Use a shapefile as landmask

import numpy as np
from opendrift.readers import reader_netCDF_CF_generic
from opendrift.readers import reader_shape
from opendrift.models.oceandrift import OceanDrift

o = OceanDrift(loglevel=20)  # Set loglevel to 0 for debug information

# This example works better using hourly input from Thredds than the daily data from test folder
reader_topaz = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml')

15:18:45 INFO    opendrift:568: OpenDriftSimulation initialised (version 1.14.3 / v1.14.3-4-ga7b402c)
15:18:45 INFO    opendrift.readers:61: Opening file with xr.open_dataset
15:18:49 INFO    opendrift.readers.reader_netCDF_CF_generic:332: Detected dimensions: {'x': 'x', 'y': 'y', 'time': 'time'}

Use shapes from Cartopy as landmask. These shapefiles are less acurate than those provided by the GSHHG dataset (available though the reader_global_landmask reader).

import cartopy.io.shapereader as shpreader
shpfilename = shpreader.natural_earth(resolution='110m',
                                    category='cultural',
                                    name='admin_0_countries')
reader_natural = reader_shape.Reader.from_shpfiles(shpfilename)

o.add_reader([reader_natural, reader_topaz])
o.set_config('general:use_auto_landmask', False)  # Disabling the automatic GSHHG landmask
o.set_config('general:coastline_action', 'stranding')

15:18:49 INFO    opendrift.readers.reader_shape:99: Pre-processing 177 geometries

Seeding elements on a grid

lons = np.linspace(12, 14.7, 30)
lats = np.linspace(67.5, 68.5, 30)
lons, lats = np.meshgrid(lons, lats)
lon = lons.ravel()
lat = lats.ravel()

time = reader_topaz.start_time
o.seed_elements(lon, lat, radius=0, number=30*30, time=time)

o.run(steps=48, time_step=3600)

15:18:49 INFO    opendrift.models.basemodel.environment:229: Fallback values will be used for the following variables which have no readers:
15:18:49 INFO    opendrift.models.basemodel.environment:232:    x_wind: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    y_wind: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    upward_sea_water_velocity: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    ocean_vertical_diffusivity: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    sea_surface_wave_significant_height: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    sea_surface_wave_stokes_drift_x_velocity: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    sea_surface_wave_stokes_drift_y_velocity: 0.000000
15:18:49 INFO    opendrift.models.basemodel.environment:232:    ocean_mixed_layer_thickness: 50.000000
15:18:49 INFO    opendrift:1778: Skipping environment variable ocean_vertical_diffusivity because of condition ['drift:vertical_mixing', 'is', False]
15:18:49 INFO    opendrift:1778: Skipping environment variable ocean_mixed_layer_thickness because of condition ['drift:vertical_mixing', 'is', False]
15:18:49 INFO    opendrift:1789: Storing previous values of element property lon because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
15:18:49 INFO    opendrift:1789: Storing previous values of element property lat because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
15:18:49 INFO    opendrift:1797: Storing previous values of environment variable sea_surface_height because of condition ['drift:vertical_advection', 'is', True]
15:18:49 INFO    opendrift:944: Using existing reader for land_binary_mask to move elements to ocean
15:18:49 INFO    opendrift.readers.reader_shape:149: Building KDTree from 177 geometries with buffer distance 0.000000e+00
15:18:49 INFO    opendrift:2085: 2018-01-01 00:00:00 - step 1 of 48 - 900 active elements (0 deactivated)
15:18:50 INFO    opendrift:2085: 2018-01-01 01:00:00 - step 2 of 48 - 900 active elements (0 deactivated)
15:18:50 INFO    opendrift:2085: 2018-01-01 02:00:00 - step 3 of 48 - 900 active elements (0 deactivated)
15:18:51 INFO    opendrift:2085: 2018-01-01 03:00:00 - step 4 of 48 - 900 active elements (0 deactivated)
15:18:51 INFO    opendrift:2085: 2018-01-01 04:00:00 - step 5 of 48 - 900 active elements (0 deactivated)
15:18:52 INFO    opendrift:2085: 2018-01-01 05:00:00 - step 6 of 48 - 900 active elements (0 deactivated)
15:18:53 INFO    opendrift:2085: 2018-01-01 06:00:00 - step 7 of 48 - 900 active elements (0 deactivated)
15:18:53 INFO    opendrift:2085: 2018-01-01 07:00:00 - step 8 of 48 - 900 active elements (0 deactivated)
15:18:54 INFO    opendrift:2085: 2018-01-01 08:00:00 - step 9 of 48 - 900 active elements (0 deactivated)
15:18:54 INFO    opendrift:2085: 2018-01-01 09:00:00 - step 10 of 48 - 900 active elements (0 deactivated)
15:18:55 INFO    opendrift:2085: 2018-01-01 10:00:00 - step 11 of 48 - 900 active elements (0 deactivated)
15:18:55 INFO    opendrift:2085: 2018-01-01 11:00:00 - step 12 of 48 - 900 active elements (0 deactivated)
15:18:56 INFO    opendrift:2085: 2018-01-01 12:00:00 - step 13 of 48 - 900 active elements (0 deactivated)
15:18:57 INFO    opendrift:2085: 2018-01-01 13:00:00 - step 14 of 48 - 900 active elements (0 deactivated)
15:18:57 INFO    opendrift:2085: 2018-01-01 14:00:00 - step 15 of 48 - 900 active elements (0 deactivated)
15:18:58 INFO    opendrift:2085: 2018-01-01 15:00:00 - step 16 of 48 - 900 active elements (0 deactivated)
15:18:58 INFO    opendrift:2085: 2018-01-01 16:00:00 - step 17 of 48 - 900 active elements (0 deactivated)
15:18:59 INFO    opendrift:2085: 2018-01-01 17:00:00 - step 18 of 48 - 900 active elements (0 deactivated)
15:18:59 INFO    opendrift:2085: 2018-01-01 18:00:00 - step 19 of 48 - 900 active elements (0 deactivated)
15:19:00 INFO    opendrift:2085: 2018-01-01 19:00:00 - step 20 of 48 - 900 active elements (0 deactivated)
15:19:01 INFO    opendrift:2085: 2018-01-01 20:00:00 - step 21 of 48 - 900 active elements (0 deactivated)
15:19:01 INFO    opendrift:2085: 2018-01-01 21:00:00 - step 22 of 48 - 900 active elements (0 deactivated)
15:19:02 INFO    opendrift:2085: 2018-01-01 22:00:00 - step 23 of 48 - 900 active elements (0 deactivated)
15:19:02 INFO    opendrift:2085: 2018-01-01 23:00:00 - step 24 of 48 - 900 active elements (0 deactivated)
15:19:03 INFO    opendrift:2085: 2018-01-02 00:00:00 - step 25 of 48 - 900 active elements (0 deactivated)
15:19:04 INFO    opendrift:2085: 2018-01-02 01:00:00 - step 26 of 48 - 900 active elements (0 deactivated)
15:19:05 INFO    opendrift:2085: 2018-01-02 02:00:00 - step 27 of 48 - 900 active elements (0 deactivated)
15:19:05 INFO    opendrift:2085: 2018-01-02 03:00:00 - step 28 of 48 - 900 active elements (0 deactivated)
15:19:06 INFO    opendrift:2085: 2018-01-02 04:00:00 - step 29 of 48 - 900 active elements (0 deactivated)
15:19:06 INFO    opendrift:2085: 2018-01-02 05:00:00 - step 30 of 48 - 900 active elements (0 deactivated)
15:19:07 INFO    opendrift:2085: 2018-01-02 06:00:00 - step 31 of 48 - 900 active elements (0 deactivated)
15:19:08 INFO    opendrift:2085: 2018-01-02 07:00:00 - step 32 of 48 - 900 active elements (0 deactivated)
15:19:08 INFO    opendrift:2085: 2018-01-02 08:00:00 - step 33 of 48 - 900 active elements (0 deactivated)
15:19:09 INFO    opendrift:2085: 2018-01-02 09:00:00 - step 34 of 48 - 900 active elements (0 deactivated)
15:19:10 INFO    opendrift:2085: 2018-01-02 10:00:00 - step 35 of 48 - 900 active elements (0 deactivated)
15:19:10 INFO    opendrift:2085: 2018-01-02 11:00:00 - step 36 of 48 - 900 active elements (0 deactivated)
15:19:11 INFO    opendrift:2085: 2018-01-02 12:00:00 - step 37 of 48 - 900 active elements (0 deactivated)
15:19:11 INFO    opendrift:2085: 2018-01-02 13:00:00 - step 38 of 48 - 899 active elements (1 deactivated)
15:19:12 INFO    opendrift:2085: 2018-01-02 14:00:00 - step 39 of 48 - 899 active elements (1 deactivated)
15:19:13 INFO    opendrift:2085: 2018-01-02 15:00:00 - step 40 of 48 - 899 active elements (1 deactivated)
15:19:13 INFO    opendrift:2085: 2018-01-02 16:00:00 - step 41 of 48 - 899 active elements (1 deactivated)
15:19:14 INFO    opendrift:2085: 2018-01-02 17:00:00 - step 42 of 48 - 899 active elements (1 deactivated)
15:19:15 INFO    opendrift:2085: 2018-01-02 18:00:00 - step 43 of 48 - 899 active elements (1 deactivated)
15:19:15 INFO    opendrift:2085: 2018-01-02 19:00:00 - step 44 of 48 - 899 active elements (1 deactivated)
15:19:16 INFO    opendrift:2085: 2018-01-02 20:00:00 - step 45 of 48 - 899 active elements (1 deactivated)
15:19:16 INFO    opendrift:2085: 2018-01-02 21:00:00 - step 46 of 48 - 899 active elements (1 deactivated)
15:19:17 INFO    opendrift:2085: 2018-01-02 22:00:00 - step 47 of 48 - 899 active elements (1 deactivated)
15:19:18 INFO    opendrift:2085: 2018-01-02 23:00:00 - step 48 of 48 - 899 active elements (1 deactivated)

<xarray.Dataset> Size: 4MB
Dimensions:                                   (trajectory: 900, time: 49)
Coordinates:
  * trajectory                                (trajectory) int64 7kB 0 1 ... 899
  * time                                      (time) datetime64[ns] 392B 2018...
Data variables: (12/21)
    status                                    (trajectory, time) float32 176kB ...
    moving                                    (trajectory, time) float32 176kB ...
    age_seconds                               (trajectory, time) float32 176kB ...
    origin_marker                             (trajectory, time) float32 176kB ...
    lon                                       (trajectory, time) float32 176kB ...
    lat                                       (trajectory, time) float32 176kB ...
    ...                                        ...
    upward_sea_water_velocity                 (trajectory, time) float32 176kB ...
    sea_surface_wave_significant_height       (trajectory, time) float32 176kB ...
    sea_surface_wave_stokes_drift_x_velocity  (trajectory, time) float32 176kB ...
    sea_surface_wave_stokes_drift_y_velocity  (trajectory, time) float32 176kB ...
    sea_floor_depth_below_sea_level           (trajectory, time) float32 176kB ...
    land_binary_mask                          (trajectory, time) float32 176kB ...
Attributes: (12/119)
    Conventions:                                                           CF...
    standard_name_vocabulary:                                              CF...
    featureType:                                                           tr...
    title:                                                                 Op...
    summary:                                                               Ou...
    keywords:                                                              tr...
    ...                                                                    ...
    geospatial_lon_units:                                                  de...
    geospatial_lon_resolution:                                             point
    runtime:                                                               0:...
    geospatial_vertical_min:                                               0.0
    geospatial_vertical_max:                                               0.0
    geospatial_vertical_positive:                                          up

xarray.Dataset

• Dimensions:
  • trajectory: 900
  • time: 49
• Coordinates: (2)
  • trajectory
    (trajectory)
    int64
    0 1 2 3 4 5 ... 895 896 897 898 899

    cf_role :

    trajectory_id

    dtype :

    <class 'numpy.int32'>

    array([  0,   1,   2, ..., 897, 898, 899], shape=(900,))

  • time
    (time)
    datetime64[ns]
    2018-01-01 ... 2018-01-03

    standard_name :

    time

    long_name :

    time

    dtype :

    <class 'numpy.float64'>

    array(['2018-01-01T00:00:00.000000000', '2018-01-01T01:00:00.000000000',
           '2018-01-01T02:00:00.000000000', '2018-01-01T03:00:00.000000000',
           '2018-01-01T04:00:00.000000000', '2018-01-01T05:00:00.000000000',
           '2018-01-01T06:00:00.000000000', '2018-01-01T07:00:00.000000000',
           '2018-01-01T08:00:00.000000000', '2018-01-01T09:00:00.000000000',
           '2018-01-01T10:00:00.000000000', '2018-01-01T11:00:00.000000000',
           '2018-01-01T12:00:00.000000000', '2018-01-01T13:00:00.000000000',
           '2018-01-01T14:00:00.000000000', '2018-01-01T15:00:00.000000000',
           '2018-01-01T16:00:00.000000000', '2018-01-01T17:00:00.000000000',
           '2018-01-01T18:00:00.000000000', '2018-01-01T19:00:00.000000000',
           '2018-01-01T20:00:00.000000000', '2018-01-01T21:00:00.000000000',
           '2018-01-01T22:00:00.000000000', '2018-01-01T23:00:00.000000000',
           '2018-01-02T00:00:00.000000000', '2018-01-02T01:00:00.000000000',
           '2018-01-02T02:00:00.000000000', '2018-01-02T03:00:00.000000000',
           '2018-01-02T04:00:00.000000000', '2018-01-02T05:00:00.000000000',
           '2018-01-02T06:00:00.000000000', '2018-01-02T07:00:00.000000000',
           '2018-01-02T08:00:00.000000000', '2018-01-02T09:00:00.000000000',
           '2018-01-02T10:00:00.000000000', '2018-01-02T11:00:00.000000000',
           '2018-01-02T12:00:00.000000000', '2018-01-02T13:00:00.000000000',
           '2018-01-02T14:00:00.000000000', '2018-01-02T15:00:00.000000000',
           '2018-01-02T16:00:00.000000000', '2018-01-02T17:00:00.000000000',
           '2018-01-02T18:00:00.000000000', '2018-01-02T19:00:00.000000000',
           '2018-01-02T20:00:00.000000000', '2018-01-02T21:00:00.000000000',
           '2018-01-02T22:00:00.000000000', '2018-01-02T23:00:00.000000000',
           '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (21)
  • status
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    dtype :

    <class 'numpy.int32'>

    valid_range :

    [0 1]

    flag_values :

    [0 1]

    flag_meanings :

    active stranded

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • moving
    (trajectory, time)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0

    dtype :

    <class 'numpy.int32'>

    minval :

    0

    maxval :

    1

    array([[1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.],
           ...,
           [1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.]], shape=(900, 49), dtype=float32)

  • age_seconds
    (trajectory, time)
    float32
    0.0 3.6e+03 ... 1.692e+05 1.728e+05

    dtype :

    <class 'numpy.float32'>

    units :

    s

    minval :

    0.0

    maxval :

    172800.0

    array([[     0.,   3600.,   7200., ..., 165600., 169200., 172800.],
           [     0.,   3600.,   7200., ..., 165600., 169200., 172800.],
           [     0.,   3600.,   7200., ..., 165600., 169200., 172800.],
           ...,
           [     0.,   3600.,   7200., ..., 165600., 169200., 172800.],
           [     0.,   3600.,   7200., ..., 165600., 169200., 172800.],
           [     0.,   3600.,   7200., ..., 165600., 169200., 172800.]],
          shape=(900, 49), dtype=float32)

  • origin_marker
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    dtype :

    <class 'numpy.int32'>

    unit :

    description :

    An integer kept constant during the simulation. Different values may be used for different seedings, to separate elements during analysis. With GUI, only a single seeding is possible.

    flag_values :

    [0]

    flag_meanings :

    Seed_0

    minval :

    0

    maxval :

    0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • lon
    (trajectory, time)
    float32
    12.0 12.0 12.0 ... 14.77 14.78

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    axis :

    X

    minval :

    11.736831

    maxval :

    15.01736

    array([[12.       , 11.997452 , 11.996539 , ..., 12.042464 , 12.026886 ,
            12.011368 ],
           [12.093103 , 12.084867 , 12.079823 , ..., 12.081334 , 12.06083  ,
            12.037527 ],
           [12.186207 , 12.174832 , 12.1664295, ..., 12.233293 , 12.236251 ,
            12.22998  ],
           ...,
           [14.513793 , 14.511426 , 14.506775 , ..., 14.576147 , 14.579472 ,
            14.588467 ],
           [14.606896 , 14.602464 , 14.595815 , ..., 14.669507 , 14.674631 ,
            14.688005 ],
           [14.7      , 14.691974 , 14.683078 , ..., 14.763984 , 14.766288 ,
            14.778265 ]], shape=(900, 49), dtype=float32)

  • lat
    (trajectory, time)
    float32
    67.5 67.51 67.52 ... 68.53 68.53

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    axis :

    Y

    minval :

    67.4565

    maxval :

    68.95808

    array([[67.5     , 67.5084  , 67.51727 , ..., 67.480865, 67.47427 ,
            67.47025 ],
           [67.5     , 67.511856, 67.52474 , ..., 67.50983 , 67.50091 ,
            67.49638 ],
           [67.5     , 67.50921 , 67.51988 , ..., 67.63559 , 67.627655,
            67.62181 ],
           ...,
           [68.5     , 68.501595, 68.50354 , ..., 68.51695 , 68.516014,
            68.51357 ],
           [68.5     , 68.50275 , 68.50559 , ..., 68.52668 , 68.52585 ,
            68.52391 ],
           [68.5     , 68.5035  , 68.507   , ..., 68.531654, 68.53107 ,
            68.52969 ]], shape=(900, 49), dtype=float32)

  • z
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    dtype :

    <class 'numpy.float32'>

    units :

    m

    standard_name :

    z

    long_name :

    vertical position

    axis :

    Z

    positive :

    up

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • wind_drift_factor
    (trajectory, time)
    float32
    0.02 0.02 0.02 ... 0.02 0.02 0.02

    dtype :

    <class 'numpy.float32'>

    units :

    1

    description :

    Elements at surface are moved with this fraction of the vind vector, in addition to currents and Stokes drift

    minval :

    0.02

    maxval :

    0.02

    array([[0.02, 0.02, 0.02, ..., 0.02, 0.02, 0.02],
           [0.02, 0.02, 0.02, ..., 0.02, 0.02, 0.02],
           [0.02, 0.02, 0.02, ..., 0.02, 0.02, 0.02],
           ...,
           [0.02, 0.02, 0.02, ..., 0.02, 0.02, 0.02],
           [0.02, 0.02, 0.02, ..., 0.02, 0.02, 0.02],
           [0.02, 0.02, 0.02, ..., 0.02, 0.02, 0.02]],
          shape=(900, 49), dtype=float32)

  • current_drift_factor
    (trajectory, time)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0

    dtype :

    <class 'numpy.float32'>

    units :

    1

    description :

    Elements are moved with this fraction of the current vector, in addition to currents and Stokes drift

    minval :

    1.0

    maxval :

    1.0

    array([[1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.],
           ...,
           [1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.],
           [1., 1., 1., ..., 1., 1., 1.]], shape=(900, 49), dtype=float32)

  • terminal_velocity
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    dtype :

    <class 'numpy.float32'>

    units :

    m/s

    description :

    Terminal rise/sinking velocity (buoyancy) in the ocean column

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • x_sea_water_velocity
    (trajectory, time)
    float32
    -0.03022 -0.01083 ... 0.136 0.136

    minval :

    -0.84602606

    maxval :

    0.6789669

    array([[-0.0302242 , -0.01082561,  0.04168542, ..., -0.18507458,
            -0.18439068, -0.18439068],
           [-0.09770244, -0.05979975,  0.02131426, ..., -0.24332711,
            -0.2765821 , -0.2765821 ],
           [-0.13492824, -0.09964079,  0.0009576 , ...,  0.03491766,
            -0.07403727, -0.07403727],
           ...,
           [-0.02690142, -0.05285398, -0.00351101, ...,  0.03776785,
             0.10217573,  0.10217573],
           [-0.05037567, -0.07555046, -0.02841385, ...,  0.05817407,
             0.15185152,  0.15185152],
           [-0.09120531, -0.1010806 , -0.03829937, ...,  0.02615485,
             0.13595879,  0.13595879]], shape=(900, 49), dtype=float32)

  • y_sea_water_velocity
    (trajectory, time)
    float32
    0.2603 0.2748 ... -0.04247 -0.04247

    minval :

    -0.6515052

    maxval :

    0.7165018

    array([[ 0.26031038,  0.27479348,  0.22772709, ..., -0.20409262,
            -0.12475014, -0.12475014],
           [ 0.36721167,  0.3994044 ,  0.35552502, ..., -0.27603787,
            -0.14031544, -0.14031544],
           [ 0.28540856,  0.3306116 ,  0.38958386, ..., -0.24593152,
            -0.18094158, -0.18094158],
           ...,
           [ 0.04939382,  0.06029136,  0.04300727, ..., -0.02925537,
            -0.07558445, -0.07558445],
           [ 0.08512232,  0.08808081,  0.05956256, ..., -0.02578659,
            -0.06010037, -0.06010037],
           [ 0.1085401 ,  0.10849848,  0.07204671, ..., -0.01815944,
            -0.04246836, -0.04246836]], shape=(900, 49), dtype=float32)

  • sea_surface_height
    (trajectory, time)
    float32
    0.4886 0.1452 ... 0.7494 0.7494

    minval :

    -0.9012145

    maxval :

    1.3849168

    array([[ 0.4885727 ,  0.14517993, -0.23342127, ...,  0.69381565,
             0.9271336 ,  0.9271336 ],
           [ 0.49601212,  0.14858425, -0.23339249, ...,  0.68687123,
             0.9250665 ,  0.9250665 ],
           [ 0.5100306 ,  0.16070464, -0.224138  , ...,  0.65037316,
             0.90407985,  0.90407985],
           ...,
           [ 0.5761482 ,  0.1797778 , -0.16351034, ...,  0.5084767 ,
             0.7735466 ,  0.7735466 ],
           [ 0.6066889 ,  0.16449822, -0.1577677 , ...,  0.50957394,
             0.7756367 ,  0.7756367 ],
           [ 0.6454551 ,  0.1502204 , -0.14475574, ...,  0.5072703 ,
             0.74939203,  0.74939203]], shape=(900, 49), dtype=float32)

  • x_wind
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • y_wind
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • upward_sea_water_velocity
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • sea_surface_wave_significant_height
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • sea_surface_wave_stokes_drift_x_velocity
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • sea_surface_wave_stokes_drift_y_velocity
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

  • sea_floor_depth_below_sea_level
    (trajectory, time)
    float32
    37.83 28.45 27.92 ... 104.7 104.7

    minval :

    13.1084585

    maxval :

    587.74023

    array([[ 37.83102 ,  28.449738,  27.918869, ...,  60.49933 ,  74.06522 ,
             74.06522 ],
           [ 37.7253  ,  27.17316 ,  22.53541 , ...,  28.15956 ,  31.9605  ,
             31.9605  ],
           [ 68.96143 ,  55.077255,  41.497387, ...,  92.60872 ,  89.27861 ,
             89.27861 ],
           ...,
           [ 90.27191 ,  89.38521 ,  87.955826, ...,  69.01942 ,  69.51655 ,
             69.51655 ],
           [ 82.25936 ,  80.154205,  78.26683 , ..., 103.38221 , 105.80314 ,
            105.80314 ],
           [105.92709 , 106.37078 , 105.59041 , ..., 104.741135, 104.66637 ,
            104.66637 ]], shape=(900, 49), dtype=float32)

  • land_binary_mask
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    minval :

    0.0

    maxval :

    0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(900, 49), dtype=float32)

• Indexes: (2)
  • trajectory
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           890, 891, 892, 893, 894, 895, 896, 897, 898, 899],
          dtype='int64', name='trajectory', length=900))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00',
                   '2018-01-01 02:00:00', '2018-01-01 03:00:00',
                   '2018-01-01 04:00:00', '2018-01-01 05:00:00',
                   '2018-01-01 06:00:00', '2018-01-01 07:00:00',
                   '2018-01-01 08:00:00', '2018-01-01 09:00:00',
                   '2018-01-01 10:00:00', '2018-01-01 11:00:00',
                   '2018-01-01 12:00:00', '2018-01-01 13:00:00',
                   '2018-01-01 14:00:00', '2018-01-01 15:00:00',
                   '2018-01-01 16:00:00', '2018-01-01 17:00:00',
                   '2018-01-01 18:00:00', '2018-01-01 19:00:00',
                   '2018-01-01 20:00:00', '2018-01-01 21:00:00',
                   '2018-01-01 22:00:00', '2018-01-01 23:00:00',
                   '2018-01-02 00:00:00', '2018-01-02 01:00:00',
                   '2018-01-02 02:00:00', '2018-01-02 03:00:00',
                   '2018-01-02 04:00:00', '2018-01-02 05:00:00',
                   '2018-01-02 06:00:00', '2018-01-02 07:00:00',
                   '2018-01-02 08:00:00', '2018-01-02 09:00:00',
                   '2018-01-02 10:00:00', '2018-01-02 11:00:00',
                   '2018-01-02 12:00:00', '2018-01-02 13:00:00',
                   '2018-01-02 14:00:00', '2018-01-02 15:00:00',
                   '2018-01-02 16:00:00', '2018-01-02 17:00:00',
                   '2018-01-02 18:00:00', '2018-01-02 19:00:00',
                   '2018-01-02 20:00:00', '2018-01-02 21:00:00',
                   '2018-01-02 22:00:00', '2018-01-02 23:00:00',
                   '2018-01-03 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq='h'))

• Attributes: (119)

  Conventions :

  CF-1.11, ACDD-1.3

  standard_name_vocabulary :

  CF Standard Name Table v85

  featureType :

  trajectory

  title :

  OpenDrift trajectory simulation

  summary :

  Output from simulation with OpenDrift framework

  keywords :

  trajectory, drift, lagrangian, simulation

  history :

  Created 2025-09-12 15:18:49.542923

  date_created :

  2025-09-12T15:18:49.542931

  source :

  Output from simulation with OpenDrift

  model_url :

  https://github.com/OpenDrift/opendrift

  opendrift_class :

  OceanDrift

  opendrift_module :

  opendrift.models.oceandrift

  readers :

  odict_keys(['shape', 'https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml'])

  time_coverage_start :

  2018-01-01 00:00:00

  time_step_calculation :

  1:00:00

  time_step_output :

  1:00:00

  config_environment:constant:x_sea_water_velocity :

  None

  config_environment:fallback:x_sea_water_velocity :

  0

  config_environment:constant:y_sea_water_velocity :

  None

  config_environment:fallback:y_sea_water_velocity :

  0

  config_environment:constant:sea_surface_height :

  None

  config_environment:fallback:sea_surface_height :

  0

  config_environment:constant:x_wind :

  None

  config_environment:fallback:x_wind :

  0

  config_environment:constant:y_wind :

  None

  config_environment:fallback:y_wind :

  0

  config_environment:constant:upward_sea_water_velocity :

  None

  config_environment:fallback:upward_sea_water_velocity :

  0

  config_environment:constant:ocean_vertical_diffusivity :

  None

  config_environment:fallback:ocean_vertical_diffusivity :

  0

  config_environment:constant:sea_surface_wave_significant_height :

  None

  config_environment:fallback:sea_surface_wave_significant_height :

  0

  config_environment:constant:sea_surface_wave_stokes_drift_x_velocity :

  None

  config_environment:fallback:sea_surface_wave_stokes_drift_x_velocity :

  0

  config_environment:constant:sea_surface_wave_stokes_drift_y_velocity :

  None

  config_environment:fallback:sea_surface_wave_stokes_drift_y_velocity :

  0

  config_environment:constant:ocean_mixed_layer_thickness :

  None

  config_environment:fallback:ocean_mixed_layer_thickness :

  50

  config_environment:constant:sea_floor_depth_below_sea_level :

  None

  config_environment:fallback:sea_floor_depth_below_sea_level :

  10000

  config_environment:constant:land_binary_mask :

  None

  config_environment:fallback:land_binary_mask :

  None

  config_general:use_auto_landmask :

  False

  config_drift:current_uncertainty :

  0

  config_drift:current_uncertainty_uniform :

  0

  config_drift:max_speed :

  2.0

  config_readers:max_number_of_fails :

  1

  config_general:simulation_name :

  config_general:coastline_action :

  stranding

  config_general:coastline_approximation_precision :

  0.001

  config_general:time_step_minutes :

  60

  config_general:time_step_output_minutes :

  None

  config_seed:ocean_only :

  True

  config_seed:number :

  1

  config_drift:max_age_seconds :

  None

  config_drift:advection_scheme :

  euler

  config_drift:horizontal_diffusivity :

  0

  config_drift:profiles_depth :

  50

  config_drift:wind_uncertainty :

  0

  config_drift:relative_wind :

  False

  config_drift:deactivate_north_of :

  None

  config_drift:deactivate_south_of :

  None

  config_drift:deactivate_east_of :

  None

  config_drift:deactivate_west_of :

  None

  config_seed:origin_marker :

  0

  config_seed:z :

  0

  config_seed:wind_drift_factor :

  0.02

  config_seed:current_drift_factor :

  1

  config_seed:terminal_velocity :

  0.0

  config_drift:vertical_advection :

  True

  config_drift:vertical_advection_at_surface :

  False

  config_drift:water_column_stretching :

  False

  config_drift:vertical_advection_correction :

  False

  config_drift:vertical_mixing :

  False

  config_drift:vertical_mixing_at_surface :

  False

  config_vertical_mixing:timestep :

  60

  config_vertical_mixing:diffusivitymodel :

  environment

  config_vertical_mixing:background_diffusivity :

  1.2e-05

  config_vertical_mixing:TSprofiles :

  False

  config_drift:wind_drift_depth :

  0.1

  config_drift:stokes_drift :

  True

  config_drift:stokes_drift_profile :

  Phillips

  config_drift:use_tabularised_stokes_drift :

  False

  config_drift:tabularised_stokes_drift_fetch :

  25000

  config_general:seafloor_action :

  lift_to_seafloor

  config_drift:truncate_ocean_model_below_m :

  None

  config_seed:seafloor :

  False

  opendrift_version :

  1.14.3

  seed_geojson :

  {"features": [], "type": "FeatureCollection"}

  simulation_time :

  2025-09-12 15:18:49.610564

  reader_x_sea_water_velocity :

  ['https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml']

  reader_y_sea_water_velocity :

  ['https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml']

  reader_sea_surface_height :

  ['https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml']

  reader_x_wind :

  0

  reader_y_wind :

  0

  reader_upward_sea_water_velocity :

  0

  reader_sea_surface_wave_significant_height :

  0

  reader_sea_surface_wave_stokes_drift_x_velocity :

  0

  reader_sea_surface_wave_stokes_drift_y_velocity :

  0

  reader_sea_floor_depth_below_sea_level :

  ['https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml']

  reader_land_binary_mask :

  ['shape']

  time_coverage_end :

  2018-01-03 00:00:00

  time_coverage_duration :

  P2DT0H0M0S

  time_coverage_resolution :

  P0DT1H0M0S

  performance :

  -------------------- Reader performance: -------------------- shape 0:00:00.0 total 0:00:00.0 preparing 0:00:00.0 reading 0:00:00.0 masking -------------------- https://thredds.met.no/thredds/dodsC/cmems/topaz6/dataset-topaz6-arc-15min-3km-be.ncml 0:00:27.9 total 0:00:00.0 preparing 0:00:27.8 reading 0:00:00.1 interpolation 0:00:00.0 interpolation_time 0:00:00.0 rotating vectors 0:00:00.0 masking -------------------- Performance: 33.2 total time 4.0 configuration 0.0 preparing main loop 0.0 moving elements to ocean 29.1 main loop 0.0 updating elements 0.0 cleaning up --------------------

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  67.4565

  geospatial_lat_max :

  68.95808

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  point

  geospatial_lon_min :

  11.736831

  geospatial_lon_max :

  15.01736

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  point

  runtime :

  0:00:33.261450

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  geospatial_vertical_positive :

  up

The custom landmask from shapefile, which is used for the simulation, is also shown in the map instead of the default GSHHG

o.plot()

o.animation()

15:19:22 INFO    opendrift:4612: Saving animation to /root/project/docs/source/gallery/animations/example_shape_landmask_0.gif...
15:19:47 INFO    opendrift:3053: Time to make animation: 0:00:26.115519