.

…all the rest are similar

NOTE: The model domain boundaries are previously calculated and saved into the repository. However, they can be recalculated with mc.calculate_boundaries() if a model has been changed.

NOTE: The user application cache directory can be revealed with mc.CAT_PATH.

main_cat = mc.setup()

# Or if you have changed a model catalog file, you can incorporate those changes into main_cat with:
# main_cat = mc.setup(override=True)

See what models are available with:

list(main_cat)

['NYOFS',
 'CBOFS-RGRID',
 'DBOFS-RGRID',
 'WCOFS-RGRID',
 'LEOFS-RGRID',
 'RTOFS-EAST',
 'RTOFS-AK',
 'RTOFS-2D',
 'WCOFS-2DS',
 'GOFS',
 'GFS-1-2DEG',
 'SFBOFS-RGRID',
 'RTOFS',
 'CREOFS-RGRID',
 'TBOFS',
 'NGOFS2',
 'GFS-1DEG',
 'NGOFS2-RGRID',
 'GOMOFS-2DS',
 'CIOFS',
 'LMHOFS-RGRID',
 'GOMOFS-RGRID',
 'LMHOFS',
 'CIOFS-RGRID',
 'DBOFS',
 'LEOFS',
 'SFBOFS',
 'TBOFS-RGRID',
 'NGOFS2-2DS',
 'RTOFS-WEST',
 'LOOFS',
 'WCOFS',
 'GOMOFS',
 'GFS-1-4DEG',
 'CREOFS',
 'LSOFS',
 'CBOFS']

Examine metadata of models in source catalog

The main catalog contains information about each model. The full list of metadata keys for a model catalog can be found with:

main_cat['CBOFS'].metadata.keys()

dict_keys(['alpha_shape', 'bounding_box', 'filetype', 'geospatial_bounds', 'grid_dim_(xyz)', 'grid_res_max_(km)', 'grid_res_min_(km)', 'grid_type', 'html_desc', 'long_name', 'catalog_dir'])

Each source also has its own metadata:

main_cat['CBOFS']['coops-forecast-agg'].metadata.keys()

dict_keys(['axis', 'catalog_dir', 'output_period_(hr)', 'overall_end_datetime', 'overall_start_datetime', 'standard_names'])

Domain boundaries

import shapely.wkt

P = shapely.wkt.loads(main_cat['CBOFS'].metadata['geospatial_bounds'])
P

Variables

A mapping for the relevant variables to NOAA applications has been written into each source catalog. The mapping is between a standard CF convention variable name to the model dataset variable name. An example list of possible variables used is:

    eastward_sea_water_velocity
    eastward_wind
    northward_sea_water_velocity
    northward_wind
    sea_ice_area_fraction
    sea_ice_thickness
    sea_surface_elevation
    sea_water_temperature
    sea_water_practical_salinity

The full standard_name list is available in the docs.

Examine the variable mapping for a given model:

main_cat['CBOFS']['coops-forecast-agg'].metadata['standard_names']

{'angle': 'angle_of_rotation_from_east_to_x',
 'eastward_sea_water_velocity': 'u',
 'eastward_wind': 'Uwind',
 'northward_sea_water_velocity': 'v',
 'northward_wind': 'Vwind',
 'ocean_s_coordinate_g1': ['s_rho', 's_w'],
 'sea_floor_depth': 'h',
 'sea_surface_elevation': 'zeta',
 'sea_water_practical_salinity': 'salt',
 'sea_water_temperature': 'temp',
 'time': 'time',
 'upward_sea_water_velocity': 'w'}

Conveniences: urlpath, model output source, and server check

You can find the model output location(s) by querying urlpath on a source. Every source has a default urlpath that can be checked.

main_cat['CBOFS']['coops-forecast-noagg'].urlpath  # shows 2 default files

['https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CBOFS/MODELS/2022/10/04/nos.cbofs.fields.n001.20221004.t00z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CBOFS/MODELS/2022/10/04/nos.cbofs.fields.n002.20221004.t00z.nc']

The datetimes associated with files to be aggregated together from models that have a list of paths in urlpath (instead of just a static link) can be examined separately for convenience in the source:

main_cat['CBOFS']['coops-forecast-noagg'].dates

[Timestamp('2022-10-03 19:00:00'), Timestamp('2022-10-03 20:00:00')]

The sources in model_catalogs are Transforms pointing to other Intake catalog files that are the target of the transforms. The sources are what need to have urlpath defined. However, model_catalogs brings forward the urlpath so you can query it from the transform too. The original model information, the target of the transform, can be found with source.target. For example:

main_cat['CBOFS']['coops-forecast-noagg'].target

coops-forecast-noagg:
  args:
    combine: by_coords
    compat: override
    coords: minimal
    data_vars: minimal
    engine: netcdf4
    parallel: true
    urlpath:
    - https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CBOFS/MODELS/2022/10/04/nos.cbofs.fields.n001.20221004.t00z.nc
    - https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CBOFS/MODELS/2022/10/04/nos.cbofs.fields.n002.20221004.t00z.nc
  description: Unaggregated 3D Chesapeake Bay model in ROMS from 30 days ago with
    48 hour forecast.
  driver: intake_xarray.opendap.OpenDapSource
  metadata:
    axis:
      T: ocean_time
      X:
      - xi_rho
      - xi_u
      - xi_v
      - xi_psi
      Y:
      - eta_rho
      - eta_u
      - eta_v
      - eta_psi
      Z:
      - s_rho
      - s_w
    catalog_dir: /Users/kthyng/projects/model_catalogs/model_catalogs/catalogs/orig/
    catloc: https://opendap.co-ops.nos.noaa.gov/thredds/catalog/NOAA/CBOFS/MODELS/catalog.xml
    output_period_(hr): 1
    overall_end_datetime: 48 hours after present time
    overall_start_datetime: 1 month before present day
    sample_locs:
    - https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CBOFS/MODELS/2022/10/04/nos.cbofs.fields.n001.20221004.t00z.nc
    - https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CBOFS/MODELS/2022/10/04/nos.cbofs.fields.n002.20221004.t00z.nc
    standard_names:
      angle: angle_of_rotation_from_east_to_x
      eastward_sea_water_velocity: u
      eastward_wind: Uwind
      northward_sea_water_velocity: v
      northward_wind: Vwind
      ocean_s_coordinate_g1:
      - s_rho
      - s_w
      sea_floor_depth: h
      sea_surface_elevation: zeta
      sea_water_practical_salinity: salt
      sea_water_temperature: temp
      time: ocean_time
      upward_sea_water_velocity: w

You can also see if the server for a given Source is currently running with

main_cat['CBOFS']['coops-forecast-noagg'].status

True

Find availability for a model

You can query a specific model for its availability for any or all model_sources. This is not a necessary step, but is useful for a user choosing the model to use and what is possible. The start and end datetimes found are saved into the user application cache and are reused if still considered fresh (as determined by the FRESH parameters for each model_source and start or end times).

• If no model_sources are input, the availability will be found and inserted into each source in the input model catalog: mc.find_availability(main_cat['CIOFS']); this returns an Intake Catalog

• Alternatively, the user can choose 1 or more model_sources to check, for example, maybe they are only interested in checking the available datetime range of a particular model_source: mc.find_availability(main_cat['CIOFS'], model_source='coops-forecast-agg'); this returns an Intake Catalog

• Or, simplest of all, the user can directly input the Intake Source they want to evaluate: mc.find_availability(main_cat['CIOFS']['coops-forecast-agg']); this returns an Intake Source

In the following example, some model_sources for the CIOFS model may still be fresh for the first call below. The second time the request is made, all previously-calculated availabilities are fresh so it is fast to run.

%%time
cat = mc.find_availability(main_cat['CIOFS'])

CPU times: user 488 ms, sys: 14.9 ms, total: 503 ms
Wall time: 3.62 s

%%time
cat = mc.find_availability(main_cat['CIOFS'], verbose=True)

coops-forecast-agg: 2022-09-28T01:00:00.000000000 to 2022-10-07T12:00:00.000000000
coops-forecast-noagg: 2022-09-04 13:00:00 to 2022-10-07 12:00:00
ncei-archive-agg: 2019-09-16T19:00:00.000000000 to 2022-10-01T18:00:00.000000000
ncei-archive-noagg: 2019-09-16 19:00:00 to 2022-10-01 18:00:00
CPU times: user 6.28 ms, sys: 1.87 ms, total: 8.15 ms
Wall time: 7.03 ms

Setup model catalog for desired date range

For unaggregated model_sources for NOAA OFS models like “coops-forecast-agg”, the locations of the necessary model files need to be determined and saved to urlpath in the source, based on the date range desired. mc.select_date_range() does this.

There are four modes for mc.select_date_range():

1. Run mc.find_availability() for all model_sources, then request your desired date range in mc.select_date_range() and the code will figure out what model_source to use.

2. Skip straight to mc.select_date_range() because you know which model_source to use for your model, and input that model_source.

3. In the special case of there only being one model_source in the cat, it does not need to be input.

4. In the special case of the model output being available at a static link or deterministic paths, the function of mc.select_date_range() is to input the desired start and end dates into the metadata so that the ultimate Dataset is subselected to that time range.

mc.select_date_range() can take in an Intake Catalog or Source, but always returns a Source.

NOTE: Some model sources have static links to the model output, in which case they can be immediately accessed, like the GOFS forecast. For those models, mc.select_date_range() does not need to be run and the model output can be accessed directly from the main catalog. You don’t need to know this though — you can use the same process described here too.

NOTE: All model sources have some files that work stored in urlpath so that you can always run main_cat[model][model_source].to_dask() and get something back. However, these are sample files and will be replaced with the proper file paths when a user runs mc.select_date_range().

Example of #1. After this is run, model output can be accessed through source_CIOFS.

start_date = pd.Timestamp.today()
main_cat = mc.setup()
cat = mc.find_availability(main_cat['WCOFS'])
source_WCOFS = mc.select_date_range(cat, start_date=start_date, end_date=start_date, override=True)

source_WCOFS.urlpath  # shows files for the date range selection

['https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n003.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n006.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n009.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n012.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n015.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n018.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n021.20221005.t03z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/WCOFS/MODELS/2022/10/05/nos.wcofs.fields.n024.20221005.t03z.nc']

The dates associated with the newly-found filenames in urlpath can be checked with source.dates:

source_WCOFS.dates

[Timestamp('2022-10-05 00:00:00'),
 Timestamp('2022-10-05 03:00:00'),
 Timestamp('2022-10-05 06:00:00'),
 Timestamp('2022-10-05 09:00:00'),
 Timestamp('2022-10-05 12:00:00'),
 Timestamp('2022-10-05 15:00:00'),
 Timestamp('2022-10-05 18:00:00'),
 Timestamp('2022-10-05 21:00:00')]

Example of #2:

today = pd.Timestamp.today()
main_cat = mc.setup()
source_LMHOFS = mc.select_date_range(main_cat['LMHOFS']['coops-forecast-noagg'], start_date=today, end_date=today)

source_LMHOFS.urlpath  # shows files for the date range selection

['https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n000.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n001.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n002.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n003.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n004.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n005.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n006.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n001.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n002.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n003.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n004.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n005.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n006.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n001.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n002.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n003.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n004.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.n005.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.f000.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.f001.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.f002.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.f003.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.f004.20221005.t18z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LMHOFS/MODELS/2022/10/05/nos.lmhofs.fields.f005.20221005.t18z.nc']

Example of #3 and #4:

today = pd.Timestamp.today()
main_cat = mc.setup()
source_GOFS = mc.select_date_range(main_cat['GOFS'], start_date=today, end_date=today+pd.Timedelta('1 day'))

source_GOFS.urlpath

'http://tds.hycom.org/thredds/dodsC/GLBy0.08/latest'

Note that since the GOFS forecast output is available at a static link, it can also be accessed directly. The difference in the resulting Dataset is that source_GOFS_direct retains all times in the output whereas source_GOFS has been filtered to the specified date range.

source_GOFS_direct = main_cat['GOFS']['hycom-forecast-agg'].to_dask()

Direct workflow

If you already know what model and model_source you want to use, you can skip ahead to get your desired date range with:

source = mc.select_date_range(main_cat['CIOFS']['coops-forecast-noagg'], start_date=start_date, end_date=start_date)
source.urlpath

['https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n006.20221005.t00z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n001.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n002.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n003.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n004.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n005.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n006.20221005.t06z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n001.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n002.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n003.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n004.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n005.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.n006.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f001.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f002.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f003.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f004.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f005.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f006.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f007.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f008.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f009.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f010.20221005.t12z.nc',
 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/CIOFS/MODELS/2022/10/05/nos.ciofs.fields.f011.20221005.t12z.nc']

Read in model output

Model output can be accessed from our examples 1 and 2 above, or from an example with a static link.

Example #1:

%%time
source_WCOFS.to_dask()

CPU times: user 3.83 s, sys: 416 ms, total: 4.25 s
Wall time: 7.83 s

<xarray.Dataset>
Dimensions:        (tracer: 2, boundary: 4, s_rho: 40, s_w: 41, eta_rho: 1016,
                    xi_rho: 348, eta_u: 1016, xi_u: 347, eta_v: 1015,
                    xi_v: 348, eta_psi: 1015, xi_psi: 347, ocean_time: 2)
Coordinates: (12/19)
  * s_rho          (s_rho) float64 -0.9875 -0.9625 -0.9375 ... -0.0375 -0.0125
  * s_w            (s_w) float64 -1.0 -0.975 -0.95 -0.925 ... -0.05 -0.025 0.0
    lon_rho        (eta_rho, xi_rho) float64 dask.array<chunksize=(1016, 348), meta=np.ndarray>
    lat_rho        (eta_rho, xi_rho) float64 dask.array<chunksize=(1016, 348), meta=np.ndarray>
    lon_u          (eta_u, xi_u) float64 dask.array<chunksize=(1016, 347), meta=np.ndarray>
    lat_u          (eta_u, xi_u) float64 dask.array<chunksize=(1016, 347), meta=np.ndarray>
    ...             ...
  * xi_v           (xi_v) int64 0 1 2 3 4 5 6 7 ... 341 342 343 344 345 346 347
  * xi_psi         (xi_psi) int64 0 1 2 3 4 5 6 ... 340 341 342 343 344 345 346
  * eta_rho        (eta_rho) int64 0 1 2 3 4 5 ... 1010 1011 1012 1013 1014 1015
  * eta_u          (eta_u) int64 0 1 2 3 4 5 6 ... 1010 1011 1012 1013 1014 1015
  * eta_v          (eta_v) int64 0 1 2 3 4 5 6 ... 1009 1010 1011 1012 1013 1014
  * eta_psi        (eta_psi) int64 0 1 2 3 4 5 ... 1009 1010 1011 1012 1013 1014
Dimensions without coordinates: tracer, boundary
Data variables: (12/80)
    ntimes         int32 ...
    ndtfast        int32 ...
    dt             float64 ...
    dtfast         float64 ...
    dstart         datetime64[ns] ...
    nHIS           int32 ...
    ...             ...
    vbar           (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1015, 348), meta=np.ndarray>
    u              (ocean_time, s_rho, eta_u, xi_u) float32 dask.array<chunksize=(1, 40, 1016, 347), meta=np.ndarray>
    v              (ocean_time, s_rho, eta_v, xi_v) float32 dask.array<chunksize=(1, 40, 1015, 348), meta=np.ndarray>
    w              (ocean_time, s_w, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 41, 1016, 348), meta=np.ndarray>
    temp           (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 40, 1016, 348), meta=np.ndarray>
    salt           (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 40, 1016, 348), meta=np.ndarray>
Attributes: (12/37)
    file:                            nos.wcofs.fields.nowcast.20221005.t03z_0...
    format:                          netCDF-3 64bit offset file
    Conventions:                     CF-1.4, SGRID-0.3
    type:                            ROMS/TOMS history file
    title:                           wcofs nowcast RUN in operational mode
    var_info:                        varinfo.dat
    ...                              ...
    tiling:                          008x060
    history:                         ROMS/TOMS, Version 3.9, Wednesday - Octo...
    ana_file:                        ROMS/Functionals/ana_btflux.h
    CPP_options:                     mode, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ...
    DODS_EXTRA.Unlimited_Dimension:  ocean_time
    EXTRA_DIMENSION.N:               40

xarray.Dataset

• Dimensions:
  • tracer: 2
  • boundary: 4
  • s_rho: 40
  • s_w: 41
  • eta_rho: 1016
  • xi_rho: 348
  • eta_u: 1016
  • xi_u: 347
  • eta_v: 1015
  • xi_v: 348
  • eta_psi: 1015
  • xi_psi: 347
  • ocean_time: 2
• Coordinates: (19)
  • s_rho
    (s_rho)
    float64
    -0.9875 -0.9625 ... -0.0375 -0.0125

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    positive :

    up

    standard_name :

    ocean_s_coordinate_g2

    formula_terms :

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

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
           -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
           -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
           -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
           -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125])

  • s_w
    (s_w)
    float64
    -1.0 -0.975 -0.95 ... -0.025 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    positive :

    up

    standard_name :

    ocean_s_coordinate_g2

    formula_terms :

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

    field :

    s_w, scalar

    axis :

    Z

    array([-1.   , -0.975, -0.95 , -0.925, -0.9  , -0.875, -0.85 , -0.825, -0.8  ,
           -0.775, -0.75 , -0.725, -0.7  , -0.675, -0.65 , -0.625, -0.6  , -0.575,
           -0.55 , -0.525, -0.5  , -0.475, -0.45 , -0.425, -0.4  , -0.375, -0.35 ,
           -0.325, -0.3  , -0.275, -0.25 , -0.225, -0.2  , -0.175, -0.15 , -0.125,
           -0.1  , -0.075, -0.05 , -0.025,  0.   ])

  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    longitude of RHO-points

    units :

    degree_east

    standard_name :

    longitude

    field :

    lon_rho, scalar

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    latitude of RHO-points

    units :

    degree_north

    standard_name :

    latitude

    field :

    lat_rho, scalar

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lon_u
    (eta_u, xi_u)
    float64
    dask.array<chunksize=(1016, 347), meta=np.ndarray>

    long_name :

    longitude of U-points

    units :

    degree_east

    standard_name :

    longitude

    field :

    lon_u, scalar

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1016, 347) (1016, 347) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lat_u
    (eta_u, xi_u)
    float64
    dask.array<chunksize=(1016, 347), meta=np.ndarray>

    long_name :

    latitude of U-points

    units :

    degree_north

    standard_name :

    latitude

    field :

    lat_u, scalar

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1016, 347) (1016, 347) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lon_v
    (eta_v, xi_v)
    float64
    dask.array<chunksize=(1015, 348), meta=np.ndarray>

    long_name :

    longitude of V-points

    units :

    degree_east

    standard_name :

    longitude

    field :

    lon_v, scalar

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1015, 348) (1015, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lat_v
    (eta_v, xi_v)
    float64
    dask.array<chunksize=(1015, 348), meta=np.ndarray>

    long_name :

    latitude of V-points

    units :

    degree_north

    standard_name :

    latitude

    field :

    lat_v, scalar

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1015, 348) (1015, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lon_psi
    (eta_psi, xi_psi)
    float64
    dask.array<chunksize=(1015, 347), meta=np.ndarray>

    long_name :

    longitude of PSI-points

    units :

    degree_east

    standard_name :

    longitude

    field :

    lon_psi, scalar

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1015, 347) (1015, 347) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lat_psi
    (eta_psi, xi_psi)
    float64
    dask.array<chunksize=(1015, 347), meta=np.ndarray>

    long_name :

    latitude of PSI-points

    units :

    degree_north

    standard_name :

    latitude

    field :

    lat_psi, scalar

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1015, 347) (1015, 347) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2022-10-05 2022-10-05T03:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    standard_name :

    time

    axis :

    T

    array(['2022-10-05T00:00:00.000000000', '2022-10-05T03:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 343 344 345 346 347

    axis :

    X

    array([  0,   1,   2, ..., 345, 346, 347])

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 342 343 344 345 346

    axis :

    X

    array([  0,   1,   2, ..., 344, 345, 346])

  • xi_v
    (xi_v)
    int64
    0 1 2 3 4 5 ... 343 344 345 346 347

    axis :

    X

    array([  0,   1,   2, ..., 345, 346, 347])

  • xi_psi
    (xi_psi)
    int64
    0 1 2 3 4 5 ... 342 343 344 345 346

    axis :

    X

    array([  0,   1,   2, ..., 344, 345, 346])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 ... 1012 1013 1014 1015

    axis :

    Y

    array([   0,    1,    2, ..., 1013, 1014, 1015])

  • eta_u
    (eta_u)
    int64
    0 1 2 3 4 ... 1012 1013 1014 1015

    axis :

    Y

    array([   0,    1,    2, ..., 1013, 1014, 1015])

  • eta_v
    (eta_v)
    int64
    0 1 2 3 4 ... 1011 1012 1013 1014

    axis :

    Y

    array([   0,    1,    2, ..., 1012, 1013, 1014])

  • eta_psi
    (eta_psi)
    int64
    0 1 2 3 4 ... 1011 1012 1013 1014

    axis :

    Y

    array([   0,    1,    2, ..., 1012, 1013, 1014])

• Data variables: (80)
  • ntimes
    ()
    int32
    ...

    long_name :

    number of long time-steps

    array(960, dtype=int32)

  • ndtfast
    ()
    int32
    ...

    long_name :

    number of short time-steps

    array(30, dtype=int32)

  • dt
    ()
    float64
    ...

    long_name :

    size of long time-steps

    units :

    second

    array(90.)

  • dtfast
    ()
    float64
    ...

    long_name :

    size of short time-steps

    units :

    second

    array(3.)

  • dstart
    ()
    datetime64[ns]
    ...

    long_name :

    time stamp assigned to model initilization

    array('2022-10-04T03:00:00.000000000', dtype='datetime64[ns]')

  • nHIS
    ()
    int32
    ...

    long_name :

    number of time-steps between history records

    array(120, dtype=int32)

  • ndefHIS
    ()
    int32
    ...

    long_name :

    number of time-steps between the creation of history files

    array(120, dtype=int32)

  • nRST
    ()
    int32
    ...

    long_name :

    number of time-steps between restart records

    cycle :

    only latest two records are maintained

    array(960, dtype=int32)

  • ntsAVG
    ()
    int32
    ...

    long_name :

    starting time-step for accumulation of time-averaged fields

    array(1, dtype=int32)

  • nAVG
    ()
    int32
    ...

    long_name :

    number of time-steps between time-averaged records

    array(960, dtype=int32)

  • ndefAVG
    ()
    int32
    ...

    long_name :

    number of time-steps between the creation of average files

    array(0, dtype=int32)

  • nSTA
    ()
    int32
    ...

    long_name :

    number of time-steps between stations records

    array(4, dtype=int32)

  • Falpha
    ()
    float64
    ...

    long_name :

    Power-law shape barotropic filter parameter

    array(2.)

  • Fbeta
    ()
    float64
    ...

    long_name :

    Power-law shape barotropic filter parameter

    array(4.)

  • Fgamma
    ()
    float64
    ...

    long_name :

    Power-law shape barotropic filter parameter

    array(0.284)

  • nl_tnu2
    (tracer)
    float64
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    nonlinear model Laplacian mixing coefficient for tracers

    units :

    meter2 second-1

    Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • nl_visc2
    ()
    float64
    ...

    long_name :

    nonlinear model Laplacian mixing coefficient for momentum

    units :

    meter2 second-1

    array(15.)

  • LuvSponge
    ()
    int32
    ...

    long_name :

    horizontal viscosity sponge activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(1, dtype=int32)

  • LtracerSponge
    (tracer)
    int32
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    horizontal diffusivity sponge activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    Array Chunk Bytes 8 B 8 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • Akt_bak
    (tracer)
    float64
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    background vertical mixing coefficient for tracers

    units :

    meter2 second-1

    Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • Akv_bak
    ()
    float64
    ...

    long_name :

    background vertical mixing coefficient for momentum

    units :

    meter2 second-1

    array(1.e-05)

  • Akk_bak
    ()
    float64
    ...

    long_name :

    background vertical mixing coefficient for turbulent energy

    units :

    meter2 second-1

    array(5.e-06)

  • Akp_bak
    ()
    float64
    ...

    long_name :

    background vertical mixing coefficient for length scale

    units :

    meter2 second-1

    array(5.e-06)

  • rdrg
    ()
    float64
    ...

    long_name :

    linear drag coefficient

    units :

    meter second-1

    array(0.0003)

  • rdrg2
    ()
    float64
    ...

    long_name :

    quadratic drag coefficient

    array(0.003)

  • Zob
    ()
    float64
    ...

    long_name :

    bottom roughness

    units :

    meter

    array(0.02)

  • Zos
    ()
    float64
    ...

    long_name :

    surface roughness

    units :

    meter

    array(0.02)

  • Znudg
    ()
    float64
    ...

    long_name :

    free-surface nudging/relaxation inverse time scale

    units :

    day-1

    array(0.033333)

  • M2nudg
    ()
    float64
    ...

    long_name :

    2D momentum nudging/relaxation inverse time scale

    units :

    day-1

    array(0.033333)

  • M3nudg
    ()
    float64
    ...

    long_name :

    3D momentum nudging/relaxation inverse time scale

    units :

    day-1

    array(0.033333)

  • Tnudg
    (tracer)
    float64
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    Tracers nudging/relaxation inverse time scale

    units :

    day-1

    Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • FSobc_in
    (boundary)
    float64
    dask.array<chunksize=(4,), meta=np.ndarray>

    long_name :

    free-surface inflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 32 B 32 B Shape (4,) (4,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • FSobc_out
    (boundary)
    float64
    dask.array<chunksize=(4,), meta=np.ndarray>

    long_name :

    free-surface outflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 32 B 32 B Shape (4,) (4,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • M2obc_in
    (boundary)
    float64
    dask.array<chunksize=(4,), meta=np.ndarray>

    long_name :

    2D momentum inflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 32 B 32 B Shape (4,) (4,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • M2obc_out
    (boundary)
    float64
    dask.array<chunksize=(4,), meta=np.ndarray>

    long_name :

    2D momentum outflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 32 B 32 B Shape (4,) (4,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • Tobc_in
    (boundary, tracer)
    float64
    dask.array<chunksize=(4, 2), meta=np.ndarray>

    long_name :

    tracers inflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 64 B 64 B Shape (4, 2) (4, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • Tobc_out
    (boundary, tracer)
    float64
    dask.array<chunksize=(4, 2), meta=np.ndarray>

    long_name :

    tracers outflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 64 B 64 B Shape (4, 2) (4, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • M3obc_in
    (boundary)
    float64
    dask.array<chunksize=(4,), meta=np.ndarray>

    long_name :

    3D momentum inflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 32 B 32 B Shape (4,) (4,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • M3obc_out
    (boundary)
    float64
    dask.array<chunksize=(4,), meta=np.ndarray>

    long_name :

    3D momentum outflow, nudging inverse time scale

    units :

    second-1

    Array Chunk Bytes 32 B 32 B Shape (4,) (4,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • rho0
    ()
    float64
    ...

    long_name :

    mean density used in Boussinesq approximation

    units :

    kilogram meter-3

    array(1025.)

  • gamma2
    ()
    float64
    ...

    long_name :

    slipperiness parameter

    array(1.)

  • LuvSrc
    ()
    int32
    ...

    long_name :

    momentum point sources and sink activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(1, dtype=int32)

  • LwSrc
    ()
    int32
    ...

    long_name :

    mass point sources and sink activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(0, dtype=int32)

  • LtracerSrc
    (tracer)
    int32
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    tracer point sources and sink activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    Array Chunk Bytes 8 B 8 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • LsshCLM
    ()
    int32
    ...

    long_name :

    sea surface height climatology processing switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(0, dtype=int32)

  • Lm2CLM
    ()
    int32
    ...

    long_name :

    2D momentum climatology processing switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(0, dtype=int32)

  • Lm3CLM
    ()
    int32
    ...

    long_name :

    3D momentum climatology processing switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(0, dtype=int32)

  • LtracerCLM
    (tracer)
    int32
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    tracer climatology processing switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    Array Chunk Bytes 8 B 8 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • LnudgeM2CLM
    ()
    int32
    ...

    long_name :

    2D momentum climatology nudging activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(0, dtype=int32)

  • LnudgeM3CLM
    ()
    int32
    ...

    long_name :

    3D momentum climatology nudging activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    array(0, dtype=int32)

  • LnudgeTCLM
    (tracer)
    int32
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    tracer climatology nudging activation switch

    flag_values :

    [0 1]

    flag_meanings :

    .FALSE. .TRUE.

    Array Chunk Bytes 8 B 8 B Shape (2,) (2,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • spherical
    ()
    int32
    ...

    long_name :

    grid type logical switch

    flag_values :

    [0 1]

    flag_meanings :

    Cartesian spherical

    array(1, dtype=int32)

  • xl
    ()
    float64
    ...

    long_name :

    domain length in the XI-direction

    units :

    meter

    array(1386609.631517)

  • el
    ()
    float64
    ...

    long_name :

    domain length in the ETA-direction

    units :

    meter

    array(4051804.716256)

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    array(2, dtype=int32)

  • Vstretching
    ()
    int32
    ...

    long_name :

    vertical terrain-following stretching function

    array(4, dtype=int32)

  • theta_s
    ()
    float64
    ...

    long_name :

    S-coordinate surface control parameter

    array(8.)

  • theta_b
    ()
    float64
    ...

    long_name :

    S-coordinate bottom control parameter

    array(3.)

  • Tcline
    ()
    float64
    ...

    long_name :

    S-coordinate surface/bottom layer width

    units :

    meter

    array(50.)

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    array(50.)

  • grid
    ()
    int32
    ...

    cf_role :

    grid_topology

    topology_dimension :

    2

    node_dimensions :

    xi_psi eta_psi

    face_dimensions :

    xi_rho: xi_psi (padding: both) eta_rho: eta_psi (padding: both)

    edge1_dimensions :

    xi_u: xi_psi eta_u: eta_psi (padding: both)

    edge2_dimensions :

    xi_v: xi_psi (padding: both) eta_v: eta_psi

    node_coordinates :

    lon_psi lat_psi

    face_coordinates :

    lon_rho lat_rho

    edge1_coordinates :

    lon_u lat_u

    edge2_coordinates :

    lon_v lat_v

    vertical_dimensions :

    s_rho: s_w (padding: none)

    array(1, dtype=int32)

  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(40,), 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 320 B 320 B Shape (40,) (40,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • Cs_w
    (s_w)
    float64
    dask.array<chunksize=(41,), 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 328 B 328 B Shape (41,) (41,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    grid :

    grid

    location :

    face

    field :

    bath, scalar

    standard_name :

    sea_floor_depth

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • f
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    Coriolis parameter at RHO-points

    units :

    second-1

    grid :

    grid

    location :

    face

    field :

    coriolis, scalar

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • pm
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in XI

    units :

    meter-1

    grid :

    grid

    location :

    face

    field :

    pm, scalar

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • pn
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in ETA

    units :

    meter-1

    grid :

    grid

    location :

    face

    field :

    pn, scalar

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • angle
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    angle between XI-axis and EAST

    units :

    radians

    grid :

    grid

    location :

    face

    field :

    angle, scalar

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(1016, 348), meta=np.ndarray>

    long_name :

    mask on RHO-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    grid :

    grid

    location :

    face

    Array Chunk Bytes 2.70 MiB 2.70 MiB Shape (1016, 348) (1016, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask_u
    (eta_u, xi_u)
    float64
    dask.array<chunksize=(1016, 347), meta=np.ndarray>

    long_name :

    mask on U-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    grid :

    grid

    location :

    edge1

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1016, 347) (1016, 347) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask_v
    (eta_v, xi_v)
    float64
    dask.array<chunksize=(1015, 348), meta=np.ndarray>

    long_name :

    mask on V-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    grid :

    grid

    location :

    edge2

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1015, 348) (1015, 348) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • mask_psi
    (eta_psi, xi_psi)
    float64
    dask.array<chunksize=(1015, 347), meta=np.ndarray>

    long_name :

    mask on psi-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    grid :

    grid

    location :

    node

    Array Chunk Bytes 2.69 MiB 2.69 MiB Shape (1015, 347) (1015, 347) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(1, 1016, 348), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    grid :

    grid

    location :

    face

    field :

    free-surface, scalar, series

    standard_name :

    sea_surface_elevation

    Array Chunk Bytes 2.70 MiB 1.35 MiB Shape (2, 1016, 348) (1, 1016, 348) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • ubar
    (ocean_time, eta_u, xi_u)
    float32
    dask.array<chunksize=(1, 1016, 347), meta=np.ndarray>

    long_name :

    vertically integrated u-momentum component

    units :

    meter second-1

    time :

    ocean_time

    grid :

    grid

    location :

    edge1

    field :

    ubar-velocity, scalar, series

    Array Chunk Bytes 2.69 MiB 1.34 MiB Shape (2, 1016, 347) (1, 1016, 347) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • vbar
    (ocean_time, eta_v, xi_v)
    float32
    dask.array<chunksize=(1, 1015, 348), meta=np.ndarray>

    long_name :

    vertically integrated v-momentum component

    units :

    meter second-1

    time :

    ocean_time

    grid :

    grid

    location :

    edge2

    field :

    vbar-velocity, scalar, series

    Array Chunk Bytes 2.69 MiB 1.35 MiB Shape (2, 1015, 348) (1, 1015, 348) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • u
    (ocean_time, s_rho, eta_u, xi_u)
    float32
    dask.array<chunksize=(1, 40, 1016, 347), meta=np.ndarray>

    long_name :

    u-momentum component

    units :

    meter second-1

    time :

    ocean_time

    grid :

    grid

    location :

    edge1

    field :

    u-velocity, scalar, series

    standard_name :

    eastward_sea_water_velocity

    Array Chunk Bytes 107.59 MiB 53.80 MiB Shape (2, 40, 1016, 347) (1, 40, 1016, 347) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • v
    (ocean_time, s_rho, eta_v, xi_v)
    float32
    dask.array<chunksize=(1, 40, 1015, 348), meta=np.ndarray>

    long_name :

    v-momentum component

    units :

    meter second-1

    time :

    ocean_time

    grid :

    grid

    location :

    edge2

    field :

    v-velocity, scalar, series

    standard_name :

    northward_sea_water_velocity

    Array Chunk Bytes 107.79 MiB 53.90 MiB Shape (2, 40, 1015, 348) (1, 40, 1015, 348) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • w
    (ocean_time, s_w, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(1, 41, 1016, 348), meta=np.ndarray>

    long_name :

    vertical momentum component

    units :

    meter second-1

    time :

    ocean_time

    standard_name :

    upward_sea_water_velocity

    grid :

    grid

    location :

    face

    field :

    w-velocity, scalar, series

    Array Chunk Bytes 110.60 MiB 55.30 MiB Shape (2, 41, 1016, 348) (1, 41, 1016, 348) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • temp
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(1, 40, 1016, 348), meta=np.ndarray>

    long_name :

    potential temperature

    units :

    Celsius

    time :

    ocean_time

    grid :

    grid

    location :

    face

    field :

    temperature, scalar, series

    standard_name :

    sea_water_temperature

    Array Chunk Bytes 107.90 MiB 53.95 MiB Shape (2, 40, 1016, 348) (1, 40, 1016, 348) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(1, 40, 1016, 348), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    grid :

    grid

    location :

    face

    field :

    salinity, scalar, series

    standard_name :

    sea_water_practical_salinity

    Array Chunk Bytes 107.90 MiB 53.95 MiB Shape (2, 40, 1016, 348) (1, 40, 1016, 348) Count 26 Tasks 2 Chunks Type float32 numpy.ndarray

• Attributes: (37)

  file :

  nos.wcofs.fields.nowcast.20221005.t03z_0002.nc

  format :

  netCDF-3 64bit offset file

  Conventions :

  CF-1.4, SGRID-0.3

  type :

  ROMS/TOMS history file

  title :

  wcofs nowcast RUN in operational mode

  var_info :

  varinfo.dat

  rst_file :

  nos.wcofs.rst.nowcast.20221005.t03z.nc

  his_base :

  nos.wcofs.fields.nowcast.20221005.t03z

  avg_file :

  nos.wcofs.avg.nc

  sta_file :

  nos.wcofs.stations.nowcast.20221005.t03z.nc

  grd_file :

  nos.wcofs.romsgrid.nc

  ini_file :

  nos.wcofs.init.nowcast.20221005.t03z.nc

  tide_file :

  nos.wcofs.roms.tides.nc

  frc_file_01 :

  nos.wcofs.met.nowcast.20221005.t03z.nc

  bry_file_01 :

  nos.wcofs.obc.20221005.t03z.nc

  clm_file_01 :

  nos.wcofs.clim.20221005.t03z.nc

  nud_file :

  nos.wcofs.nudgcoef.nc

  script_file :

  wcofs_ROMS_nowcast.in

  spos_file :

  nos.wcofs.stations.in

  NLM_TADV :

  ADVECTION: HORIZONTAL VERTICAL temp: Upstream3 Centered4 salt: Upstream3 Centered4

  NLM_LBC :

  EDGE: WEST SOUTH EAST NORTH zeta: Cha Cha Clo Cha ubar: Fla Fla Clo Fla vbar: Fla Fla Clo Fla u: RadNud RadNud Clo RadNud v: RadNud RadNud Clo RadNud temp: RadNud RadNud Clo RadNud salt: RadNud RadNud Clo RadNud tke: Gra Gra Clo Gra

  svn_url :

  svn_rev :

  code_dir :

  /lfs/h1/ops/prod/packages/nosofs.v3.4.2/sorc/ROMS.fd

  header_dir :

  /lfs/h1/ops/prod/packages/nosofs.v3.4.2/sorc/ROMS.fd/ROMS/Include

  header_file :

  wcofs.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ftn-intel

  compiler_command :

  /opt/cray/pe/craype/2.7.10/bin/ftn

  compiler_flags :

  -fp-model precise -ip -O2

  tiling :

  008x060

  history :

  ROMS/TOMS, Version 3.9, Wednesday - October 5, 2022 - 3:08:59 AM

  ana_file :

  ROMS/Functionals/ana_btflux.h

  CPP_options :

  mode, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, AVERAGES, !BOUNDARY_ALLGATHER, BULK_FLUXES, !COLLECT_ALL..., CURVGRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, LIMIT_STFLX_COOLING, LONGWAVE_OUT, MASKING, MIX_S_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, POWER_LAW, PROFILE, K_GSCHEME, RADIATION_2D, REDUCE_ALLREDUCE, RI_SPLINES, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES_VDIFF, SPLINES_VVISC, SSH_TIDES, STATIONS, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D

  DODS_EXTRA.Unlimited_Dimension :

  ocean_time

  EXTRA_DIMENSION.N :

  40

Example #2:

%%time
source_LMHOFS.to_dask()

CPU times: user 3.28 s, sys: 1.16 s, total: 4.45 s
Wall time: 14.7 s

<xarray.Dataset>
Dimensions:             (nele: 171377, node: 90806, three: 3, time: 24,
                         maxnode: 11, maxelem: 9, four: 4, siglay: 20,
                         siglev: 21)
Coordinates:
    lon                 (node) float32 dask.array<chunksize=(90806,), meta=np.ndarray>
    lat                 (node) float32 dask.array<chunksize=(90806,), meta=np.ndarray>
    lonc                (nele) float32 dask.array<chunksize=(171377,), meta=np.ndarray>
    latc                (nele) float32 dask.array<chunksize=(171377,), meta=np.ndarray>
  * time                (time) datetime64[ns] 2022-10-05 ... 2022-10-05T23:00...
    sigma_layers        (siglay, node) float32 dask.array<chunksize=(20, 90806), meta=np.ndarray>
    sigma_levels        (siglev, node) float32 dask.array<chunksize=(21, 90806), meta=np.ndarray>
Dimensions without coordinates: nele, node, three, maxnode, maxelem, four,
                                siglay, siglev
Data variables: (12/48)
    nprocs              int32 ...
    partition           (nele) int32 dask.array<chunksize=(171377,), meta=np.ndarray>
    x                   (node) float32 dask.array<chunksize=(90806,), meta=np.ndarray>
    y                   (node) float32 dask.array<chunksize=(90806,), meta=np.ndarray>
    xc                  (nele) float32 dask.array<chunksize=(171377,), meta=np.ndarray>
    yc                  (nele) float32 dask.array<chunksize=(171377,), meta=np.ndarray>
    ...                  ...
    atmos_press         (time, node) float32 dask.array<chunksize=(1, 90806), meta=np.ndarray>
    wet_nodes           (time, node) int32 dask.array<chunksize=(1, 90806), meta=np.ndarray>
    wet_cells           (time, nele) int32 dask.array<chunksize=(1, 171377), meta=np.ndarray>
    wet_nodes_prev_int  (time, node) int32 dask.array<chunksize=(1, 90806), meta=np.ndarray>
    wet_cells_prev_int  (time, nele) int32 dask.array<chunksize=(1, 171377), meta=np.ndarray>
    wet_cells_prev_ext  (time, nele) int32 dask.array<chunksize=(1, 171377), meta=np.ndarray>
Attributes: (12/17)
    title:                           LMHOFS
    institution:                     School for Marine Science and Technology
    source:                          FVCOM_4.3
    history:                         model started at: 05/10/2022   07:15
    references:                      http://fvcom.smast.umassd.edu, http://co...
    Conventions:                     CF-1.0
    ...                              ...
    Surface_Heat_Forcing:            FVCOM variable surface heat forcing file...
    Surface_Wind_Forcing:            FVCOM variable surface Wind forcing:\nFI...
    Surface_PrecipEvap_Forcing:      FVCOM periodic surface precip forcing:\n...
    DODS.strlen:                     26
    DODS.dimName:                    DateStrLen
    DODS_EXTRA.Unlimited_Dimension:  time

xarray.Dataset

• Dimensions:
  • nele: 171377
  • node: 90806
  • three: 3
  • time: 24
  • maxnode: 11
  • maxelem: 9
  • four: 4
  • siglay: 20
  • siglev: 21
• Coordinates: (7)
  • lon
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    nodal longitude

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • lat
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    nodal latitude

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • lonc
    (nele)
    float32
    dask.array<chunksize=(171377,), meta=np.ndarray>

    long_name :

    zonal longitude

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 669.44 kiB 669.44 kiB Shape (171377,) (171377,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • latc
    (nele)
    float32
    dask.array<chunksize=(171377,), meta=np.ndarray>

    long_name :

    zonal latitude

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 669.44 kiB 669.44 kiB Shape (171377,) (171377,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • time
    (time)
    datetime64[ns]
    2022-10-05 ... 2022-10-05T23:00:...

    long_name :

    time

    format :

    defined reference date

    time_zone :

    UTC

    standard_name :

    time

    axis :

    T

    array(['2022-10-05T00:00:00.000000000', '2022-10-05T00:59:56.484375008',
           '2022-10-05T02:00:03.515624992', '2022-10-05T03:00:00.000000000',
           '2022-10-05T03:59:56.484375008', '2022-10-05T05:00:03.515624992',
           '2022-10-05T06:00:00.000000000', '2022-10-05T06:59:56.484375008',
           '2022-10-05T08:00:03.515624992', '2022-10-05T09:00:00.000000000',
           '2022-10-05T09:59:56.484375008', '2022-10-05T11:00:03.515624992',
           '2022-10-05T12:00:00.000000000', '2022-10-05T12:59:56.484375008',
           '2022-10-05T14:00:03.515624992', '2022-10-05T15:00:00.000000000',
           '2022-10-05T15:59:56.484375008', '2022-10-05T17:00:03.515624992',
           '2022-10-05T18:00:00.000000000', '2022-10-05T18:59:56.484375008',
           '2022-10-05T20:00:03.515624992', '2022-10-05T21:00:00.000000000',
           '2022-10-05T21:59:56.484375008', '2022-10-05T23:00:03.515624992'],
          dtype='datetime64[ns]')

  • sigma_layers
    (siglay, node)
    float32
    dask.array<chunksize=(20, 90806), meta=np.ndarray>

    long_name :

    Sigma Layers

    standard_name :

    ocean_sigma_coordinate

    positive :

    up

    valid_min :

    -1.0

    valid_max :

    0.0

    formula_terms :

    sigma: sigma_layers eta: zeta depth: h

    Array Chunk Bytes 6.93 MiB 6.93 MiB Shape (20, 90806) (20, 90806) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • sigma_levels
    (siglev, node)
    float32
    dask.array<chunksize=(21, 90806), meta=np.ndarray>

    long_name :

    Sigma Levels

    standard_name :

    ocean_sigma_coordinate

    positive :

    up

    valid_min :

    -1.0

    valid_max :

    0.0

    formula_terms :

    sigma: sigma_levels eta: zeta depth: h

    Array Chunk Bytes 7.27 MiB 7.27 MiB Shape (21, 90806) (21, 90806) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (48)
  • nprocs
    ()
    int32
    ...

    long_name :

    number of processors

    array(630, dtype=int32)

  • partition
    (nele)
    int32
    dask.array<chunksize=(171377,), meta=np.ndarray>

    long_name :

    partition

    Array Chunk Bytes 669.44 kiB 669.44 kiB Shape (171377,) (171377,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • x
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    nodal x-coordinate

    units :

    meters

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • y
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    nodal y-coordinate

    units :

    meters

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • xc
    (nele)
    float32
    dask.array<chunksize=(171377,), meta=np.ndarray>

    long_name :

    zonal x-coordinate

    units :

    meters

    Array Chunk Bytes 669.44 kiB 669.44 kiB Shape (171377,) (171377,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • yc
    (nele)
    float32
    dask.array<chunksize=(171377,), meta=np.ndarray>

    long_name :

    zonal y-coordinate

    units :

    meters

    Array Chunk Bytes 669.44 kiB 669.44 kiB Shape (171377,) (171377,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • h
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    Bathymetry

    standard_name :

    sea_floor_depth

    units :

    m

    positive :

    down

    grid :

    Bathymetry_Mesh

    type :

    data

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • nv
    (three, nele)
    int32
    dask.array<chunksize=(3, 171377), meta=np.ndarray>

    long_name :

    nodes surrounding element

    Array Chunk Bytes 1.96 MiB 1.96 MiB Shape (3, 171377) (3, 171377) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • iint
    (time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    internal mode iteration number

    Array Chunk Bytes 96 B 4 B Shape (24,) (1,) Count 72 Tasks 24 Chunks Type int32 numpy.ndarray

  • Times
    (time)
    |S64
    dask.array<chunksize=(1,), meta=np.ndarray>

    time_zone :

    UTC

    Array Chunk Bytes 1.50 kiB 64 B Shape (24,) (1,) Count 72 Tasks 24 Chunks Type |S64 numpy.ndarray

  • zeta
    (time, node)
    float32
    dask.array<chunksize=(1, 90806), meta=np.ndarray>

    long_name :

    Water Surface Elevation

    units :

    meters

    positive :

    up

    standard_name :

    sea_surface_elevation

    grid :

    Bathymetry_Mesh

    type :

    data

    location :

    node

    Array Chunk Bytes 8.31 MiB 354.71 kiB Shape (24, 90806) (1, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • nbe
    (three, nele)
    int32
    dask.array<chunksize=(3, 171377), meta=np.ndarray>

    long_name :

    elements surrounding each element

    Array Chunk Bytes 1.96 MiB 1.96 MiB Shape (3, 171377) (3, 171377) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • ntsn
    (node)
    int32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    #nodes surrounding each node

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • nbsn
    (maxnode, node)
    int32
    dask.array<chunksize=(11, 90806), meta=np.ndarray>

    long_name :

    nodes surrounding each node

    Array Chunk Bytes 3.81 MiB 3.81 MiB Shape (11, 90806) (11, 90806) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • ntve
    (node)
    int32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    #elems surrounding each node

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • nbve
    (maxelem, node)
    int32
    dask.array<chunksize=(9, 90806), meta=np.ndarray>

    long_name :

    elems surrounding each node

    Array Chunk Bytes 3.12 MiB 3.12 MiB Shape (9, 90806) (9, 90806) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray

  • a1u
    (four, nele)
    float32
    dask.array<chunksize=(4, 171377), meta=np.ndarray>

    long_name :

    a1u

    Array Chunk Bytes 2.62 MiB 2.62 MiB Shape (4, 171377) (4, 171377) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • a2u
    (four, nele)
    float32
    dask.array<chunksize=(4, 171377), meta=np.ndarray>

    long_name :

    a2u

    Array Chunk Bytes 2.62 MiB 2.62 MiB Shape (4, 171377) (4, 171377) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • aw0
    (three, nele)
    float32
    dask.array<chunksize=(3, 171377), meta=np.ndarray>

    long_name :

    aw0

    Array Chunk Bytes 1.96 MiB 1.96 MiB Shape (3, 171377) (3, 171377) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • awx
    (three, nele)
    float32
    dask.array<chunksize=(3, 171377), meta=np.ndarray>

    long_name :

    awx

    Array Chunk Bytes 1.96 MiB 1.96 MiB Shape (3, 171377) (3, 171377) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • awy
    (three, nele)
    float32
    dask.array<chunksize=(3, 171377), meta=np.ndarray>

    long_name :

    awy

    Array Chunk Bytes 1.96 MiB 1.96 MiB Shape (3, 171377) (3, 171377) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • art2
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    Area of elements around a node

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • art1
    (node)
    float32
    dask.array<chunksize=(90806,), meta=np.ndarray>

    long_name :

    Area of Node-Base Control volume

    Array Chunk Bytes 354.71 kiB 354.71 kiB Shape (90806,) (90806,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • u
    (time, siglay, nele)
    float32
    dask.array<chunksize=(1, 20, 171377), meta=np.ndarray>

    long_name :

    Eastward Water Velocity

    standard_name :

    eastward_sea_water_velocity

    units :

    meters s-1

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 313.80 MiB 13.08 MiB Shape (24, 20, 171377) (1, 20, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • v
    (time, siglay, nele)
    float32
    dask.array<chunksize=(1, 20, 171377), meta=np.ndarray>

    long_name :

    Northward Water Velocity

    standard_name :

    northward_sea_water_velocity

    units :

    meters s-1

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 313.80 MiB 13.08 MiB Shape (24, 20, 171377) (1, 20, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • tauc
    (time, nele)
    float32
    dask.array<chunksize=(1, 171377), meta=np.ndarray>

    long_name :

    bed stress magnitude from currents

    note1 :

    this stress is bottom boundary condtion on velocity field

    note2 :

    dimensions are stress/rho

    units :

    m^2 s^-2

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 15.69 MiB 669.44 kiB Shape (24, 171377) (1, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • omega
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Vertical Sigma Coordinate Velocity

    units :

    s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • ww
    (time, siglay, nele)
    float32
    dask.array<chunksize=(1, 20, 171377), meta=np.ndarray>

    long_name :

    Upward Water Velocity

    units :

    meters s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 313.80 MiB 13.08 MiB Shape (24, 20, 171377) (1, 20, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • temp
    (time, siglay, node)
    float32
    dask.array<chunksize=(1, 20, 90806), meta=np.ndarray>

    long_name :

    temperature

    standard_name :

    sea_water_temperature

    units :

    degrees_C

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    node

    Array Chunk Bytes 166.27 MiB 6.93 MiB Shape (24, 20, 90806) (1, 20, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • salinity
    (time, siglay, node)
    float32
    dask.array<chunksize=(1, 20, 90806), meta=np.ndarray>

    long_name :

    salinity

    standard_name :

    sea_water_practical_salinity

    units :

    1e-3

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    node

    Array Chunk Bytes 166.27 MiB 6.93 MiB Shape (24, 20, 90806) (1, 20, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • viscofm
    (time, siglay, nele)
    float32
    dask.array<chunksize=(1, 20, 171377), meta=np.ndarray>

    long_name :

    Horizontal Turbulent Eddy Viscosity For Momentum

    units :

    m 2 s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 313.80 MiB 13.08 MiB Shape (24, 20, 171377) (1, 20, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • viscofh
    (time, siglay, node)
    float32
    dask.array<chunksize=(1, 20, 90806), meta=np.ndarray>

    long_name :

    Horizontal Turbulent Eddy Viscosity For Scalars

    units :

    m 2 s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 166.27 MiB 6.93 MiB Shape (24, 20, 90806) (1, 20, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • km
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Turbulent Eddy Viscosity For Momentum

    units :

    m 2 s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • kh
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Turbulent Eddy Viscosity For Scalars

    units :

    m 2 s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • kq
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Turbulent Eddy Viscosity For Q2/Q2L

    units :

    m 2 s-1

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • q2
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Turbulent Kinetic Energy

    units :

    m2 s-2

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • q2l
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Turbulent Kinetic Energy X Turbulent Macroscale

    units :

    m3 s-2

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • l
    (time, siglev, node)
    float32
    dask.array<chunksize=(1, 21, 90806), meta=np.ndarray>

    long_name :

    Turbulent Macroscale

    units :

    m3 s-2

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 174.58 MiB 7.27 MiB Shape (24, 21, 90806) (1, 21, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • short_wave
    (time, node)
    float32
    dask.array<chunksize=(1, 90806), meta=np.ndarray>

    long_name :

    Short Wave Radiation

    units :

    W m-2

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    node

    Array Chunk Bytes 8.31 MiB 354.71 kiB Shape (24, 90806) (1, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • net_heat_flux
    (time, node)
    float32
    dask.array<chunksize=(1, 90806), meta=np.ndarray>

    long_name :

    Surface Net Heat Flux

    units :

    W m-2

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    node

    Array Chunk Bytes 8.31 MiB 354.71 kiB Shape (24, 90806) (1, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • uwind_speed
    (time, nele)
    float32
    dask.array<chunksize=(1, 171377), meta=np.ndarray>

    long_name :

    Eastward Wind Velocity

    standard_name :

    eastward_wind

    units :

    meters s-1

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 15.69 MiB 669.44 kiB Shape (24, 171377) (1, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • vwind_speed
    (time, nele)
    float32
    dask.array<chunksize=(1, 171377), meta=np.ndarray>

    long_name :

    Northward Wind Velocity

    standard_name :

    northward_wind

    units :

    meters s-1

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 15.69 MiB 669.44 kiB Shape (24, 171377) (1, 171377) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • atmos_press
    (time, node)
    float32
    dask.array<chunksize=(1, 90806), meta=np.ndarray>

    long_name :

    Atmospheric Pressure

    units :

    pascals

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 8.31 MiB 354.71 kiB Shape (24, 90806) (1, 90806) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

  • wet_nodes
    (time, node)
    int32
    dask.array<chunksize=(1, 90806), meta=np.ndarray>

    long_name :

    Wet_Nodes

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    node

    Array Chunk Bytes 8.31 MiB 354.71 kiB Shape (24, 90806) (1, 90806) Count 72 Tasks 24 Chunks Type int32 numpy.ndarray

  • wet_cells
    (time, nele)
    int32
    dask.array<chunksize=(1, 171377), meta=np.ndarray>

    long_name :

    Wet_Cells

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 15.69 MiB 669.44 kiB Shape (24, 171377) (1, 171377) Count 72 Tasks 24 Chunks Type int32 numpy.ndarray

  • wet_nodes_prev_int
    (time, node)
    int32
    dask.array<chunksize=(1, 90806), meta=np.ndarray>

    long_name :

    Wet_Nodes_At_Previous_Internal_Step

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    node

    Array Chunk Bytes 8.31 MiB 354.71 kiB Shape (24, 90806) (1, 90806) Count 72 Tasks 24 Chunks Type int32 numpy.ndarray

  • wet_cells_prev_int
    (time, nele)
    int32
    dask.array<chunksize=(1, 171377), meta=np.ndarray>

    long_name :

    Wet_Cells_At_Previous_Internal_Step

    grid :

    fvcom_grid

    type :

    data

    mesh :

    fvcom_mesh

    location :

    face

    Array Chunk Bytes 15.69 MiB 669.44 kiB Shape (24, 171377) (1, 171377) Count 72 Tasks 24 Chunks Type int32 numpy.ndarray

  • wet_cells_prev_ext
    (time, nele)
    int32
    dask.array<chunksize=(1, 171377), meta=np.ndarray>

    long_name :

    Wet_Cells_At_Previous_External_Step

    grid :

    fvcom_grid

    type :

    data

    Array Chunk Bytes 15.69 MiB 669.44 kiB Shape (24, 171377) (1, 171377) Count 72 Tasks 24 Chunks Type int32 numpy.ndarray

• Attributes: (17)

  title :

  LMHOFS

  institution :

  School for Marine Science and Technology

  source :

  FVCOM_4.3

  history :

  model started at: 05/10/2022 07:15

  references :

  http://fvcom.smast.umassd.edu, http://codfish.smast.umassd.edu

  Conventions :

  CF-1.0

  CoordinateSystem :

  GeoReferenced

  CoordinateProjection :

  proj=utm +zone=16 +datum=NAD83

  Tidal_Forcing :

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

  River_Forcing :

  THERE ARE 12 RIVERS IN THIS MODEL. RIVER INFLOW IS ON THE edges WHERE TEMPERATURE AND SALINITY ARE specified IN THE MODEL. THE FOLLOWING RIVER NAMES ARE USED: 04127885 04127885 04159130 04159130 04159130 04157005 04157005 04157005 04157005 040851385 040851385 040851385

  GroundWater_Forcing :

  GROUND WATER FORCING IS OFF!

  Surface_Heat_Forcing :

  FVCOM variable surface heat forcing file: FILE NAME:nos.lmhofs.hflux.nowcast.20221005.t06z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format

  Surface_Wind_Forcing :

  FVCOM variable surface Wind forcing: FILE NAME:nos.lmhofs.met.nowcast.20221005.t06z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format

  Surface_PrecipEvap_Forcing :

  FVCOM periodic surface precip forcing: FILE NAME:nos.lmhofs.met.nowcast.20221005.t06z.nc SOURCE:FVCOM grid (unstructured) surface forcing Unknown start date meta data format

  DODS.strlen :

  26

  DODS.dimName :

  DateStrLen

  DODS_EXTRA.Unlimited_Dimension :

  time

Example #3 (if you know you have direct access to static link):

%%time
main_cat['GOFS']['hycom-forecast-agg'].to_dask()

CPU times: user 121 µs, sys: 1 µs, total: 122 µs
Wall time: 126 µs

<xarray.Dataset>
Dimensions:            (depth: 40, lat: 4251, lon: 4500, time: 8)
Coordinates:
  * depth              (depth) float64 0.0 2.0 4.0 6.0 ... 3e+03 4e+03 5e+03
  * lat                (lat) float64 -80.0 -79.96 -79.92 ... 89.92 89.96 90.0
  * lon                (lon) float64 0.0 0.07996 0.16 0.24 ... 359.8 359.8 359.9
  * time               (time) datetime64[ns] 2022-10-05T18:00:00 ... 2022-10-...
    time_run           (time) datetime64[ns] 2022-10-04T12:00:00 ... 2022-10-...
Data variables:
    time_offset        (time) datetime64[ns] 2022-09-30T18:00:00 ... 2022-10-...
    water_u            (time, depth, lat, lon) float32 ...
    water_u_bottom     (time, lat, lon) float32 ...
    water_v            (time, depth, lat, lon) float32 ...
    water_v_bottom     (time, lat, lon) float32 ...
    water_temp         (time, depth, lat, lon) float32 ...
    water_temp_bottom  (time, lat, lon) float32 ...
    salinity           (time, depth, lat, lon) float32 ...
    salinity_bottom    (time, lat, lon) float32 ...
    surf_el            (time, lat, lon) float32 ...
Attributes: (12/14)
    classification_level:      UNCLASSIFIED
    distribution_statement:    Approved for public release. Distribution unli...
    downgrade_date:            not applicable
    classification_authority:  not applicable
    institution:               Fleet Numerical Meteorology and Oceanography C...
    source:                    HYCOM archive file
    ...                        ...
    field_type:                instantaneous
    Conventions:               CF-1.4, NAVO_netcdf_v1.1
    _CoordSysBuilder:          ucar.nc2.dataset.conv.CF1Convention
    cdm_data_type:             GRID
    featureType:               GRID
    location:                  Proto fmrc:GLBy0.08_930_FMRC

xarray.Dataset

• Dimensions:
  • depth: 40
  • lat: 4251
  • lon: 4500
  • time: 8
• Coordinates: (5)
  • depth
    (depth)
    float64
    0.0 2.0 4.0 ... 3e+03 4e+03 5e+03

    units :

    m

    long_name :

    Depth

    standard_name :

    depth

    positive :

    down

    axis :

    Z

    NAVO_code :

    5

    _CoordinateAxisType :

    Height

    _CoordinateZisPositive :

    down

    array([0.00e+00, 2.00e+00, 4.00e+00, 6.00e+00, 8.00e+00, 1.00e+01, 1.20e+01,
           1.50e+01, 2.00e+01, 2.50e+01, 3.00e+01, 3.50e+01, 4.00e+01, 4.50e+01,
           5.00e+01, 6.00e+01, 7.00e+01, 8.00e+01, 9.00e+01, 1.00e+02, 1.25e+02,
           1.50e+02, 2.00e+02, 2.50e+02, 3.00e+02, 3.50e+02, 4.00e+02, 5.00e+02,
           6.00e+02, 7.00e+02, 8.00e+02, 9.00e+02, 1.00e+03, 1.25e+03, 1.50e+03,
           2.00e+03, 2.50e+03, 3.00e+03, 4.00e+03, 5.00e+03])

  • lat
    (lat)
    float64
    -80.0 -79.96 -79.92 ... 89.96 90.0

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    point_spacing :

    even

    axis :

    Y

    NAVO_code :

    1

    _CoordinateAxisType :

    Lat

    array([-80.      , -79.959999, -79.919998, ...,  89.919998,  89.959999,
            90.      ])

  • lon
    (lon)
    float64
    0.0 0.07996 0.16 ... 359.8 359.9

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    modulo :

    360 degrees

    axis :

    X

    NAVO_code :

    2

    _CoordinateAxisType :

    Lon

    array([0.000000e+00, 7.995605e-02, 1.600342e-01, ..., 3.597600e+02,
           3.598400e+02, 3.599200e+02])

  • time
    (time)
    datetime64[ns]
    2022-10-05T18:00:00 ... 2022-10-...

    long_name :

    Forecast time for ForecastModelRunCollection

    standard_name :

    time

    _CoordinateAxisType :

    Time

    axis :

    T

    array(['2022-10-05T18:00:00.000000000', '2022-10-05T21:00:00.000000000',
           '2022-10-06T00:00:00.000000000', '2022-10-06T03:00:00.000000000',
           '2022-10-06T06:00:00.000000000', '2022-10-06T09:00:00.000000000',
           '2022-10-06T12:00:00.000000000', '2022-10-06T15:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_run
    (time)
    datetime64[ns]
    2022-10-04T12:00:00 ... 2022-10-...

    long_name :

    run times for coordinate = time

    standard_name :

    forecast_reference_time

    _CoordinateAxisType :

    RunTime

    array(['2022-10-04T12:00:00.000000000', '2022-10-04T12:00:00.000000000',
           '2022-10-04T12:00:00.000000000', '2022-10-04T12:00:00.000000000',
           '2022-10-04T12:00:00.000000000', '2022-10-04T12:00:00.000000000',
           '2022-10-04T12:00:00.000000000', '2022-10-04T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (10)
  • time_offset
    (time)
    datetime64[ns]
    ...

    long_name :

    offset hour from start of run for coordinate = time

    standard_name :

    forecast_period

    array(['2022-09-30T18:00:00.000000000', '2022-09-30T21:00:00.000000000',
           '2022-10-01T00:00:00.000000000', '2022-10-01T03:00:00.000000000',
           '2022-10-01T06:00:00.000000000', '2022-10-01T09:00:00.000000000',
           '2022-10-01T12:00:00.000000000', '2022-10-01T15:00:00.000000000'],
          dtype='datetime64[ns]')

  • water_u
    (time, depth, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time depth lat lon

    units :

    m/s

    long_name :

    Eastward Water Velocity

    standard_name :

    eastward_sea_water_velocity

    NAVO_code :

    17

    [6121440000 values with dtype=float32]

  • water_u_bottom
    (time, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time lat lon

    units :

    m/s

    long_name :

    Eastward Water Velocity

    standard_name :

    eastward_sea_water_velocity_at_bottom

    NAVO_code :

    17

    [153036000 values with dtype=float32]

  • water_v
    (time, depth, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time depth lat lon

    units :

    m/s

    long_name :

    Northward Water Velocity

    standard_name :

    northward_sea_water_velocity

    NAVO_code :

    18

    [6121440000 values with dtype=float32]

  • water_v_bottom
    (time, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time lat lon

    units :

    m/s

    long_name :

    Northward Water Velocity

    standard_name :

    northward_sea_water_velocity_at_bottom

    NAVO_code :

    18

    [153036000 values with dtype=float32]

  • water_temp
    (time, depth, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time depth lat lon

    units :

    degC

    long_name :

    Water Temperature

    standard_name :

    sea_water_temperature

    NAVO_code :

    15

    comment :

    in-situ temperature

    [6121440000 values with dtype=float32]

  • water_temp_bottom
    (time, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time lat lon

    units :

    degC

    long_name :

    Water Temperature

    standard_name :

    sea_water_temperature_at_bottom

    NAVO_code :

    15

    comment :

    in-situ temperature

    [153036000 values with dtype=float32]

  • salinity
    (time, depth, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time depth lat lon

    units :

    psu

    long_name :

    Salinity

    standard_name :

    sea_water_practical_salinity

    NAVO_code :

    16

    [6121440000 values with dtype=float32]

  • salinity_bottom
    (time, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time lat lon

    units :

    psu

    long_name :

    Salinity

    standard_name :

    sea_water_salinity_at_bottom

    NAVO_code :

    16

    [153036000 values with dtype=float32]

  • surf_el
    (time, lat, lon)
    float32
    ...

    _CoordinateAxes :

    time_run time lat lon

    units :

    m

    long_name :

    Water Surface Elevation

    standard_name :

    sea_surface_elevation

    NAVO_code :

    32

    [153036000 values with dtype=float32]

• Attributes: (14)

  classification_level :

  UNCLASSIFIED

  distribution_statement :

  Approved for public release. Distribution unlimited.

  downgrade_date :

  not applicable

  classification_authority :

  not applicable

  institution :

  Fleet Numerical Meteorology and Oceanography Center

  source :

  HYCOM archive file

  history :

  archv2ncdf2d ; FMRC Best Dataset

  comment :

  p-grid

  field_type :

  instantaneous

  Conventions :

  CF-1.4, NAVO_netcdf_v1.1

  _CoordSysBuilder :

  ucar.nc2.dataset.conv.CF1Convention

  cdm_data_type :

  GRID

  featureType :

  GRID

  location :

  Proto fmrc:GLBy0.08_930_FMRC

Other topics

NOAA OFS models: how to use filetypes besides default 3D “fields”

All NOAA OFS model configurations are available with 3D fields filetypes. However, for some models there are other filetypes:

• regular_grid: model output interpolated to rectilinear grid

• 2ds: only surface model output, variable names changed

You can see what model configurations are available, specifically including filetypes, by looking at the main catalog since they are in the catalog name:

list(main_cat)

['NYOFS',
 'CBOFS-RGRID',
 'DBOFS-RGRID',
 'WCOFS-RGRID',
 'LEOFS-RGRID',
 'RTOFS-EAST',
 'RTOFS-AK',
 'RTOFS-2D',
 'WCOFS-2DS',
 'GOFS',
 'GFS-1-2DEG',
 'SFBOFS-RGRID',
 'RTOFS',
 'CREOFS-RGRID',
 'TBOFS',
 'NGOFS2',
 'GFS-1DEG',
 'NGOFS2-RGRID',
 'GOMOFS-2DS',
 'CIOFS',
 'LMHOFS-RGRID',
 'GOMOFS-RGRID',
 'LMHOFS',
 'CIOFS-RGRID',
 'DBOFS',
 'LEOFS',
 'SFBOFS',
 'TBOFS-RGRID',
 'NGOFS2-2DS',
 'RTOFS-WEST',
 'LOOFS',
 'WCOFS',
 'GOMOFS',
 'GFS-1-4DEG',
 'CREOFS',
 'LSOFS',
 'CBOFS']

start_date = pd.Timestamp.today()
source_TBOFSRG = mc.select_date_range(main_cat['TBOFS-RGRID'], model_source='coops-forecast-noagg', 
                                      start_date=start_date, end_date=start_date)
ds = source_TBOFSRG.to_dask()

Access variables and axis

Metadata has been added to the model Datasets to facilitate certain variable and axis accessibility. The variable access is set up by adding standard variable names to the datasets when they are opened, based on the variables available, and the axis accessibility is first by adding some attributes and then by using cf-xarray. See the following examples.

Filter an xarray Dataset by the attribute of standard_name (returns an xarray Dataset):

ds.filter_by_attrs(standard_name='sea_water_practical_salinity')

<xarray.Dataset>
Dimensions:     (ocean_time: 24, Depth: 11, ny: 393, nx: 329)
Coordinates:
  * Depth       (Depth) float64 0.0 2.0 4.0 6.0 8.0 ... 12.0 15.0 20.0 25.0 30.0
    Latitude    (ny, nx) float64 dask.array<chunksize=(393, 329), meta=np.ndarray>
    Longitude   (ny, nx) float64 dask.array<chunksize=(393, 329), meta=np.ndarray>
  * ocean_time  (ocean_time) datetime64[ns] 2022-10-05 ... 2022-10-05T23:00:00
  * nx          (nx) int64 0 1 2 3 4 5 6 7 8 ... 321 322 323 324 325 326 327 328
  * ny          (ny) int64 0 1 2 3 4 5 6 7 8 ... 385 386 387 388 389 390 391 392
Data variables:
    salt        (ocean_time, Depth, ny, nx) float32 dask.array<chunksize=(1, 11, 393, 329), meta=np.ndarray>
Attributes: (12/33)
    file:                            nos.tbofs.fields.nowcast.20221005.t00z_0...
    format:                          netCDF-4/HDF5 file
    Conventions:                     CF-1.4, SGRID-0.3
    type:                            ROMS/TOMS history file
    title:                           tbofs nowcast RUN in operational mode
    var_info:                        varinfo.dat
    ...                              ...
    compiler_flags:                  -fp-model precise -ip -O2
    tiling:                          008x016
    history:                         ROMS/TOMS, Version 3.9, Wednesday - Octo...
    ana_file:                        ROMS/Functionals/ana_btflux.h, ROMS/Func...
    CPP_options:                     mode, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ...
    DODS_EXTRA.Unlimited_Dimension:  ocean_time

xarray.Dataset

• Dimensions:
  • ocean_time: 24
  • Depth: 11
  • ny: 393
  • nx: 329
• Coordinates: (6)
  • Depth
    (Depth)
    float64
    0.0 2.0 4.0 6.0 ... 20.0 25.0 30.0

    long_name :

    Depths of Standard Layer

    units :

    m

    positive :

    down

    axis :

    Z

    array([ 0.,  2.,  4.,  6.,  8., 10., 12., 15., 20., 25., 30.])

  • Latitude
    (ny, nx)
    float64
    dask.array<chunksize=(393, 329), meta=np.ndarray>

    long_name :

    Latitude in common grid

    units :

    degrees_north

    standard_name :

    latitude

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (393, 329) (393, 329) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • Longitude
    (ny, nx)
    float64
    dask.array<chunksize=(393, 329), meta=np.ndarray>

    long_name :

    Longitude in common grid

    units :

    degrees_east

    standard_name :

    longitude

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (393, 329) (393, 329) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2022-10-05 ... 2022-10-05T23:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    standard_name :

    time

    axis :

    T

    array(['2022-10-05T00:00:00.000000000', '2022-10-05T01:00:00.000000000',
           '2022-10-05T02:00:00.000000000', '2022-10-05T03:00:00.000000000',
           '2022-10-05T04:00:00.000000000', '2022-10-05T05:00:00.000000000',
           '2022-10-05T06:00:00.000000000', '2022-10-05T07:00:00.000000000',
           '2022-10-05T08:00:00.000000000', '2022-10-05T09:00:00.000000000',
           '2022-10-05T10:00:00.000000000', '2022-10-05T11:00:00.000000000',
           '2022-10-05T12:00:00.000000000', '2022-10-05T13:00:00.000000000',
           '2022-10-05T14:00:00.000000000', '2022-10-05T15:00:00.000000000',
           '2022-10-05T16:00:00.000000000', '2022-10-05T17:00:00.000000000',
           '2022-10-05T18:00:00.000000000', '2022-10-05T19:00:00.000000000',
           '2022-10-05T20:00:00.000000000', '2022-10-05T21:00:00.000000000',
           '2022-10-05T22:00:00.000000000', '2022-10-05T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • nx
    (nx)
    int64
    0 1 2 3 4 5 ... 324 325 326 327 328

    axis :

    X

    array([  0,   1,   2, ..., 326, 327, 328])

  • ny
    (ny)
    int64
    0 1 2 3 4 5 ... 388 389 390 391 392

    axis :

    Y

    array([  0,   1,   2, ..., 390, 391, 392])

• Data variables: (1)
  • salt
    (ocean_time, Depth, ny, nx)
    float32
    dask.array<chunksize=(1, 11, 393, 329), meta=np.ndarray>

    long_name :

    salinity

    units :

    PSU

    time :

    ocean_time

    grid :

    grid

    location :

    face

    field :

    salinity, scalar, series

    standard_name :

    sea_water_practical_salinity

    Array Chunk Bytes 130.21 MiB 5.43 MiB Shape (24, 11, 393, 329) (1, 11, 393, 329) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

• Attributes: (33)

  file :

  nos.tbofs.fields.nowcast.20221005.t00z_0007.nc

  format :

  netCDF-4/HDF5 file

  Conventions :

  CF-1.4, SGRID-0.3

  type :

  ROMS/TOMS history file

  title :

  tbofs nowcast RUN in operational mode

  var_info :

  varinfo.dat

  rst_file :

  nos.tbofs.rst.nowcast.20221005.t00z.nc

  his_base :

  nos.tbofs.fields.nowcast.20221005.t00z

  sta_file :

  nos.tbofs.stations.nowcast.20221005.t00z.nc

  grd_file :

  nos.tbofs.romsgrid.nc

  ini_file :

  nos.tbofs.init.nowcast.20221005.t00z.nc

  tide_file :

  nos.tbofs.roms.tides.nc

  frc_file_01 :

  nos.tbofs.met.nowcast.20221005.t00z.nc

  bry_file_01 :

  nos.tbofs.obc.20221005.t00z.nc

  script_file :

  tbofs_ROMS_nowcast.in

  spos_file :

  nos.tbofs.stations.in

  NLM_TADV :

  ADVECTION: HORIZONTAL VERTICAL temp: HSIMT HSIMT salt: HSIMT HSIMT

  NLM_LBC :

  EDGE: WEST SOUTH EAST NORTH zeta: Clo Cha Clo Clo ubar: Clo Fla Clo Clo vbar: Clo Fla Clo Clo u: Clo Rad Clo Clo v: Clo Rad Clo Clo temp: Clo RadNud Clo Clo salt: Clo RadNud Clo Clo tke: Clo Gra Clo Clo

  svn_url :

  svn_rev :

  code_dir :

  /lfs/h1/ops/prod/packages/nosofs.v3.4.2/sorc/ROMS.fd

  header_dir :

  /lfs/h1/ops/prod/packages/nosofs.v3.4.2/sorc/ROMS.fd/ROMS/Include

  header_file :

  tbofs.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ftn-intel

  compiler_command :

  /opt/cray/pe/craype/2.7.10/bin/ftn

  compiler_flags :

  -fp-model precise -ip -O2

  tiling :

  008x016

  history :

  ROMS/TOMS, Version 3.9, Wednesday - October 5, 2022 - 1:01:02 AM

  ana_file :

  ROMS/Functionals/ana_btflux.h, ROMS/Functionals/ana_rain.h, ROMS/Functionals/ana_stflux.h

  CPP_options :

  mode, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ANA_RAIN, ANA_SSFLUX, ASSUMED_SHAPE, ATM_PRESS, !BOUNDARY_ALLGATHER, BULK_FLUXES, !COLLECT_ALL..., CURVGRID, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, HDF5, LIMIT_BSTRESS, KANTHA_CLAYSON, LONGWAVE_OUT, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, PERFECT_RESTART, POWER_LAW, PROFILE, K_GSCHEME, RADIATION_2D, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, STATIONS, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, VISC_GRID, WET_DRY

  DODS_EXTRA.Unlimited_Dimension :

  ocean_time

Alternatively, you could back out the variable name and use it directly (returns an xarray DataArray):

varname = source_TBOFSRG.metadata['standard_names']['sea_water_practical_salinity']
ds[varname]

<xarray.DataArray 'salt' (ocean_time: 24, Depth: 11, ny: 393, nx: 329)>
dask.array<concatenate, shape=(24, 11, 393, 329), dtype=float32, chunksize=(1, 11, 393, 329), chunktype=numpy.ndarray>
Coordinates:
  * Depth       (Depth) float64 0.0 2.0 4.0 6.0 8.0 ... 12.0 15.0 20.0 25.0 30.0
    Latitude    (ny, nx) float64 dask.array<chunksize=(393, 329), meta=np.ndarray>
    Longitude   (ny, nx) float64 dask.array<chunksize=(393, 329), meta=np.ndarray>
  * ocean_time  (ocean_time) datetime64[ns] 2022-10-05 ... 2022-10-05T23:00:00
  * nx          (nx) int64 0 1 2 3 4 5 6 7 8 ... 321 322 323 324 325 326 327 328
  * ny          (ny) int64 0 1 2 3 4 5 6 7 8 ... 385 386 387 388 389 390 391 392
Attributes:
    long_name:      salinity
    units:          PSU
    time:           ocean_time
    grid:           grid
    location:       face
    field:          salinity, scalar, series
    standard_name:  sea_water_practical_salinity

xarray.DataArray

'salt'

• ocean_time: 24
• Depth: 11
• ny: 393
• nx: 329

• dask.array<chunksize=(1, 11, 393, 329), meta=np.ndarray>

  Array Chunk Bytes 130.21 MiB 5.43 MiB Shape (24, 11, 393, 329) (1, 11, 393, 329) Count 72 Tasks 24 Chunks Type float32 numpy.ndarray

• Coordinates: (6)
  • Depth
    (Depth)
    float64
    0.0 2.0 4.0 6.0 ... 20.0 25.0 30.0

    long_name :

    Depths of Standard Layer

    units :

    m

    positive :

    down

    axis :

    Z

    array([ 0.,  2.,  4.,  6.,  8., 10., 12., 15., 20., 25., 30.])

  • Latitude
    (ny, nx)
    float64
    dask.array<chunksize=(393, 329), meta=np.ndarray>

    long_name :

    Latitude in common grid

    units :

    degrees_north

    standard_name :

    latitude

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (393, 329) (393, 329) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • Longitude
    (ny, nx)
    float64
    dask.array<chunksize=(393, 329), meta=np.ndarray>

    long_name :

    Longitude in common grid

    units :

    degrees_east

    standard_name :

    longitude

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (393, 329) (393, 329) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2022-10-05 ... 2022-10-05T23:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    standard_name :

    time

    axis :

    T

    array(['2022-10-05T00:00:00.000000000', '2022-10-05T01:00:00.000000000',
           '2022-10-05T02:00:00.000000000', '2022-10-05T03:00:00.000000000',
           '2022-10-05T04:00:00.000000000', '2022-10-05T05:00:00.000000000',
           '2022-10-05T06:00:00.000000000', '2022-10-05T07:00:00.000000000',
           '2022-10-05T08:00:00.000000000', '2022-10-05T09:00:00.000000000',
           '2022-10-05T10:00:00.000000000', '2022-10-05T11:00:00.000000000',
           '2022-10-05T12:00:00.000000000', '2022-10-05T13:00:00.000000000',
           '2022-10-05T14:00:00.000000000', '2022-10-05T15:00:00.000000000',
           '2022-10-05T16:00:00.000000000', '2022-10-05T17:00:00.000000000',
           '2022-10-05T18:00:00.000000000', '2022-10-05T19:00:00.000000000',
           '2022-10-05T20:00:00.000000000', '2022-10-05T21:00:00.000000000',
           '2022-10-05T22:00:00.000000000', '2022-10-05T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • nx
    (nx)
    int64
    0 1 2 3 4 5 ... 324 325 326 327 328

    axis :

    X

    array([  0,   1,   2, ..., 326, 327, 328])

  • ny
    (ny)
    int64
    0 1 2 3 4 5 ... 388 389 390 391 392

    axis :

    Y

    array([  0,   1,   2, ..., 390, 391, 392])

• Attributes: (7)

  long_name :

  salinity

  units :

  PSU

  time :

  ocean_time

  grid :

  grid

  location :

  face

  field :

  salinity, scalar, series

  standard_name :

  sea_water_practical_salinity

Refer to time axis without knowing the time variable name with cf-xarray:

ds.cf['T']

<xarray.DataArray 'ocean_time' (ocean_time: 24)>
array(['2022-10-05T00:00:00.000000000', '2022-10-05T01:00:00.000000000',
       '2022-10-05T02:00:00.000000000', '2022-10-05T03:00:00.000000000',
       '2022-10-05T04:00:00.000000000', '2022-10-05T05:00:00.000000000',
       '2022-10-05T06:00:00.000000000', '2022-10-05T07:00:00.000000000',
       '2022-10-05T08:00:00.000000000', '2022-10-05T09:00:00.000000000',
       '2022-10-05T10:00:00.000000000', '2022-10-05T11:00:00.000000000',
       '2022-10-05T12:00:00.000000000', '2022-10-05T13:00:00.000000000',
       '2022-10-05T14:00:00.000000000', '2022-10-05T15:00:00.000000000',
       '2022-10-05T16:00:00.000000000', '2022-10-05T17:00:00.000000000',
       '2022-10-05T18:00:00.000000000', '2022-10-05T19:00:00.000000000',
       '2022-10-05T20:00:00.000000000', '2022-10-05T21:00:00.000000000',
       '2022-10-05T22:00:00.000000000', '2022-10-05T23:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * ocean_time  (ocean_time) datetime64[ns] 2022-10-05 ... 2022-10-05T23:00:00
Attributes:
    long_name:      time since initialization
    field:          time, scalar, series
    standard_name:  time
    axis:           T

xarray.DataArray

'ocean_time'

• ocean_time: 24

• 2022-10-05 2022-10-05T01:00:00 ... 2022-10-05T23:00:00

  array(['2022-10-05T00:00:00.000000000', '2022-10-05T01:00:00.000000000',
         '2022-10-05T02:00:00.000000000', '2022-10-05T03:00:00.000000000',
         '2022-10-05T04:00:00.000000000', '2022-10-05T05:00:00.000000000',
         '2022-10-05T06:00:00.000000000', '2022-10-05T07:00:00.000000000',
         '2022-10-05T08:00:00.000000000', '2022-10-05T09:00:00.000000000',
         '2022-10-05T10:00:00.000000000', '2022-10-05T11:00:00.000000000',
         '2022-10-05T12:00:00.000000000', '2022-10-05T13:00:00.000000000',
         '2022-10-05T14:00:00.000000000', '2022-10-05T15:00:00.000000000',
         '2022-10-05T16:00:00.000000000', '2022-10-05T17:00:00.000000000',
         '2022-10-05T18:00:00.000000000', '2022-10-05T19:00:00.000000000',
         '2022-10-05T20:00:00.000000000', '2022-10-05T21:00:00.000000000',
         '2022-10-05T22:00:00.000000000', '2022-10-05T23:00:00.000000000'],
        dtype='datetime64[ns]')

• Coordinates: (1)
  • ocean_time
    (ocean_time)
    datetime64[ns]
    2022-10-05 ... 2022-10-05T23:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    standard_name :

    time

    axis :

    T

    array(['2022-10-05T00:00:00.000000000', '2022-10-05T01:00:00.000000000',
           '2022-10-05T02:00:00.000000000', '2022-10-05T03:00:00.000000000',
           '2022-10-05T04:00:00.000000000', '2022-10-05T05:00:00.000000000',
           '2022-10-05T06:00:00.000000000', '2022-10-05T07:00:00.000000000',
           '2022-10-05T08:00:00.000000000', '2022-10-05T09:00:00.000000000',
           '2022-10-05T10:00:00.000000000', '2022-10-05T11:00:00.000000000',
           '2022-10-05T12:00:00.000000000', '2022-10-05T13:00:00.000000000',
           '2022-10-05T14:00:00.000000000', '2022-10-05T15:00:00.000000000',
           '2022-10-05T16:00:00.000000000', '2022-10-05T17:00:00.000000000',
           '2022-10-05T18:00:00.000000000', '2022-10-05T19:00:00.000000000',
           '2022-10-05T20:00:00.000000000', '2022-10-05T21:00:00.000000000',
           '2022-10-05T22:00:00.000000000', '2022-10-05T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Attributes: (4)

  long_name :

  time since initialization

  field :

  time, scalar, series

  standard_name :

  time

  axis :

  T