Regridding to Healpix Grid using XESMF

import astropy.coordinates
import cartopy.crs as ccrs
import cdshealpix.nested
import cf_xarray  # noqa: F401
import dask
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import xdggs
import xesmf

xr.set_options(keep_attrs=True, display_expand_attrs=False, display_expand_data=False)

<xarray.core.options.set_options at 0x7f2c77106b70>

from distributed import Client

client = Client()
client

Client

Client-65712eda-689d-11f0-92a6-6045bd3360ad

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Launch dashboard in JupyterLab

Cluster Info

LocalCluster

9f0529aa

Dashboard: http://127.0.0.1:8787/status	Workers: 4
Total threads: 4	Total memory: 15.62 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-8728befe-f146-4e48-8c4f-587f99531bb9

Comm: tcp://127.0.0.1:36059	Workers: 4
Dashboard: http://127.0.0.1:8787/status	Total threads: 4
Started: Just now	Total memory: 15.62 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:45801	Total threads: 1
Dashboard: http://127.0.0.1:34611/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:46853
Local directory: /tmp/dask-scratch-space/worker-ii6ntouj

Worker: 1

Comm: tcp://127.0.0.1:42611	Total threads: 1
Dashboard: http://127.0.0.1:34009/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:42673
Local directory: /tmp/dask-scratch-space/worker-1tkzpptb

Worker: 2

Comm: tcp://127.0.0.1:34661	Total threads: 1
Dashboard: http://127.0.0.1:42821/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:42545
Local directory: /tmp/dask-scratch-space/worker-qdslrzfl

Worker: 3

Comm: tcp://127.0.0.1:35937	Total threads: 1
Dashboard: http://127.0.0.1:33703/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:42283
Local directory: /tmp/dask-scratch-space/worker-xeoq3v4l

rectilinear grid: the air_temperature example dataset

ds = xr.tutorial.open_dataset("air_temperature", chunks={"time": 20}).isel(
    time=slice(None, 400)
)
ds

<xarray.Dataset> Size: 4MB
Dimensions:  (lat: 25, time: 400, lon: 53)
Coordinates:
  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time     (time) datetime64[ns] 3kB 2013-01-01 ... 2013-04-10T18:00:00
Data variables:
    air      (time, lat, lon) float64 4MB dask.array<chunksize=(20, 25, 53), meta=np.ndarray>
Attributes: (5)

xarray.Dataset

• Dimensions:
  • lat: 25
  • time: 400
  • lon: 53
• Coordinates: (3)
  • lat
    (lat)
    float32
    75.0 72.5 70.0 ... 20.0 17.5 15.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
           45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,
           15. ], dtype=float32)

  • lon
    (lon)
    float32
    200.0 202.5 205.0 ... 327.5 330.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2013-04-10T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2013-04-10T06:00:00.000000000',
           '2013-04-10T12:00:00.000000000', '2013-04-10T18:00:00.000000000'],
          shape=(400,), dtype='datetime64[ns]')

• Data variables: (1)
  • air
    (time, lat, lon)
    float64
    dask.array<chunksize=(20, 25, 53), meta=np.ndarray>

    long_name :

    4xDaily Air temperature at sigma level 995

    units :

    degK

    precision :

    2

    GRIB_id :

    11

    GRIB_name :

    TMP

    var_desc :

    Air temperature

    dataset :

    NMC Reanalysis

    level_desc :

    Surface

    statistic :

    Individual Obs

    parent_stat :

    Other

    actual_range :

    [185.16 322.1 ]

    Array Chunk Bytes 4.04 MiB 207.03 kiB Shape (400, 25, 53) (20, 25, 53) Dask graph 20 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
           45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
           15.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
           225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
           250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
           275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
           300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
           325.0, 327.5, 330.0],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2013-04-08 12:00:00', '2013-04-08 18:00:00',
                   '2013-04-09 00:00:00', '2013-04-09 06:00:00',
                   '2013-04-09 12:00:00', '2013-04-09 18:00:00',
                   '2013-04-10 00:00:00', '2013-04-10 06:00:00',
                   '2013-04-10 12:00:00', '2013-04-10 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=400, freq=None))

• Attributes: (5)

  Conventions :

  COARDS

  title :

  4x daily NMC reanalysis (1948)

  description :

  Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.

  platform :

  Model

  references :

  http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

upscaled = ds.interp(lon=np.linspace(200, 330, 1060), lat=np.linspace(15, 75, 500))
upscaled

<xarray.Dataset> Size: 2GB
Dimensions:  (time: 400, lat: 500, lon: 1060)
Coordinates:
  * time     (time) datetime64[ns] 3kB 2013-01-01 ... 2013-04-10T18:00:00
  * lon      (lon) float64 8kB 200.0 200.1 200.2 200.4 ... 329.8 329.9 330.0
  * lat      (lat) float64 4kB 15.0 15.12 15.24 15.36 ... 74.64 74.76 74.88 75.0
Data variables:
    air      (time, lat, lon) float64 2GB dask.array<chunksize=(20, 500, 1060), meta=np.ndarray>
Attributes: (5)

xarray.Dataset

• Dimensions:
  • time: 400
  • lat: 500
  • lon: 1060
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2013-04-10T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2013-04-10T06:00:00.000000000',
           '2013-04-10T12:00:00.000000000', '2013-04-10T18:00:00.000000000'],
          shape=(400,), dtype='datetime64[ns]')

  • lon
    (lon)
    float64
    200.0 200.1 200.2 ... 329.9 330.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([200.      , 200.122757, 200.245515, ..., 329.754485, 329.877243,
           330.      ], shape=(1060,))

  • lat
    (lat)
    float64
    15.0 15.12 15.24 ... 74.88 75.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([15.      , 15.12024 , 15.240481, ..., 74.759519, 74.87976 , 75.      ],
          shape=(500,))

• Data variables: (1)
  • air
    (time, lat, lon)
    float64
    dask.array<chunksize=(20, 500, 1060), meta=np.ndarray>

    long_name :

    4xDaily Air temperature at sigma level 995

    units :

    degK

    precision :

    2

    GRIB_id :

    11

    GRIB_name :

    TMP

    var_desc :

    Air temperature

    dataset :

    NMC Reanalysis

    level_desc :

    Surface

    statistic :

    Individual Obs

    parent_stat :

    Other

    actual_range :

    [185.16 322.1 ]

    Array Chunk Bytes 1.58 GiB 80.87 MiB Shape (400, 500, 1060) (20, 500, 1060) Dask graph 20 chunks in 19 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2013-04-08 12:00:00', '2013-04-08 18:00:00',
                   '2013-04-09 00:00:00', '2013-04-09 06:00:00',
                   '2013-04-09 12:00:00', '2013-04-09 18:00:00',
                   '2013-04-10 00:00:00', '2013-04-10 06:00:00',
                   '2013-04-10 12:00:00', '2013-04-10 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=400, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([             200.0, 200.12275731822473, 200.24551463644949,
            200.3682719546742, 200.49102927289897,  200.6137865911237,
           200.73654390934846, 200.85930122757318,  200.9820585457979,
           201.10481586402267,
           ...
           328.89518413597733, 329.01794145420206, 329.14069877242684,
            329.2634560906516,  329.3862134088763,  329.5089707271011,
           329.63172804532576, 329.75448536355054,  329.8772426817753,
                        330.0],
          dtype='float64', name='lon', length=1060))

  • lat
    PandasIndex

    PandasIndex(Index([              15.0, 15.120240480961924, 15.240480961923847,
           15.360721442885772, 15.480961923847696,  15.60120240480962,
           15.721442885771543, 15.841683366733466,  15.96192384769539,
           16.082164328657313,
           ...
            73.91783567134269,   74.0380761523046,  74.15831663326654,
            74.27855711422845,  74.39879759519039,   74.5190380761523,
            74.63927855711422,  74.75951903807615,  74.87975951903807,
                         75.0],
          dtype='float64', name='lat', length=500))

• Attributes: (5)

  Conventions :

  COARDS

  title :

  4x daily NMC reanalysis (1948)

  description :

  Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.

  platform :

  Model

  references :

  http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

level = 7
lon = astropy.coordinates.Longitude(
    [200, 225, 250, 275, 300, 330, 330, 300, 275, 250, 225, 200], unit="degree"
)
lat = astropy.coordinates.Latitude(
    [15, 15, 15, 15, 15, 15, 75, 75, 75, 75, 75, 75], unit="degree"
)
cell_ids, _, _ = cdshealpix.nested.polygon_search(lon, lat, depth=level, flat=True)

target_grid = (
    xr.Dataset(coords={"cell_ids": ("cells", cell_ids)})
    .dggs.decode({"grid_name": "healpix", "level": level, "indexing_scheme": "nested"})
    .dggs.assign_latlon_coords()
)
target_grid

<xarray.Dataset> Size: 610kB
Dimensions:    (cells: 25400)
Coordinates:
  * cell_ids   (cells) uint64 203kB 33629 33630 33631 ... 131069 131070 131071
    latitude   (cells) float64 203kB 15.09 15.09 15.4 ... 41.01 41.01 41.41
    longitude  (cells) float64 203kB 229.6 228.9 229.2 ... 270.4 269.6 270.0
Dimensions without coordinates: cells
Data variables:
    *empty*
Indexes:
    cell_ids  HealpixIndex(level=7, indexing_scheme=nested)

xarray.Dataset

• Dimensions:
  • cells: 25400
• Coordinates: (3)
  • cell_ids
    (cells)
    uint64
    33629 33630 33631 ... 131070 131071

    grid_name :

    healpix

    level :

    7

    indexing_scheme :

    nested

    array([ 33629,  33630,  33631, ..., 131069, 131070, 131071],
          shape=(25400,), dtype=uint64)

  • latitude
    (cells)
    float64
    15.09 15.09 15.4 ... 41.01 41.41

    array([15.0947871 , 15.0947871 , 15.4040937 , ..., 41.01449967,
           41.01449967, 41.41118864], shape=(25400,))

  • longitude
    (cells)
    float64
    229.6 228.9 229.2 ... 269.6 270.0

    array([229.5703125, 228.8671875, 229.21875  , ..., 270.3515625,
           269.6484375, 270.       ], shape=(25400,))

• Data variables: (0)
• Indexes: (1)
  • cell_ids
    HealpixIndex(level=7, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2cdc264110>

• Attributes: (0)

%%time
regridder = xesmf.Regridder(
    upscaled, target_grid, method="bilinear", locstream_out=True
)
regridder

CPU times: user 4.07 s, sys: 163 ms, total: 4.23 s
Wall time: 4.11 s

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_500x1060_1x25400.nc 
Reuse pre-computed weights? False 
Input grid shape:           (500, 1060) 
Output grid shape:          (1, 25400) 
Periodic in longitude?      False

regridded = regridder.regrid_dataset(
    upscaled, skipna=True, keep_attrs=True
).dggs.decode()
regridded

<xarray.Dataset> Size: 82MB
Dimensions:    (time: 400, cells: 25400)
Coordinates:
  * time       (time) datetime64[ns] 3kB 2013-01-01 ... 2013-04-10T18:00:00
  * cell_ids   (cells) uint64 203kB 33629 33630 33631 ... 131069 131070 131071
    latitude   (cells) float64 203kB 15.09 15.09 15.4 ... 41.01 41.01 41.41
    longitude  (cells) float64 203kB 229.6 228.9 229.2 ... 270.4 269.6 270.0
Dimensions without coordinates: cells
Data variables:
    air        (time, cells) float64 81MB dask.array<chunksize=(20, 25400), meta=np.ndarray>
Indexes:
    cell_ids  HealpixIndex(level=7, indexing_scheme=nested)
Attributes: (6)

xarray.Dataset

• Dimensions:
  • time: 400
  • cells: 25400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2013-04-10T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2013-04-10T06:00:00.000000000',
           '2013-04-10T12:00:00.000000000', '2013-04-10T18:00:00.000000000'],
          shape=(400,), dtype='datetime64[ns]')

  • cell_ids
    (cells)
    uint64
    33629 33630 33631 ... 131070 131071

    grid_name :

    healpix

    level :

    7

    indexing_scheme :

    nested

    array([ 33629,  33630,  33631, ..., 131069, 131070, 131071],
          shape=(25400,), dtype=uint64)

  • latitude
    (cells)
    float64
    15.09 15.09 15.4 ... 41.01 41.41

    array([15.0947871 , 15.0947871 , 15.4040937 , ..., 41.01449967,
           41.01449967, 41.41118864], shape=(25400,))

  • longitude
    (cells)
    float64
    229.6 228.9 229.2 ... 269.6 270.0

    array([229.5703125, 228.8671875, 229.21875  , ..., 270.3515625,
           269.6484375, 270.       ], shape=(25400,))

• Data variables: (1)
  • air
    (time, cells)
    float64
    dask.array<chunksize=(20, 25400), meta=np.ndarray>

    long_name :

    4xDaily Air temperature at sigma level 995

    units :

    degK

    precision :

    2

    GRIB_id :

    11

    GRIB_name :

    TMP

    var_desc :

    Air temperature

    dataset :

    NMC Reanalysis

    level_desc :

    Surface

    statistic :

    Individual Obs

    parent_stat :

    Other

    actual_range :

    [185.16 322.1 ]

    Array Chunk Bytes 77.51 MiB 3.88 MiB Shape (400, 25400) (20, 25400) Dask graph 20 chunks in 37 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2013-04-08 12:00:00', '2013-04-08 18:00:00',
                   '2013-04-09 00:00:00', '2013-04-09 06:00:00',
                   '2013-04-09 12:00:00', '2013-04-09 18:00:00',
                   '2013-04-10 00:00:00', '2013-04-10 06:00:00',
                   '2013-04-10 12:00:00', '2013-04-10 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=400, freq=None))

  • cell_ids
    HealpixIndex(level=7, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c60628050>

• Attributes: (6)

  Conventions :

  COARDS

  title :

  4x daily NMC reanalysis (1948)

  description :

  Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.

  platform :

  Model

  references :

  http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

  regrid_method :

  bilinear

computed = regridded.compute()
computed

<xarray.Dataset> Size: 82MB
Dimensions:    (time: 400, cells: 25400)
Coordinates:
  * time       (time) datetime64[ns] 3kB 2013-01-01 ... 2013-04-10T18:00:00
  * cell_ids   (cells) uint64 203kB 33629 33630 33631 ... 131069 131070 131071
    latitude   (cells) float64 203kB 15.09 15.09 15.4 ... 41.01 41.01 41.41
    longitude  (cells) float64 203kB 229.6 228.9 229.2 ... 270.4 269.6 270.0
Dimensions without coordinates: cells
Data variables:
    air        (time, cells) float64 81MB 295.0 295.0 294.7 ... 288.4 286.9
Indexes:
    cell_ids  HealpixIndex(level=7, indexing_scheme=nested)
Attributes: (6)

xarray.Dataset

• Dimensions:
  • time: 400
  • cells: 25400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2013-04-10T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2013-04-10T06:00:00.000000000',
           '2013-04-10T12:00:00.000000000', '2013-04-10T18:00:00.000000000'],
          shape=(400,), dtype='datetime64[ns]')

  • cell_ids
    (cells)
    uint64
    33629 33630 33631 ... 131070 131071

    grid_name :

    healpix

    level :

    7

    indexing_scheme :

    nested

    array([ 33629,  33630,  33631, ..., 131069, 131070, 131071],
          shape=(25400,), dtype=uint64)

  • latitude
    (cells)
    float64
    15.09 15.09 15.4 ... 41.01 41.41

    array([15.0947871 , 15.0947871 , 15.4040937 , ..., 41.01449967,
           41.01449967, 41.41118864], shape=(25400,))

  • longitude
    (cells)
    float64
    229.6 228.9 229.2 ... 269.6 270.0

    array([229.5703125, 228.8671875, 229.21875  , ..., 270.3515625,
           269.6484375, 270.       ], shape=(25400,))

• Data variables: (1)
  • air
    (time, cells)
    float64
    295.0 295.0 294.7 ... 288.4 286.9

    long_name :

    4xDaily Air temperature at sigma level 995

    units :

    degK

    precision :

    2

    GRIB_id :

    11

    GRIB_name :

    TMP

    var_desc :

    Air temperature

    dataset :

    NMC Reanalysis

    level_desc :

    Surface

    statistic :

    Individual Obs

    parent_stat :

    Other

    actual_range :

    [185.16 322.1 ]

    array([[294.95254112, 295.01199088, 294.72043525, ..., 269.42659306,
            268.81642873, 268.02092167],
           [295.13852215, 295.1682073 , 294.90939662, ..., 265.06034682,
            264.39699493, 263.33841579],
           [294.78410509, 294.73094822, 294.49654651, ..., 260.42862059,
            259.71801493, 259.10298694],
           ...,
           [295.79930487, 295.94419589, 295.62863288, ..., 286.26439181,
            286.30759894, 284.54424505],
           [295.06245989, 295.1499281 , 294.82046906, ..., 287.07864165,
            286.826193  , 285.20761295],
           [295.41338546, 295.41434604, 295.13278046, ..., 288.84888427,
            288.39709323, 286.897394  ]], shape=(400, 25400))

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2013-04-08 12:00:00', '2013-04-08 18:00:00',
                   '2013-04-09 00:00:00', '2013-04-09 06:00:00',
                   '2013-04-09 12:00:00', '2013-04-09 18:00:00',
                   '2013-04-10 00:00:00', '2013-04-10 06:00:00',
                   '2013-04-10 12:00:00', '2013-04-10 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=400, freq=None))

  • cell_ids
    HealpixIndex(level=7, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c38eb9400>

• Attributes: (6)

  Conventions :

  COARDS

  title :

  4x daily NMC reanalysis (1948)

  description :

  Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.

  platform :

  Model

  references :

  http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

  regrid_method :

  bilinear

computed["air"].dggs.explore(alpha=0.8)

curvilinear grid: the rasm dataset

ds = xr.tutorial.open_dataset("rasm", chunks={"time": 8})
ds

<xarray.Dataset> Size: 17MB
Dimensions:  (time: 36, y: 205, x: 275)
Coordinates:
  * time     (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00
    xc       (y, x) float64 451kB dask.array<chunksize=(205, 275), meta=np.ndarray>
    yc       (y, x) float64 451kB dask.array<chunksize=(205, 275), meta=np.ndarray>
Dimensions without coordinates: y, x
Data variables:
    Tair     (time, y, x) float64 16MB dask.array<chunksize=(8, 205, 275), meta=np.ndarray>
Attributes: (11)

xarray.Dataset

• Dimensions:
  • time: 36
  • y: 205
  • x: 275
• Coordinates: (3)
  • time
    (time)
    object
    1980-09-16 12:00:00 ... 1983-08-...

    long_name :

    time

    type_preferred :

    int

    array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • xc
    (y, x)
    float64
    dask.array<chunksize=(205, 275), meta=np.ndarray>

    long_name :

    longitude of grid cell center

    units :

    degrees_east

    Array Chunk Bytes 440.43 kiB 440.43 kiB Shape (205, 275) (205, 275) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • yc
    (y, x)
    float64
    dask.array<chunksize=(205, 275), meta=np.ndarray>

    long_name :

    latitude of grid cell center

    units :

    degrees_north

    Array Chunk Bytes 440.43 kiB 440.43 kiB Shape (205, 275) (205, 275) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (1)
  • Tair
    (time, y, x)
    float64
    dask.array<chunksize=(8, 205, 275), meta=np.ndarray>

    units :

    C

    long_name :

    Surface air temperature

    type_preferred :

    double

    time_rep :

    instantaneous

    Array Chunk Bytes 15.48 MiB 3.44 MiB Shape (36, 205, 275) (8, 205, 275) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1980-09-16 12:00:00, 1980-10-17 00:00:00, 1980-11-16 12:00:00,
                 1980-12-17 00:00:00, 1981-01-17 00:00:00, 1981-02-15 12:00:00,
                 1981-03-17 00:00:00, 1981-04-16 12:00:00, 1981-05-17 00:00:00,
                 1981-06-16 12:00:00, 1981-07-17 00:00:00, 1981-08-17 00:00:00,
                 1981-09-16 12:00:00, 1981-10-17 00:00:00, 1981-11-16 12:00:00,
                 1981-12-17 00:00:00, 1982-01-17 00:00:00, 1982-02-15 12:00:00,
                 1982-03-17 00:00:00, 1982-04-16 12:00:00, 1982-05-17 00:00:00,
                 1982-06-16 12:00:00, 1982-07-17 00:00:00, 1982-08-17 00:00:00,
                 1982-09-16 12:00:00, 1982-10-17 00:00:00, 1982-11-16 12:00:00,
                 1982-12-17 00:00:00, 1983-01-17 00:00:00, 1983-02-15 12:00:00,
                 1983-03-17 00:00:00, 1983-04-16 12:00:00, 1983-05-17 00:00:00,
                 1983-06-16 12:00:00, 1983-07-17 00:00:00, 1983-08-17 00:00:00],
                dtype='object', length=36, calendar='noleap', freq=None))

• Attributes: (11)

  title :

  /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc

  institution :

  U.W.

  source :

  RACM R1002RBRxaaa01a

  output_frequency :

  daily

  output_mode :

  averaged

  convention :

  CF-1.4

  references :

  Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.

  comment :

  Output from the Variable Infiltration Capacity (VIC) model.

  nco_openmp_thread_number :

  1

  NCO :

  netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  history :

  Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc history deleted for brevity

fig, ax = plt.subplots(subplot_kw={"projection": ccrs.NorthPolarStereo()})
ds["Tair"].isel(time=1).plot.pcolormesh(
    x="xc", y="yc", ax=ax, transform=ccrs.PlateCarree()
)

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7f2c38971430>

level = 8
lon = astropy.coordinates.Longitude(0, unit="degree")
lat = astropy.coordinates.Latitude(90, unit="degree")
cell_ids, _, _ = cdshealpix.nested.cone_search(
    lon, lat, depth=level, flat=True, radius=(90 - 16.5) << astropy.units.degree
)

target_grid = (
    xr.Dataset(coords={"cell_ids": ("cells", cell_ids)})
    .dggs.decode({"grid_name": "healpix", "level": level, "indexing_scheme": "nested"})
    .dggs.assign_latlon_coords()
)
target_grid

<xarray.Dataset> Size: 7MB
Dimensions:    (cells: 283136)
Coordinates:
  * cell_ids   (cells) uint64 2MB 3573 3574 3575 3577 ... 524285 524286 524287
    latitude   (cells) float64 2MB 16.33 16.33 16.49 16.33 ... 41.41 41.41 41.61
    longitude  (cells) float64 2MB 48.34 47.99 48.16 47.64 ... 270.2 269.8 270.0
Dimensions without coordinates: cells
Data variables:
    *empty*
Indexes:
    cell_ids  HealpixIndex(level=8, indexing_scheme=nested)

xarray.Dataset

• Dimensions:
  • cells: 283136
• Coordinates: (3)
  • cell_ids
    (cells)
    uint64
    3573 3574 3575 ... 524286 524287

    grid_name :

    healpix

    level :

    8

    indexing_scheme :

    nested

    array([  3573,   3574,   3575, ..., 524285, 524286, 524287],
          shape=(283136,), dtype=uint64)

  • latitude
    (cells)
    float64
    16.33 16.33 16.49 ... 41.41 41.61

    array([16.33482278, 16.33482278, 16.49036879, ..., 41.41118864,
           41.41118864, 41.61044309], shape=(283136,))

  • longitude
    (cells)
    float64
    48.34 47.99 48.16 ... 269.8 270.0

    array([ 48.33984375,  47.98828125,  48.1640625 , ..., 270.17578125,
           269.82421875, 270.        ], shape=(283136,))

• Data variables: (0)
• Indexes: (1)
  • cell_ids
    HealpixIndex(level=8, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c388b6780>

• Attributes: (0)

%%time
regridder = xesmf.Regridder(ds, target_grid, method="bilinear", locstream_out=True)
regridder

CPU times: user 2.39 s, sys: 45.2 ms, total: 2.44 s
Wall time: 2.39 s

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_205x275_1x283136.nc 
Reuse pre-computed weights? False 
Input grid shape:           (205, 275) 
Output grid shape:          (1, 283136) 
Periodic in longitude?      False

regridded = (
    regridder.regrid_dataset(ds, keep_attrs=True, skipna=True).dggs.decode().compute()
)
regridded

/home/runner/work/regridding-ecosystem-overview/regridding-ecosystem-overview/.pixi/envs/default/lib/python3.12/site-packages/distributed/client.py:3383: UserWarning: Sending large graph of size 15.31 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(

<xarray.Dataset> Size: 88MB
Dimensions:    (time: 36, cells: 283136)
Coordinates:
  * time       (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00
  * cell_ids   (cells) uint64 2MB 3573 3574 3575 3577 ... 524285 524286 524287
    latitude   (cells) float64 2MB 16.33 16.33 16.49 16.33 ... 41.41 41.41 41.61
    longitude  (cells) float64 2MB 48.34 47.99 48.16 47.64 ... 270.2 269.8 270.0
Dimensions without coordinates: cells
Data variables:
    Tair       (time, cells) float64 82MB nan nan nan nan ... nan nan nan nan
Indexes:
    cell_ids  HealpixIndex(level=8, indexing_scheme=nested)
Attributes: (12)

xarray.Dataset

• Dimensions:
  • time: 36
  • cells: 283136
• Coordinates: (4)
  • time
    (time)
    object
    1980-09-16 12:00:00 ... 1983-08-...

    long_name :

    time

    type_preferred :

    int

    array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • cell_ids
    (cells)
    uint64
    3573 3574 3575 ... 524286 524287

    grid_name :

    healpix

    level :

    8

    indexing_scheme :

    nested

    array([  3573,   3574,   3575, ..., 524285, 524286, 524287],
          shape=(283136,), dtype=uint64)

  • latitude
    (cells)
    float64
    16.33 16.33 16.49 ... 41.41 41.61

    array([16.33482278, 16.33482278, 16.49036879, ..., 41.41118864,
           41.41118864, 41.61044309], shape=(283136,))

  • longitude
    (cells)
    float64
    48.34 47.99 48.16 ... 269.8 270.0

    array([ 48.33984375,  47.98828125,  48.1640625 , ..., 270.17578125,
           269.82421875, 270.        ], shape=(283136,))

• Data variables: (1)
  • Tair
    (time, cells)
    float64
    nan nan nan nan ... nan nan nan nan

    units :

    C

    long_name :

    Surface air temperature

    type_preferred :

    double

    time_rep :

    instantaneous

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]], shape=(36, 283136))

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1980-09-16 12:00:00, 1980-10-17 00:00:00, 1980-11-16 12:00:00,
                 1980-12-17 00:00:00, 1981-01-17 00:00:00, 1981-02-15 12:00:00,
                 1981-03-17 00:00:00, 1981-04-16 12:00:00, 1981-05-17 00:00:00,
                 1981-06-16 12:00:00, 1981-07-17 00:00:00, 1981-08-17 00:00:00,
                 1981-09-16 12:00:00, 1981-10-17 00:00:00, 1981-11-16 12:00:00,
                 1981-12-17 00:00:00, 1982-01-17 00:00:00, 1982-02-15 12:00:00,
                 1982-03-17 00:00:00, 1982-04-16 12:00:00, 1982-05-17 00:00:00,
                 1982-06-16 12:00:00, 1982-07-17 00:00:00, 1982-08-17 00:00:00,
                 1982-09-16 12:00:00, 1982-10-17 00:00:00, 1982-11-16 12:00:00,
                 1982-12-17 00:00:00, 1983-01-17 00:00:00, 1983-02-15 12:00:00,
                 1983-03-17 00:00:00, 1983-04-16 12:00:00, 1983-05-17 00:00:00,
                 1983-06-16 12:00:00, 1983-07-17 00:00:00, 1983-08-17 00:00:00],
                dtype='object', length=36, calendar='noleap', freq=None))

  • cell_ids
    HealpixIndex(level=8, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c388ee270>

• Attributes: (12)

  title :

  /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc

  institution :

  U.W.

  source :

  RACM R1002RBRxaaa01a

  output_frequency :

  daily

  output_mode :

  averaged

  convention :

  CF-1.4

  references :

  Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.

  comment :

  Output from the Variable Infiltration Capacity (VIC) model.

  nco_openmp_thread_number :

  1

  NCO :

  netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  history :

  Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc history deleted for brevity

  regrid_method :

  bilinear

regridded["Tair"].dggs.explore(alpha=0.8)

curvilinear grid: the ROMS_example dataset

ds = xr.tutorial.open_dataset(
    "ROMS_example", chunks={"time": 1, "eta_rho": -1, "xi_rho": -1}
)
ds

<xarray.Dataset> Size: 19MB
Dimensions:     (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 371)
Coordinates:
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    lon_rho     (eta_rho, xi_rho) float64 567kB dask.array<chunksize=(191, 371), meta=np.ndarray>
    hc          float64 8B ...
    h           (eta_rho, xi_rho) float64 567kB dask.array<chunksize=(191, 371), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 567kB dask.array<chunksize=(191, 371), meta=np.ndarray>
    Vtransform  int32 4B ...
  * ocean_time  (ocean_time) datetime64[ns] 16B 2001-08-01 2001-08-08
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
Dimensions without coordinates: eta_rho, xi_rho
Data variables:
    salt        (ocean_time, s_rho, eta_rho, xi_rho) float32 17MB dask.array<chunksize=(1, 15, 191, 371), meta=np.ndarray>
    zeta        (ocean_time, eta_rho, xi_rho) float32 567kB dask.array<chunksize=(1, 191, 371), meta=np.ndarray>
Attributes: (34)

xarray.Dataset

• Dimensions:
  • ocean_time: 2
  • s_rho: 30
  • eta_rho: 191
  • xi_rho: 371
• Coordinates: (8)
  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

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

  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 371), meta=np.ndarray>

    long_name :

    longitude of RHO-points

    units :

    degree_east

    standard_name :

    longitude

    field :

    lon_rho, scalar

    Array Chunk Bytes 553.60 kiB 553.60 kiB Shape (191, 371) (191, 371) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 371), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    field :

    bath, scalar

    Array Chunk Bytes 553.60 kiB 553.60 kiB Shape (191, 371) (191, 371) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 371), meta=np.ndarray>

    long_name :

    latitude of RHO-points

    units :

    degree_north

    standard_name :

    latitude

    field :

    lat_rho, scalar

    Array Chunk Bytes 553.60 kiB 553.60 kiB Shape (191, 371) (191, 371) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2001-08-01 2001-08-08

    long_name :

    time since initialization

    field :

    time, scalar, series

    array(['2001-08-01T00:00:00.000000000', '2001-08-08T00:00:00.000000000'],
          dtype='datetime64[ns]')

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

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    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

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

• Data variables: (2)
  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(1, 15, 191, 371), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 16.22 MiB 4.05 MiB Shape (2, 30, 191, 371) (1, 15, 191, 371) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(1, 191, 371), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    Array Chunk Bytes 553.60 kiB 276.80 kiB Shape (2, 191, 371) (1, 191, 371) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2001-08-01', '2001-08-08'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • s_rho
    PandasIndex

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

• Attributes: (34)

  file :

  ../output_20yr_obc/2001/ocean_his_0015.nc

  format :

  netCDF-4/HDF5 file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  TXLA ROMS hindcast run with dyes and oxygen

  rst_file :

  ../output_20yr_obc/2001/ocean_rst.nc

  his_base :

  ../output_20yr_obc/2001/ocean_his

  avg_base :

  ../output_20yr_obc/2001/ocean_avg

  dia_base :

  ../output_20yr_obc/2001/ocean_dia

  sta_file :

  ocean_sta.nc

  grd_file :

  ../inputs/grd/txla2_grd_v4_test_lcut_hglo_wtype.nc

  ini_file :

  ../output_20yr_obc/2000/ocean_rst.nc

  frc_file_01 :

  ../inputs/2001/txla_bulk_ERAI_2001.nc

  frc_file_02 :

  ../inputs/2001/txla_flx_ICOADS_AVHRR_SST_2001.nc, ../inputs/2002/txla_flx_ICOADS_AVHRR_SST_2002.nc

  bry_file_01 :

  ../inputs/2001/txla2_bry_2001_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_bry_2002_glo_ReAna_v4_o2woa.nc

  clm_file_01 :

  ../inputs/2001/txla2_clm_2001_01_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_02_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_03_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_04_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_05_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_06_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_07_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_08_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_09_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_10_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_11_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_12_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_clm_2002_01_glo_ReAna_v4_o2woa.nc

  script_file :

  spos_file :

  /scratch/user/d.kobashi/inputs/stations.in

  NLM_LBC :

  EDGE: WEST SOUTH EAST NORTH zeta: Che Che Che Clo ubar: Shc Shc Shc Clo vbar: Shc Shc Shc Clo u: Rad Rad Rad Clo v: Rad Rad Rad Clo temp: Rad Rad Rad Clo salt: Rad Rad Rad Clo dye_01: Gra Gra Gra Clo dye_02: Rad Rad Rad Clo dye_03: Rad Rad Rad Clo dye_04: Rad Rad Rad Clo tke: Gra Gra Gra Clo

  svn_url :

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

  svn_rev :

  code_dir :

  /scratch/user/d.kobashi/source_code/COAWST/COAWST.r960-dev

  header_dir :

  /home/d.kobashi/TXLA_ROMS_reana/work_20yr_obc

  header_file :

  txla2.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /software/easybuild/software/impi/5.0.1.035-iccifort-2015.0.090/bin64/mpiifort

  compiler_flags :

  -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free

  tiling :

  010x012

  history :

  Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /copano/d1/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc --cnk_dmn ocean_time,4 --cnk_dmn eta_rho,8 --cnk_dmn eta_u,8 --cnk_dmn eta_v,8 --cnk_dmn eta_psi,8 --cnk_dmn xi_rho,16 --cnk_dmn xi_u,16 --cnk_dmn xi_v,16 --cnk_dmn xi_psi,16 --cnk_dmn s_rho,2 --cnk_dmn s_w,2 --output /copano/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc ROMS/TOMS, Version 3.7, Monday - July 18, 2016 - 10:38:26 PM

  ana_file :

  /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_sponge.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_nudgcoef.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_stflux.h

  CPP_options :

  TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOEF, ANA_SPFLUX, ANA_SPONGE, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, CURVGRID, DEFLATE, DIAGNOSTICS_TS, DIAGNOSTICS_UV, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, RADIATION_2D, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES, SPHERICAL, STATIONS, T_PASSIVE, TS_MPDATA, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_VIS2, VAR_RHO_2D, VISC_GRID, WTYPE_GRID

  NCO :

  netCDF Operators version 4.7.6-alpha04 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

fig, ax = plt.subplots(subplot_kw={"projection": ccrs.Miller()}, figsize=(12, 12))
ds["salt"].isel(ocean_time=0, s_rho=1).drop(["hc", "Vtransform"]).plot.pcolormesh(
    x="lon_rho", y="lat_rho", ax=ax, transform=ccrs.PlateCarree()
)

/tmp/ipykernel_4774/3005707708.py:2: DeprecationWarning: dropping variables using `drop` is deprecated; use drop_vars.
  ds["salt"].isel(ocean_time=0, s_rho=1).drop(["hc", "Vtransform"]).plot.pcolormesh(

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7f2c38fe7e30>

min_lon, max_lon = map(float, dask.compute(ds["lon_rho"].min(), ds["lon_rho"].max()))
min_lat, max_lat = map(float, dask.compute(ds["lat_rho"].min(), ds["lat_rho"].max()))

grid_info = xdggs.HealpixInfo.from_dict({"level": 12, "indexing_scheme": "nested"})

lon = astropy.coordinates.Longitude([min_lon, max_lon, max_lon, min_lon], unit="degree")
lat = astropy.coordinates.Latitude([min_lat, min_lat, max_lat, max_lat], unit="degree")
cell_ids, _, _ = cdshealpix.nested.polygon_search(
    lon, lat, depth=grid_info.level, flat=True
)

target_grid = (
    xr.Dataset(coords={"cell_ids": ("cells", cell_ids)})
    .dggs.decode(grid_info)
    .dggs.assign_latlon_coords()
)
target_grid

<xarray.Dataset> Size: 2MB
Dimensions:    (cells: 96332)
Coordinates:
  * cell_ids   (cells) uint64 771kB 133389141 133389142 ... 133964297 133964298
    latitude   (cells) float64 771kB 27.45 27.45 27.46 ... 30.9 30.91 30.91
    longitude  (cells) float64 771kB 271.9 271.9 271.9 ... 269.1 269.1 269.1
Dimensions without coordinates: cells
Data variables:
    *empty*
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)

xarray.Dataset

• Dimensions:
  • cells: 96332
• Coordinates: (3)
  • cell_ids
    (cells)
    uint64
    133389141 133389142 ... 133964298

    grid_name :

    healpix

    level :

    12

    indexing_scheme :

    nested

    array([133389141, 133389142, 133389143, ..., 133964296, 133964297,
           133964298], shape=(96332,), dtype=uint64)

  • latitude
    (cells)
    float64
    27.45 27.45 27.46 ... 30.91 30.91

    array([27.44761922, 27.44761922, 27.45812811, ..., 30.8978541 ,
           30.90872252, 30.90872252], shape=(96332,))

  • longitude
    (cells)
    float64
    271.9 271.9 271.9 ... 269.1 269.1

    array([271.92260742, 271.90063477, 271.91162109, ..., 269.09912109,
           269.11010742, 269.08813477], shape=(96332,))

• Data variables: (0)
• Indexes: (1)
  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c388ed520>

• Attributes: (0)

%%time
regridder = xesmf.Regridder(ds, target_grid, method="bilinear", locstream_out=True)
regridder

CPU times: user 1.25 s, sys: 18.4 ms, total: 1.27 s
Wall time: 1.25 s

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_191x371_1x96332.nc 
Reuse pre-computed weights? False 
Input grid shape:           (191, 371) 
Output grid shape:          (1, 96332) 
Periodic in longitude?      False

regridded = regridder.regrid_dataset(
    ds, keep_attrs=True, skipna=True, na_thres=0.5
).dggs.decode()
regridded

<xarray.Dataset> Size: 50MB
Dimensions:     (ocean_time: 2, s_rho: 30, cells: 96332)
Coordinates:
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    hc          float64 8B ...
    Vtransform  int32 4B ...
  * ocean_time  (ocean_time) datetime64[ns] 16B 2001-08-01 2001-08-08
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * cell_ids    (cells) uint64 771kB 133389141 133389142 ... 133964297 133964298
    latitude    (cells) float64 771kB 27.45 27.45 27.46 ... 30.9 30.91 30.91
    longitude   (cells) float64 771kB 271.9 271.9 271.9 ... 269.1 269.1 269.1
Dimensions without coordinates: cells
Data variables:
    salt        (ocean_time, s_rho, cells) float64 46MB dask.array<chunksize=(1, 15, 96332), meta=np.ndarray>
    zeta        (ocean_time, cells) float64 2MB dask.array<chunksize=(1, 96332), meta=np.ndarray>
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
Attributes: (35)

xarray.Dataset

• Dimensions:
  • ocean_time: 2
  • s_rho: 30
  • cells: 96332
• Coordinates: (8)
  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

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

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2001-08-01 2001-08-08

    long_name :

    time since initialization

    field :

    time, scalar, series

    array(['2001-08-01T00:00:00.000000000', '2001-08-08T00:00:00.000000000'],
          dtype='datetime64[ns]')

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

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    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

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

  • cell_ids
    (cells)
    uint64
    133389141 133389142 ... 133964298

    grid_name :

    healpix

    level :

    12

    indexing_scheme :

    nested

    array([133389141, 133389142, 133389143, ..., 133964296, 133964297,
           133964298], shape=(96332,), dtype=uint64)

  • latitude
    (cells)
    float64
    27.45 27.45 27.46 ... 30.91 30.91

    array([27.44761922, 27.44761922, 27.45812811, ..., 30.8978541 ,
           30.90872252, 30.90872252], shape=(96332,))

  • longitude
    (cells)
    float64
    271.9 271.9 271.9 ... 269.1 269.1

    array([271.92260742, 271.90063477, 271.91162109, ..., 269.09912109,
           269.11010742, 269.08813477], shape=(96332,))

• Data variables: (2)
  • salt
    (ocean_time, s_rho, cells)
    float64
    dask.array<chunksize=(1, 15, 96332), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 44.10 MiB 11.02 MiB Shape (2, 30, 96332) (1, 15, 96332) Dask graph 4 chunks in 21 graph layers Data type float64 numpy.ndarray

  • zeta
    (ocean_time, cells)
    float64
    dask.array<chunksize=(1, 96332), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    Array Chunk Bytes 1.47 MiB 752.59 kiB Shape (2, 96332) (1, 96332) Dask graph 2 chunks in 21 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2001-08-01', '2001-08-08'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • s_rho
    PandasIndex

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

  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c388b4440>

• Attributes: (35)

  file :

  ../output_20yr_obc/2001/ocean_his_0015.nc

  format :

  netCDF-4/HDF5 file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  TXLA ROMS hindcast run with dyes and oxygen

  rst_file :

  ../output_20yr_obc/2001/ocean_rst.nc

  his_base :

  ../output_20yr_obc/2001/ocean_his

  avg_base :

  ../output_20yr_obc/2001/ocean_avg

  dia_base :

  ../output_20yr_obc/2001/ocean_dia

  sta_file :

  ocean_sta.nc

  grd_file :

  ../inputs/grd/txla2_grd_v4_test_lcut_hglo_wtype.nc

  ini_file :

  ../output_20yr_obc/2000/ocean_rst.nc

  frc_file_01 :

  ../inputs/2001/txla_bulk_ERAI_2001.nc

  frc_file_02 :

  ../inputs/2001/txla_flx_ICOADS_AVHRR_SST_2001.nc, ../inputs/2002/txla_flx_ICOADS_AVHRR_SST_2002.nc

  bry_file_01 :

  ../inputs/2001/txla2_bry_2001_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_bry_2002_glo_ReAna_v4_o2woa.nc

  clm_file_01 :

  ../inputs/2001/txla2_clm_2001_01_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_02_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_03_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_04_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_05_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_06_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_07_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_08_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_09_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_10_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_11_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_12_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_clm_2002_01_glo_ReAna_v4_o2woa.nc

  script_file :

  spos_file :

  /scratch/user/d.kobashi/inputs/stations.in

  NLM_LBC :

  EDGE: WEST SOUTH EAST NORTH zeta: Che Che Che Clo ubar: Shc Shc Shc Clo vbar: Shc Shc Shc Clo u: Rad Rad Rad Clo v: Rad Rad Rad Clo temp: Rad Rad Rad Clo salt: Rad Rad Rad Clo dye_01: Gra Gra Gra Clo dye_02: Rad Rad Rad Clo dye_03: Rad Rad Rad Clo dye_04: Rad Rad Rad Clo tke: Gra Gra Gra Clo

  svn_url :

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

  svn_rev :

  code_dir :

  /scratch/user/d.kobashi/source_code/COAWST/COAWST.r960-dev

  header_dir :

  /home/d.kobashi/TXLA_ROMS_reana/work_20yr_obc

  header_file :

  txla2.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /software/easybuild/software/impi/5.0.1.035-iccifort-2015.0.090/bin64/mpiifort

  compiler_flags :

  -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free

  tiling :

  010x012

  history :

  Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /copano/d1/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc --cnk_dmn ocean_time,4 --cnk_dmn eta_rho,8 --cnk_dmn eta_u,8 --cnk_dmn eta_v,8 --cnk_dmn eta_psi,8 --cnk_dmn xi_rho,16 --cnk_dmn xi_u,16 --cnk_dmn xi_v,16 --cnk_dmn xi_psi,16 --cnk_dmn s_rho,2 --cnk_dmn s_w,2 --output /copano/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc ROMS/TOMS, Version 3.7, Monday - July 18, 2016 - 10:38:26 PM

  ana_file :

  /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_sponge.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_nudgcoef.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_stflux.h

  CPP_options :

  TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOEF, ANA_SPFLUX, ANA_SPONGE, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, CURVGRID, DEFLATE, DIAGNOSTICS_TS, DIAGNOSTICS_UV, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, RADIATION_2D, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES, SPHERICAL, STATIONS, T_PASSIVE, TS_MPDATA, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_VIS2, VAR_RHO_2D, VISC_GRID, WTYPE_GRID

  NCO :

  netCDF Operators version 4.7.6-alpha04 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  regrid_method :

  bilinear

computed = regridded.compute().where(lambda ds: ds.notnull(), drop=True)
computed

<xarray.Dataset> Size: 24MB
Dimensions:     (ocean_time: 2, s_rho: 30, cells: 45625)
Coordinates:
    Cs_r        (s_rho) float64 240B -0.933 -0.8092 ... -0.0005206 -5.758e-05
    hc          float64 8B 20.0
    Vtransform  int32 4B 2
  * ocean_time  (ocean_time) datetime64[ns] 16B 2001-08-01 2001-08-08
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * cell_ids    (cells) uint64 365kB 133406431 133406443 ... 133798305 133798306
    latitude    (cells) float64 365kB 27.73 27.71 27.71 ... 29.97 29.98 29.98
    longitude   (cells) float64 365kB 269.2 269.1 269.1 ... 266.2 266.2 266.2
Dimensions without coordinates: cells
Data variables:
    salt        (ocean_time, s_rho, cells) float64 22MB 34.94 34.93 ... 12.12
    zeta        (ocean_time, cells) float64 730kB -0.1275 -0.1262 ... -0.01867
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
Attributes: (35)

xarray.Dataset

• Dimensions:
  • ocean_time: 2
  • s_rho: 30
  • cells: 45625
• Coordinates: (8)
  • Cs_r
    (s_rho)
    float64
    -0.933 -0.8092 ... -5.758e-05

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

    array([-9.33010396e-01, -8.09234736e-01, -6.98779853e-01, -6.01008926e-01,
           -5.15058562e-01, -4.39938913e-01, -3.74609181e-01, -3.18031817e-01,
           -2.69209327e-01, -2.27207488e-01, -1.91168387e-01, -1.60316097e-01,
           -1.33957253e-01, -1.11478268e-01, -9.23404709e-02, -7.60741092e-02,
           -6.22718662e-02, -5.05823390e-02, -4.07037635e-02, -3.23781605e-02,
           -2.53860004e-02, -1.95414261e-02, -1.46880431e-02, -1.06952600e-02,
           -7.45515186e-03, -4.87981407e-03, -2.89916971e-03, -1.45919898e-03,
           -5.20560097e-04, -5.75774004e-05])

  • hc
    ()
    float64
    20.0

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    array(20.)

  • Vtransform
    ()
    int32
    2

    long_name :

    vertical terrain-following transformation equation

    array(2, dtype=int32)

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2001-08-01 2001-08-08

    long_name :

    time since initialization

    field :

    time, scalar, series

    array(['2001-08-01T00:00:00.000000000', '2001-08-08T00:00:00.000000000'],
          dtype='datetime64[ns]')

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

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    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

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

  • cell_ids
    (cells)
    uint64
    133406431 133406443 ... 133798306

    grid_name :

    healpix

    level :

    12

    indexing_scheme :

    nested

    array([133406431, 133406443, 133406444, ..., 133798304, 133798305,
           133798306], shape=(45625,), dtype=uint64)

  • latitude
    (cells)
    float64
    27.73 27.71 27.71 ... 29.98 29.98

    array([27.73171266, 27.71064339, 27.71064339, ..., 29.96770083,
           29.97846605, 29.97846605], shape=(45625,))

  • longitude
    (cells)
    float64
    269.2 269.1 269.1 ... 266.2 266.2

    array([269.16503906, 269.05517578, 269.07714844, ..., 266.17675781,
           266.18774414, 266.16577148], shape=(45625,))

• Data variables: (2)
  • salt
    (ocean_time, s_rho, cells)
    float64
    34.94 34.93 34.93 ... 12.4 12.12

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    array([[[34.93819427, 34.93248367, 34.93167877, ..., 14.3999052 ,
             13.17514149, 14.85388128],
            [34.94499969, 34.94665909, 34.93588638, ..., 13.90372467,
             12.93943634, 14.1016134 ],
            [34.97452545, 34.98368454, 34.97496414, ..., 12.94494152,
             11.9046888 , 12.88325033],
            ...,
            [36.16990662, 36.16558075, 36.16846085, ...,  8.70376587,
              8.84046118,  8.64335741],
            [36.17995071, 36.1823349 , 36.18006897, ...,  8.70324612,
              8.84048465,  8.64300751],
            [36.18956375, 36.1890831 , 36.18884659, ...,  8.7029953 ,
              8.84067131,  8.6428166 ]],
    
           [[34.93996811, 34.93884659, 34.93603897, ..., 16.65669823,
             16.4733305 , 16.63393893],
            [34.94958496, 34.95692444, 34.95136261, ..., 16.61934853,
             16.44516058, 16.61686998],
            [34.97234344, 34.97727966, 34.97235107, ..., 16.50738144,
             16.21454865, 16.47602182],
            ...,
            [36.09970474, 36.12430191, 36.12123108, ..., 11.04564953,
             12.40360209, 12.1305511 ],
            [36.09973907, 36.12527466, 36.12220764, ..., 11.03445148,
             12.40020624, 12.12720977],
            [36.09983444, 36.12537384, 36.12233353, ..., 11.02725124,
             12.39729505, 12.12435452]]], shape=(2, 30, 45625))

  • zeta
    (ocean_time, cells)
    float64
    -0.1275 -0.1262 ... -0.01867

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    array([[-0.12750664, -0.12618086, -0.12627368, ..., -0.18589561,
            -0.18542562, -0.18531978],
           [-0.16346394, -0.15337852, -0.15534961, ..., -0.01857305,
            -0.01813341, -0.01867156]], shape=(2, 45625))

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2001-08-01', '2001-08-08'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • s_rho
    PandasIndex

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

  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7f2c38e1e060>

• Attributes: (35)

  file :

  ../output_20yr_obc/2001/ocean_his_0015.nc

  format :

  netCDF-4/HDF5 file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  TXLA ROMS hindcast run with dyes and oxygen

  rst_file :

  ../output_20yr_obc/2001/ocean_rst.nc

  his_base :

  ../output_20yr_obc/2001/ocean_his

  avg_base :

  ../output_20yr_obc/2001/ocean_avg

  dia_base :

  ../output_20yr_obc/2001/ocean_dia

  sta_file :

  ocean_sta.nc

  grd_file :

  ../inputs/grd/txla2_grd_v4_test_lcut_hglo_wtype.nc

  ini_file :

  ../output_20yr_obc/2000/ocean_rst.nc

  frc_file_01 :

  ../inputs/2001/txla_bulk_ERAI_2001.nc

  frc_file_02 :

  ../inputs/2001/txla_flx_ICOADS_AVHRR_SST_2001.nc, ../inputs/2002/txla_flx_ICOADS_AVHRR_SST_2002.nc

  bry_file_01 :

  ../inputs/2001/txla2_bry_2001_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_bry_2002_glo_ReAna_v4_o2woa.nc

  clm_file_01 :

  ../inputs/2001/txla2_clm_2001_01_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_02_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_03_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_04_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_05_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_06_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_07_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_08_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_09_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_10_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_11_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_12_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_clm_2002_01_glo_ReAna_v4_o2woa.nc

  script_file :

  spos_file :

  /scratch/user/d.kobashi/inputs/stations.in

  NLM_LBC :

  EDGE: WEST SOUTH EAST NORTH zeta: Che Che Che Clo ubar: Shc Shc Shc Clo vbar: Shc Shc Shc Clo u: Rad Rad Rad Clo v: Rad Rad Rad Clo temp: Rad Rad Rad Clo salt: Rad Rad Rad Clo dye_01: Gra Gra Gra Clo dye_02: Rad Rad Rad Clo dye_03: Rad Rad Rad Clo dye_04: Rad Rad Rad Clo tke: Gra Gra Gra Clo

  svn_url :

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

  svn_rev :

  code_dir :

  /scratch/user/d.kobashi/source_code/COAWST/COAWST.r960-dev

  header_dir :

  /home/d.kobashi/TXLA_ROMS_reana/work_20yr_obc

  header_file :

  txla2.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /software/easybuild/software/impi/5.0.1.035-iccifort-2015.0.090/bin64/mpiifort

  compiler_flags :

  -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free

  tiling :

  010x012

  history :

  Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /copano/d1/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc --cnk_dmn ocean_time,4 --cnk_dmn eta_rho,8 --cnk_dmn eta_u,8 --cnk_dmn eta_v,8 --cnk_dmn eta_psi,8 --cnk_dmn xi_rho,16 --cnk_dmn xi_u,16 --cnk_dmn xi_v,16 --cnk_dmn xi_psi,16 --cnk_dmn s_rho,2 --cnk_dmn s_w,2 --output /copano/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc ROMS/TOMS, Version 3.7, Monday - July 18, 2016 - 10:38:26 PM

  ana_file :

  /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_sponge.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_nudgcoef.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_stflux.h

  CPP_options :

  TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOEF, ANA_SPFLUX, ANA_SPONGE, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, CURVGRID, DEFLATE, DIAGNOSTICS_TS, DIAGNOSTICS_UV, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, RADIATION_2D, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES, SPHERICAL, STATIONS, T_PASSIVE, TS_MPDATA, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_VIS2, VAR_RHO_2D, VISC_GRID, WTYPE_GRID

  NCO :

  netCDF Operators version 4.7.6-alpha04 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  regrid_method :

  bilinear

computed["salt"].dggs.explore(alpha=0.8)