Regridding to H3 using XESMF

import cartopy.crs as ccrs
import cf_xarray  # noqa: F401
import dask
import h3ronpy
import matplotlib.pyplot as plt
import numpy as np
import shapely
import xarray as xr
import xdggs  # noqa: F401
import xesmf

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

<xarray.core.options.set_options at 0x7f7c0c7c0ec0>

from distributed import Client

client = Client()
client

Client

Client-543f8752-689d-11f0-9231-6045bd3360ad

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

Launch dashboard in JupyterLab

Cluster Info

LocalCluster

d7ade6ae

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-ede82dab-3935-4627-959c-a69cfef45f06

Comm: tcp://127.0.0.1:40515	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://127.0.0.1:32983	Total threads: 1
Dashboard: http://127.0.0.1:45777/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:39061
Local directory: /tmp/dask-scratch-space/worker-w8kt_aip

Worker: 1

Comm: tcp://127.0.0.1:39907	Total threads: 1
Dashboard: http://127.0.0.1:38465/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:45877
Local directory: /tmp/dask-scratch-space/worker-npuq01dt

Worker: 2

Comm: tcp://127.0.0.1:34435	Total threads: 1
Dashboard: http://127.0.0.1:39979/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:46833
Local directory: /tmp/dask-scratch-space/worker-d2xmauek

Worker: 3

Comm: tcp://127.0.0.1:42101	Total threads: 1
Dashboard: http://127.0.0.1:34635/status	Memory: 3.91 GiB
Nanny: tcp://127.0.0.1:34917
Local directory: /tmp/dask-scratch-space/worker-t0eft2sh

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)
).assign_coords(lon=lambda ds: (ds["lon"] + 180) % 360 - 180)
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
  * lat      (lat) float64 4kB 15.0 15.12 15.24 15.36 ... 74.64 74.76 74.88 75.0
  * lon      (lon) float64 8kB -160.0 -159.9 -159.8 ... -30.25 -30.12 -30.0
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]')

  • 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,))

  • lon
    (lon)
    float64
    -160.0 -159.9 ... -30.12 -30.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([-160.      , -159.877243, -159.754485, ...,  -30.245515,  -30.122757,
            -30.      ], shape=(1060,))

• 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))

  • 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))

  • lon
    PandasIndex

    PandasIndex(Index([             -160.0, -159.87724268177527, -159.75448536355054,
           -159.63172804532576, -159.50897072710103,  -159.3862134088763,
           -159.26345609065152, -159.14069877242684, -159.01794145420206,
           -158.89518413597733,
           ...
            -31.10481586402267,  -30.98205854579794, -30.859301227573155,
           -30.736543909348427,   -30.6137865911237, -30.491029272898913,
           -30.368271954674242, -30.245514636449457,  -30.12275731822473,
                         -30.0],
          dtype='float64', name='lon', length=1060))

• 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 = 4
geom = shapely.box(
    float(upscaled["lon"].min()),
    float(upscaled["lat"].min()),
    float(upscaled["lon"].max()),
    float(upscaled["lat"].max()),
)
cell_ids = np.asarray(
    h3ronpy.vector.geometry_to_cells(
        geom, resolution=level, containment_mode=h3ronpy.ContainmentMode.Covers
    )
)

target_grid = (
    xr.Dataset(coords={"cell_ids": ("cells", cell_ids)})
    .dggs.decode({"grid_name": "h3", "level": level})
    .dggs.assign_latlon_coords()
)
target_grid

<xarray.Dataset> Size: 901kB
Dimensions:    (cells: 37555)
Coordinates:
  * cell_ids   (cells) uint64 300kB 594512207790735359 ... 596425581361364991
    latitude   (cells) float64 300kB 75.06 75.21 74.94 ... 15.11 14.84 15.18
    longitude  (cells) float64 300kB -109.7 -112.3 -111.2 ... -113.6 -113.9
Dimensions without coordinates: cells
Data variables:
    *empty*
Indexes:
    cell_ids  H3Index(level=4)

xarray.Dataset

• Dimensions:
  • cells: 37555
• Coordinates: (3)
  • cell_ids
    (cells)
    uint64
    594512207790735359 ... 596425581...

    grid_name :

    h3

    level :

    4

    array([594512207790735359, 594512216380669951, 594512224970604543, ...,
           596425564181495807, 596425572771430399, 596425581361364991],
          shape=(37555,), dtype=uint64)

  • latitude
    (cells)
    float64
    75.06 75.21 74.94 ... 14.84 15.18

    array([75.0644922 , 75.20959863, 74.93707292, ..., 15.10988911,
           14.84235472, 15.17852559], shape=(37555,))

  • longitude
    (cells)
    float64
    -109.7 -112.3 ... -113.6 -113.9

    array([-109.67853312, -112.33444315, -111.19846397, ..., -114.29787847,
           -113.5622709 , -113.85228122], shape=(37555,))

• Data variables: (0)
• Indexes: (1)
  • cell_ids
    H3Index(level=4)

    <xdggs.h3.H3Index object at 0x7f7ba82580e0>

• Attributes: (0)

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

CPU times: user 4.44 s, sys: 168 ms, total: 4.6 s
Wall time: 4.47 s

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_500x1060_1x37555.nc 
Reuse pre-computed weights? False 
Input grid shape:           (500, 1060) 
Output grid shape:          (1, 37555) 
Periodic in longitude?      False

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

<xarray.Dataset> Size: 121MB
Dimensions:    (time: 400, cells: 37555)
Coordinates:
  * time       (time) datetime64[ns] 3kB 2013-01-01 ... 2013-04-10T18:00:00
  * cell_ids   (cells) uint64 300kB 594512207790735359 ... 596425581361364991
    latitude   (cells) float64 300kB 75.06 75.21 74.94 ... 15.11 14.84 15.18
    longitude  (cells) float64 300kB -109.7 -112.3 -111.2 ... -113.6 -113.9
Dimensions without coordinates: cells
Data variables:
    air        (time, cells) float64 120MB dask.array<chunksize=(20, 37555), meta=np.ndarray>
Indexes:
    cell_ids  H3Index(level=4)
Attributes: (6)

xarray.Dataset

• Dimensions:
  • time: 400
  • cells: 37555
• 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
    594512207790735359 ... 596425581...

    grid_name :

    h3

    level :

    4

    array([594512207790735359, 594512216380669951, 594512224970604543, ...,
           596425564181495807, 596425572771430399, 596425581361364991],
          shape=(37555,), dtype=uint64)

  • latitude
    (cells)
    float64
    75.06 75.21 74.94 ... 14.84 15.18

    array([75.0644922 , 75.20959863, 74.93707292, ..., 15.10988911,
           14.84235472, 15.17852559], shape=(37555,))

  • longitude
    (cells)
    float64
    -109.7 -112.3 ... -113.6 -113.9

    array([-109.67853312, -112.33444315, -111.19846397, ..., -114.29787847,
           -113.5622709 , -113.85228122], shape=(37555,))

• Data variables: (1)
  • air
    (time, cells)
    float64
    dask.array<chunksize=(20, 37555), 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 114.61 MiB 5.73 MiB Shape (400, 37555) (20, 37555) 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
    H3Index(level=4)

    <xdggs.h3.H3Index object at 0x7f7ba8258170>

• 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: 121MB
Dimensions:    (time: 400, cells: 37555)
Coordinates:
  * time       (time) datetime64[ns] 3kB 2013-01-01 ... 2013-04-10T18:00:00
  * cell_ids   (cells) uint64 300kB 594512207790735359 ... 596425581361364991
    latitude   (cells) float64 300kB 75.06 75.21 74.94 ... 15.11 14.84 15.18
    longitude  (cells) float64 300kB -109.7 -112.3 -111.2 ... -113.6 -113.9
Dimensions without coordinates: cells
Data variables:
    air        (time, cells) float64 120MB nan nan 245.6 ... 298.0 nan 298.0
Indexes:
    cell_ids  H3Index(level=4)
Attributes: (6)

xarray.Dataset

• Dimensions:
  • time: 400
  • cells: 37555
• 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
    594512207790735359 ... 596425581...

    grid_name :

    h3

    level :

    4

    array([594512207790735359, 594512216380669951, 594512224970604543, ...,
           596425564181495807, 596425572771430399, 596425581361364991],
          shape=(37555,), dtype=uint64)

  • latitude
    (cells)
    float64
    75.06 75.21 74.94 ... 14.84 15.18

    array([75.0644922 , 75.20959863, 74.93707292, ..., 15.10988911,
           14.84235472, 15.17852559], shape=(37555,))

  • longitude
    (cells)
    float64
    -109.7 -112.3 ... -113.6 -113.9

    array([-109.67853312, -112.33444315, -111.19846397, ..., -114.29787847,
           -113.5622709 , -113.85228122], shape=(37555,))

• Data variables: (1)
  • air
    (time, cells)
    float64
    nan nan 245.6 ... 298.0 nan 298.0

    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([[         nan,          nan, 245.57658828, ..., 296.84033955,
                     nan, 296.87954889],
           [         nan,          nan, 243.17151247, ..., 297.12049002,
                     nan, 297.20535341],
           [         nan,          nan, 241.61687767, ..., 296.56255563,
                     nan, 296.67524918],
           ...,
           [         nan,          nan, 264.61540619, ..., 298.48607138,
                     nan, 298.48314863],
           [         nan,          nan, 266.76652842, ..., 297.68060807,
                     nan, 297.68178197],
           [         nan,          nan, 268.65590028, ..., 298.01919664,
                     nan, 298.04503574]], shape=(400, 37555))

• 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
    H3Index(level=4)

    <xdggs.h3.H3Index object at 0x7f7ba82914c0>

• 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 0x7f7b8c110bc0>

level = 4
geom = shapely.box(0, 16.5, 360, 90)
cell_ids = np.asarray(
    h3ronpy.vector.geometry_to_cells(
        geom, resolution=level, containment_mode=h3ronpy.ContainmentMode.Covers
    )
)
target_grid = (
    xr.Dataset(coords={"cell_ids": ("cells", cell_ids)})
    .dggs.decode({"grid_name": "h3", "level": level})
    .dggs.assign_latlon_coords()
)
target_grid

<xarray.Dataset> Size: 2MB
Dimensions:    (cells: 103217)
Coordinates:
  * cell_ids   (cells) uint64 826kB 594475159402840063 ... 596505244414771199
    latitude   (cells) float64 826kB 79.24 79.63 79.12 ... 16.29 16.54 16.46
    longitude  (cells) float64 826kB 38.02 37.33 40.12 ... 139.4 140.1 140.6
Dimensions without coordinates: cells
Data variables:
    *empty*
Indexes:
    cell_ids  H3Index(level=4)

xarray.Dataset

• Dimensions:
  • cells: 103217
• Coordinates: (3)
  • cell_ids
    (cells)
    uint64
    594475159402840063 ... 596505244...

    grid_name :

    h3

    level :

    4

    array([594475159402840063, 594475167992774655, 594475176582709247, ...,
           596505175695294463, 596505235824836607, 596505244414771199],
          shape=(103217,), dtype=uint64)

  • latitude
    (cells)
    float64
    79.24 79.63 79.12 ... 16.54 16.46

    array([79.24239851, 79.63352592, 79.12378871, ..., 16.28740624,
           16.53696235, 16.45928488], shape=(103217,))

  • longitude
    (cells)
    float64
    38.02 37.33 40.12 ... 140.1 140.6

    array([ 38.02340701,  37.33333266,  40.12073979, ..., 139.38371854,
           140.12426248, 140.57229677], shape=(103217,))

• Data variables: (0)
• Indexes: (1)
  • cell_ids
    H3Index(level=4)

    <xdggs.h3.H3Index object at 0x7f7b8c5e8230>

• Attributes: (0)

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

CPU times: user 1.23 s, sys: 13.5 ms, total: 1.24 s
Wall time: 1.23 s

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_205x275_1x103217.nc 
Reuse pre-computed weights? False 
Input grid shape:           (205, 275) 
Output grid shape:          (1, 103217) 
Periodic in longitude?      False

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

<xarray.Dataset> Size: 32MB
Dimensions:    (time: 36, cells: 103217)
Coordinates:
  * time       (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00
  * cell_ids   (cells) uint64 826kB 594475159402840063 ... 596505244414771199
    latitude   (cells) float64 826kB 79.24 79.63 79.12 ... 16.29 16.54 16.46
    longitude  (cells) float64 826kB 38.02 37.33 40.12 ... 139.4 140.1 140.6
Dimensions without coordinates: cells
Data variables:
    Tair       (time, cells) float64 30MB nan -2.794 nan nan ... nan nan nan nan
Indexes:
    cell_ids  H3Index(level=4)
Attributes: (12)

xarray.Dataset

• Dimensions:
  • time: 36
  • cells: 103217
• 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
    594475159402840063 ... 596505244...

    grid_name :

    h3

    level :

    4

    array([594475159402840063, 594475167992774655, 594475176582709247, ...,
           596505175695294463, 596505235824836607, 596505244414771199],
          shape=(103217,), dtype=uint64)

  • latitude
    (cells)
    float64
    79.24 79.63 79.12 ... 16.54 16.46

    array([79.24239851, 79.63352592, 79.12378871, ..., 16.28740624,
           16.53696235, 16.45928488], shape=(103217,))

  • longitude
    (cells)
    float64
    38.02 37.33 40.12 ... 140.1 140.6

    array([ 38.02340701,  37.33333266,  40.12073979, ..., 139.38371854,
           140.12426248, 140.57229677], shape=(103217,))

• Data variables: (1)
  • Tair
    (time, cells)
    float64
    nan -2.794 nan nan ... nan nan nan

    units :

    C

    long_name :

    Surface air temperature

    type_preferred :

    double

    time_rep :

    instantaneous

    array([[         nan,  -2.79384336,          nan, ...,          nan,
                     nan,          nan],
           [         nan, -13.14467537,          nan, ...,          nan,
                     nan,          nan],
           [         nan, -16.77208461,          nan, ...,          nan,
                     nan,          nan],
           ...,
           [         nan,  -1.85748251,          nan, ...,          nan,
                     nan,          nan],
           [         nan,  -0.12271452,          nan, ...,          nan,
                     nan,          nan],
           [         nan,  -2.10853356,          nan, ...,          nan,
                     nan,          nan]], shape=(36, 103217))

• 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
    H3Index(level=4)

    <xdggs.h3.H3Index object at 0x7f7b8c1c0d40>

• 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})
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, 96, 186), 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, 96, 186), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 16.22 MiB 1.02 MiB Shape (2, 30, 191, 371) (1, 15, 96, 186) Dask graph 16 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_4657/3005707708.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 0x7f7b8c21af60>

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()))

level = 6
geom = shapely.box(min_lon, min_lat, max_lon, max_lat)
cell_ids = np.asarray(
    h3ronpy.vector.geometry_to_cells(
        geom, resolution=level, containment_mode=h3ronpy.ContainmentMode.Covers
    )
)
target_grid = (
    xr.Dataset(coords={"cell_ids": ("cells", cell_ids)})
    .dggs.decode({"grid_name": "h3", "level": level})
    .dggs.assign_latlon_coords()
)
target_grid

<xarray.Dataset> Size: 155kB
Dimensions:    (cells: 6471)
Coordinates:
  * cell_ids   (cells) uint64 52kB 604679243367972863 ... 604686774727344127
    latitude   (cells) float64 52kB 28.78 29.25 29.15 29.2 ... 27.51 27.57 27.51
    longitude  (cells) float64 52kB -87.72 -87.74 -87.74 ... -89.37 -89.28
Dimensions without coordinates: cells
Data variables:
    *empty*
Indexes:
    cell_ids  H3Index(level=6)

xarray.Dataset

• Dimensions:
  • cells: 6471
• Coordinates: (3)
  • cell_ids
    (cells)
    uint64
    604679243367972863 ... 604686774...

    grid_name :

    h3

    level :

    6

    array([604679243367972863, 604679246052327423, 604679246454980607, ...,
           604686774458908671, 604686774593126399, 604686774727344127],
          shape=(6471,), dtype=uint64)

  • latitude
    (cells)
    float64
    28.78 29.25 29.15 ... 27.57 27.51

    array([28.7839127 , 29.2548046 , 29.150453  , ..., 27.51267085,
           27.5659643 , 27.51156805], shape=(6471,))

  • longitude
    (cells)
    float64
    -87.72 -87.74 ... -89.37 -89.28

    array([-87.7154096 , -87.7371879 , -87.73961304, ..., -89.34128013,
           -89.37364563, -89.2756367 ], shape=(6471,))

• Data variables: (0)
• Indexes: (1)
  • cell_ids
    H3Index(level=6)

    <xdggs.h3.H3Index object at 0x7f7b8c6f5760>

• Attributes: (0)

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

CPU times: user 513 ms, sys: 8.77 ms, total: 522 ms
Wall time: 537 ms

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_191x371_1x6471.nc 
Reuse pre-computed weights? False 
Input grid shape:           (191, 371) 
Output grid shape:          (1, 6471) 
Periodic in longitude?      False

regridded = (
    regridder.regrid_dataset(ds, keep_attrs=True, skipna=True, na_thres=0.5)
    .dggs.decode()
    .compute()
    .where(lambda ds: ds.notnull(), drop=True)
)
regridded

/home/runner/work/regridding-ecosystem-overview/regridding-ecosystem-overview/.pixi/envs/default/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py:450: PerformanceWarning: Regridding is increasing the number of chunks by a factor of 35.0, you might want to specify sizes in `output_chunks` in the regridder call. Default behaviour is to preserve the chunk sizes from the input (96, 186).
  result_vars[name] = func(*variable_args)

<xarray.Dataset> Size: 2MB
Dimensions:     (ocean_time: 2, s_rho: 30, cells: 2980)
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 24kB 604679246991851519 ... 604686709094875135
    latitude    (cells) float64 24kB 29.31 29.36 29.36 ... 27.74 27.64 27.69
    longitude   (cells) float64 24kB -87.83 -87.87 -87.8 ... -91.33 -91.36
Dimensions without coordinates: cells
Data variables:
    salt        (ocean_time, s_rho, cells) float64 1MB 35.39 35.72 ... 36.16
    zeta        (ocean_time, cells) float64 48kB -0.2887 -0.2773 ... -0.1436
Indexes:
    cell_ids  H3Index(level=6)
Attributes: (35)

xarray.Dataset

• Dimensions:
  • ocean_time: 2
  • s_rho: 30
  • cells: 2980
• 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
    604679246991851519 ... 604686709...

    grid_name :

    h3

    level :

    6

    array([604679246991851519, 604679247126069247, 604679247394504703, ...,
           604686708826439679, 604686708960657407, 604686709094875135],
          shape=(2980,), dtype=uint64)

  • latitude
    (cells)
    float64
    29.31 29.36 29.36 ... 27.64 27.69

    array([29.31056851, 29.36394825, 29.36155516, ..., 27.7440886 ,
           27.63789957, 27.69120851], shape=(2980,))

  • longitude
    (cells)
    float64
    -87.83 -87.87 ... -91.33 -91.36

    array([-87.83445066, -87.86614284, -87.8004272 , ..., -91.2643388 ,
           -91.33049672, -91.36439342], shape=(2980,))

• Data variables: (2)
  • salt
    (ocean_time, s_rho, cells)
    float64
    35.39 35.72 35.62 ... 36.15 36.16

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    array([[[35.38721085, 35.72259903, 35.6201973 , ..., 34.99189758,
             34.91461182, 34.93117142],
            [35.5358963 , 35.90664673, 35.76451874, ..., 35.02586365,
             34.93194962, 34.96170044],
            [35.67207336, 36.02700043, 35.91119003, ..., 35.09923172,
             34.95978928, 34.98789978],
            ...,
            [35.01153183, 34.99617767, 35.01000214, ..., 36.03802872,
             36.17417145, 36.179039  ],
            [35.01361847, 34.98945999, 35.0100975 , ..., 36.04302216,
             36.18837738, 36.18867493],
            [35.01735306, 34.98985291, 35.01085663, ..., 36.04439545,
             36.19015503, 36.19020081]],
    
           [[35.56031036, 35.8293457 , 35.75767136, ..., 35.00284195,
             34.91387558, 34.93244171],
            [35.68213654, 35.84500885, 35.81285095, ..., 35.03821564,
             34.92707443, 34.9587822 ],
            [35.77061081, 35.89471817, 35.86262512, ..., 35.09191895,
             34.94727325, 34.97735596],
            ...,
            [35.43530655, 35.35011673, 35.39170456, ..., 36.15488052,
             36.14595413, 36.15541458],
            [35.43549728, 35.35037231, 35.39204788, ..., 36.15486908,
             36.14661026, 36.15547562],
            [35.43585587, 35.35077286, 35.39202499, ..., 36.15483475,
             36.14680481, 36.15545654]]], shape=(2, 30, 2980))

  • zeta
    (ocean_time, cells)
    float64
    -0.2887 -0.2773 ... -0.1395 -0.1436

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    array([[-0.28873718, -0.27725863, -0.2848717 , ..., -0.13890328,
            -0.12944168, -0.13611248],
           [-0.29570997, -0.26661184, -0.27590039, ..., -0.14654863,
            -0.13948096, -0.14360252]], shape=(2, 2980))

• 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
    H3Index(level=6)

    <xdggs.h3.H3Index object at 0x7f7b8c75f410>

• 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

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