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')

17:13:51 INFO    opendrift:513: OpenDriftSimulation initialised (version 1.14.2 / v1.14.2-88-gf09b7bd)
17:13:51 INFO    opendrift.readers:61: Opening file with xr.open_dataset
17:13:54 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')

17:13:54 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)

17:13:54 INFO    opendrift.models.basemodel.environment:229: Fallback values will be used for the following variables which have no readers:
17:13:54 INFO    opendrift.models.basemodel.environment:232:    x_wind: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    y_wind: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    upward_sea_water_velocity: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    ocean_vertical_diffusivity: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    sea_surface_wave_significant_height: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    sea_surface_wave_stokes_drift_x_velocity: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    sea_surface_wave_stokes_drift_y_velocity: 0.000000
17:13:54 INFO    opendrift.models.basemodel.environment:232:    ocean_mixed_layer_thickness: 50.000000
17:13:54 INFO    opendrift:1732: Skipping environment variable ocean_vertical_diffusivity because of condition ['drift:vertical_mixing', 'is', False]
17:13:54 INFO    opendrift:1732: Skipping environment variable ocean_mixed_layer_thickness because of condition ['drift:vertical_mixing', 'is', False]
17:13:54 INFO    opendrift:1743: Storing previous values of element property lon because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
17:13:54 INFO    opendrift:1743: Storing previous values of element property lat because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
17:13:54 INFO    opendrift:1751: Storing previous values of environment variable sea_surface_height because of condition ['drift:vertical_advection', 'is', True]
17:13:54 INFO    opendrift:899: Using existing reader for land_binary_mask
17:13:54 INFO    opendrift.readers.reader_shape:149: Building KDTree from 177 geometries with buffer distance 0.000000e+00
17:13:54 INFO    opendrift:2035: 2023-01-01 00:00:00 - step 1 of 48 - 900 active elements (0 deactivated)
17:13:55 INFO    opendrift:2035: 2023-01-01 01:00:00 - step 2 of 48 - 900 active elements (0 deactivated)
17:13:58 INFO    opendrift:2035: 2023-01-01 02:00:00 - step 3 of 48 - 880 active elements (20 deactivated)
17:13:58 INFO    opendrift:2035: 2023-01-01 03:00:00 - step 4 of 48 - 880 active elements (20 deactivated)
17:13:59 INFO    opendrift:2035: 2023-01-01 04:00:00 - step 5 of 48 - 878 active elements (22 deactivated)
17:14:00 INFO    opendrift:2035: 2023-01-01 05:00:00 - step 6 of 48 - 878 active elements (22 deactivated)
17:14:01 INFO    opendrift:2035: 2023-01-01 06:00:00 - step 7 of 48 - 878 active elements (22 deactivated)
17:14:01 INFO    opendrift:2035: 2023-01-01 07:00:00 - step 8 of 48 - 877 active elements (23 deactivated)
17:14:02 INFO    opendrift:2035: 2023-01-01 08:00:00 - step 9 of 48 - 875 active elements (25 deactivated)
17:14:03 INFO    opendrift:2035: 2023-01-01 09:00:00 - step 10 of 48 - 875 active elements (25 deactivated)
17:14:04 INFO    opendrift:2035: 2023-01-01 10:00:00 - step 11 of 48 - 873 active elements (27 deactivated)
17:14:04 INFO    opendrift:2035: 2023-01-01 11:00:00 - step 12 of 48 - 873 active elements (27 deactivated)
17:14:05 INFO    opendrift:2035: 2023-01-01 12:00:00 - step 13 of 48 - 873 active elements (27 deactivated)
17:14:06 INFO    opendrift:2035: 2023-01-01 13:00:00 - step 14 of 48 - 872 active elements (28 deactivated)
17:14:07 INFO    opendrift:2035: 2023-01-01 14:00:00 - step 15 of 48 - 871 active elements (29 deactivated)
17:14:07 INFO    opendrift:2035: 2023-01-01 15:00:00 - step 16 of 48 - 871 active elements (29 deactivated)
17:14:08 INFO    opendrift:2035: 2023-01-01 16:00:00 - step 17 of 48 - 871 active elements (29 deactivated)
17:14:09 INFO    opendrift:2035: 2023-01-01 17:00:00 - step 18 of 48 - 870 active elements (30 deactivated)
17:14:10 INFO    opendrift:2035: 2023-01-01 18:00:00 - step 19 of 48 - 869 active elements (31 deactivated)
17:14:10 INFO    opendrift:2035: 2023-01-01 19:00:00 - step 20 of 48 - 869 active elements (31 deactivated)
17:14:11 INFO    opendrift:2035: 2023-01-01 20:00:00 - step 21 of 48 - 869 active elements (31 deactivated)
17:14:12 INFO    opendrift:2035: 2023-01-01 21:00:00 - step 22 of 48 - 869 active elements (31 deactivated)
17:14:12 INFO    opendrift:2035: 2023-01-01 22:00:00 - step 23 of 48 - 869 active elements (31 deactivated)
17:14:13 INFO    opendrift:2035: 2023-01-01 23:00:00 - step 24 of 48 - 868 active elements (32 deactivated)
17:14:14 INFO    opendrift:2035: 2023-01-02 00:00:00 - step 25 of 48 - 867 active elements (33 deactivated)
17:14:15 INFO    opendrift:2035: 2023-01-02 01:00:00 - step 26 of 48 - 867 active elements (33 deactivated)
17:14:16 INFO    opendrift:2035: 2023-01-02 02:00:00 - step 27 of 48 - 864 active elements (36 deactivated)
17:14:16 INFO    opendrift:2035: 2023-01-02 03:00:00 - step 28 of 48 - 864 active elements (36 deactivated)
17:14:17 INFO    opendrift:2035: 2023-01-02 04:00:00 - step 29 of 48 - 864 active elements (36 deactivated)
17:14:18 INFO    opendrift:2035: 2023-01-02 05:00:00 - step 30 of 48 - 864 active elements (36 deactivated)
17:14:18 INFO    opendrift:2035: 2023-01-02 06:00:00 - step 31 of 48 - 864 active elements (36 deactivated)
17:14:19 INFO    opendrift:2035: 2023-01-02 07:00:00 - step 32 of 48 - 863 active elements (37 deactivated)
17:14:20 INFO    opendrift:2035: 2023-01-02 08:00:00 - step 33 of 48 - 863 active elements (37 deactivated)
17:14:20 INFO    opendrift:2035: 2023-01-02 09:00:00 - step 34 of 48 - 861 active elements (39 deactivated)
17:14:21 INFO    opendrift:2035: 2023-01-02 10:00:00 - step 35 of 48 - 860 active elements (40 deactivated)
17:14:22 INFO    opendrift:2035: 2023-01-02 11:00:00 - step 36 of 48 - 858 active elements (42 deactivated)
17:14:22 INFO    opendrift:2035: 2023-01-02 12:00:00 - step 37 of 48 - 858 active elements (42 deactivated)
17:14:23 INFO    opendrift:2035: 2023-01-02 13:00:00 - step 38 of 48 - 857 active elements (43 deactivated)
17:14:24 INFO    opendrift:2035: 2023-01-02 14:00:00 - step 39 of 48 - 857 active elements (43 deactivated)
17:14:24 INFO    opendrift:2035: 2023-01-02 15:00:00 - step 40 of 48 - 856 active elements (44 deactivated)
17:14:25 INFO    opendrift:2035: 2023-01-02 16:00:00 - step 41 of 48 - 855 active elements (45 deactivated)
17:14:26 INFO    opendrift:2035: 2023-01-02 17:00:00 - step 42 of 48 - 854 active elements (46 deactivated)
17:14:27 INFO    opendrift:2035: 2023-01-02 18:00:00 - step 43 of 48 - 854 active elements (46 deactivated)
17:14:27 INFO    opendrift:2035: 2023-01-02 19:00:00 - step 44 of 48 - 852 active elements (48 deactivated)
17:14:28 INFO    opendrift:2035: 2023-01-02 20:00:00 - step 45 of 48 - 851 active elements (49 deactivated)
17:14:29 INFO    opendrift:2035: 2023-01-02 21:00:00 - step 46 of 48 - 851 active elements (49 deactivated)
17:14:30 INFO    opendrift:2035: 2023-01-02 22:00:00 - step 47 of 48 - 851 active elements (49 deactivated)
17:14:31 INFO    opendrift:2035: 2023-01-02 23:00:00 - step 48 of 48 - 850 active elements (50 deactivated)

<xarray.Dataset> Size: 4MB
Dimensions:                                   (trajectory: 900, time: 49)
Coordinates:
  * trajectory                                (trajectory) int64 7kB 0 1 ... 899
  * time                                      (time) datetime64[ns] 392B 2023...
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]
    2023-01-01 ... 2023-01-03

    standard_name :

    time

    long_name :

    time

    dtype :

    <class 'numpy.float64'>

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T01:00:00.000000000',
           '2023-01-01T02:00:00.000000000', '2023-01-01T03:00:00.000000000',
           '2023-01-01T04:00:00.000000000', '2023-01-01T05:00:00.000000000',
           '2023-01-01T06:00:00.000000000', '2023-01-01T07:00:00.000000000',
           '2023-01-01T08:00:00.000000000', '2023-01-01T09:00:00.000000000',
           '2023-01-01T10:00:00.000000000', '2023-01-01T11:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T13:00:00.000000000',
           '2023-01-01T14:00:00.000000000', '2023-01-01T15:00:00.000000000',
           '2023-01-01T16:00:00.000000000', '2023-01-01T17:00:00.000000000',
           '2023-01-01T18:00:00.000000000', '2023-01-01T19:00:00.000000000',
           '2023-01-01T20:00:00.000000000', '2023-01-01T21:00:00.000000000',
           '2023-01-01T22:00:00.000000000', '2023-01-01T23:00:00.000000000',
           '2023-01-02T00:00:00.000000000', '2023-01-02T01:00:00.000000000',
           '2023-01-02T02:00:00.000000000', '2023-01-02T03:00:00.000000000',
           '2023-01-02T04:00:00.000000000', '2023-01-02T05:00:00.000000000',
           '2023-01-02T06:00:00.000000000', '2023-01-02T07:00:00.000000000',
           '2023-01-02T08:00:00.000000000', '2023-01-02T09:00:00.000000000',
           '2023-01-02T10:00:00.000000000', '2023-01-02T11:00:00.000000000',
           '2023-01-02T12:00:00.000000000', '2023-01-02T13:00:00.000000000',
           '2023-01-02T14:00:00.000000000', '2023-01-02T15:00:00.000000000',
           '2023-01-02T16:00:00.000000000', '2023-01-02T17:00:00.000000000',
           '2023-01-02T18:00:00.000000000', '2023-01-02T19:00:00.000000000',
           '2023-01-02T20:00:00.000000000', '2023-01-02T21:00:00.000000000',
           '2023-01-02T22:00:00.000000000', '2023-01-02T23:00:00.000000000',
           '2023-01-03T00:00:00.000000000'], 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 12.03 12.05 ... 14.96 14.95

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    axis :

    X

    minval :

    12.0

    maxval :

    15.223386

    array([[12.       , 12.0303545, 12.052847 , ..., 12.482344 , 12.470093 ,
            12.457545 ],
           [12.093103 , 12.125996 , 12.157017 , ..., 12.613725 , 12.599938 ,
            12.586041 ],
           [12.186207 , 12.215896 , 12.24539  , ..., 12.694525 , 12.680466 ,
            12.666243 ],
           ...,
           [14.513793 , 14.522423 , 14.532999 , ..., 14.844349 , 14.840429 ,
            14.836187 ],
           [14.606896 , 14.612562 , 14.620839 , ..., 14.906981 , 14.90346  ,
            14.89992  ],
           [14.7      , 14.70337  , 14.709921 , ..., 14.958829 , 14.955042 ,
            14.951324 ]], shape=(900, 49), dtype=float32)

  • lat
    (trajectory, time)
    float32
    67.5 67.5 67.49 ... 68.5 68.5 68.5

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    axis :

    Y

    minval :

    67.42017

    maxval :

    68.75868

    array([[67.5     , 67.496376, 67.492165, ..., 67.458916, 67.45654 ,
            67.45368 ],
           [67.5     , 67.49734 , 67.48963 , ..., 67.43611 , 67.43346 ,
            67.43045 ],
           [67.5     , 67.49729 , 67.491165, ..., 67.43714 , 67.43391 ,
            67.430504],
           ...,
           [68.5     , 68.50217 , 68.50366 , ..., 68.489395, 68.48845 ,
            68.48764 ],
           [68.5     , 68.50149 , 68.50222 , ..., 68.48821 , 68.4875  ,
            68.48684 ],
           [68.5     , 68.500565, 68.5004  , ..., 68.49888 , 68.49857 ,
            68.49829 ]], 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
    0.3603 0.267 ... -0.04226 -0.04226

    minval :

    -0.59250516

    maxval :

    0.69784015

    array([[ 0.36027598,  0.2670191 ,  0.20259352, ..., -0.14564472,
            -0.14919738, -0.14919738],
           [ 0.39038134,  0.36830828,  0.3109987 , ..., -0.1640648 ,
            -0.16540691, -0.16540691],
           [ 0.35236827,  0.35014936,  0.30750898, ..., -0.16730241,
            -0.16928703, -0.16928703],
           ...,
           [ 0.09807607,  0.12018636,  0.13529764, ..., -0.04458209,
            -0.04823161, -0.04823161],
           [ 0.06438819,  0.09407415,  0.11995235, ..., -0.04006203,
            -0.04024094, -0.04024094],
           [ 0.03830405,  0.07445946,  0.10185227, ..., -0.04304175,
            -0.04225982, -0.04225982]], shape=(900, 49), dtype=float32)

  • y_sea_water_velocity
    (trajectory, time)
    float32
    -0.1122 -0.1305 ... -0.008743

    minval :

    -0.60356736

    maxval :

    0.57712555

    array([[-1.12170659e-01, -1.30489022e-01, -1.94583565e-01, ...,
            -7.35547468e-02, -8.84761661e-02, -8.84761661e-02],
           [-8.23545083e-02, -2.38597453e-01, -2.75745213e-01, ...,
            -8.22975188e-02, -9.29141939e-02, -9.29141939e-02],
           [-8.38205516e-02, -1.89781711e-01, -2.42018536e-01, ...,
            -1.00249149e-01, -1.05530664e-01, -1.05530664e-01],
           ...,
           [ 6.71983808e-02,  4.62411046e-02,  2.13005319e-02, ...,
            -2.92251837e-02, -2.50597764e-02, -2.50597764e-02],
           [ 4.61871549e-02,  2.25550625e-02, -8.75125115e-05, ...,
            -2.18372550e-02, -2.05522627e-02, -2.05522627e-02],
           [ 1.74187608e-02, -5.22674061e-03, -2.42639203e-02, ...,
            -9.41595715e-03, -8.74302629e-03, -8.74302629e-03]],
          shape=(900, 49), dtype=float32)

  • sea_surface_height
    (trajectory, time)
    float32
    -0.8262 -0.8473 ... -0.1962 -0.1962

    minval :

    -0.9449728

    maxval :

    0.7070262

    array([[-0.8261687 , -0.8472681 , -0.7013644 , ...,  0.00215531,
            -0.29847494, -0.29847494],
           [-0.83657676, -0.8493381 , -0.70292693, ...,  0.00384008,
            -0.30034655, -0.30034655],
           [-0.8398286 , -0.85400957, -0.70615155, ...,  0.00621596,
            -0.30009067, -0.30009067],
           ...,
           [-0.673892  , -0.7359988 , -0.6578921 , ...,  0.06059292,
            -0.1964374 , -0.1964374 ],
           [-0.67339694, -0.73623043, -0.65837103, ...,  0.06173269,
            -0.1964396 , -0.1964396 ],
           [-0.67428374, -0.7378258 , -0.66033256, ...,  0.06266223,
            -0.19623359, -0.19623359]], 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 34.16 37.26 ... 87.01 87.01

    minval :

    12.911012

    maxval :

    414.81555

    array([[ 37.83102 ,  34.158604,  37.256187, ..., 191.62173 , 190.19409 ,
            190.19409 ],
           [ 37.7253  ,  53.07212 ,  74.25539 , ..., 224.9132  , 223.5293  ,
            223.5293  ],
           [ 68.96143 ,  84.051414, 106.3436  , ..., 239.32523 , 238.21085 ,
            238.21085 ],
           ...,
           [ 90.27191 ,  91.72703 ,  91.39114 , ...,  92.86736 ,  92.17461 ,
             92.17461 ],
           [ 82.25936 ,  80.786476,  84.09972 , ...,  87.83062 ,  87.924515,
             87.924515],
           [105.92709 , 105.709015, 104.59628 , ...,  86.70595 ,  87.01476 ,
             87.01476 ]], 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(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
                   '2023-01-01 02:00:00', '2023-01-01 03:00:00',
                   '2023-01-01 04:00:00', '2023-01-01 05:00:00',
                   '2023-01-01 06:00:00', '2023-01-01 07:00:00',
                   '2023-01-01 08:00:00', '2023-01-01 09:00:00',
                   '2023-01-01 10:00:00', '2023-01-01 11:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 13:00:00',
                   '2023-01-01 14:00:00', '2023-01-01 15:00:00',
                   '2023-01-01 16:00:00', '2023-01-01 17:00:00',
                   '2023-01-01 18:00:00', '2023-01-01 19:00:00',
                   '2023-01-01 20:00:00', '2023-01-01 21:00:00',
                   '2023-01-01 22:00:00', '2023-01-01 23:00:00',
                   '2023-01-02 00:00:00', '2023-01-02 01:00:00',
                   '2023-01-02 02:00:00', '2023-01-02 03:00:00',
                   '2023-01-02 04:00:00', '2023-01-02 05:00:00',
                   '2023-01-02 06:00:00', '2023-01-02 07:00:00',
                   '2023-01-02 08:00:00', '2023-01-02 09:00:00',
                   '2023-01-02 10:00:00', '2023-01-02 11:00:00',
                   '2023-01-02 12:00:00', '2023-01-02 13:00:00',
                   '2023-01-02 14:00:00', '2023-01-02 15:00:00',
                   '2023-01-02 16:00:00', '2023-01-02 17:00:00',
                   '2023-01-02 18:00:00', '2023-01-02 19:00:00',
                   '2023-01-02 20:00:00', '2023-01-02 21:00:00',
                   '2023-01-02 22:00:00', '2023-01-02 23:00:00',
                   '2023-01-03 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 2025-07-11 17:13:54.862830

  date_created :

  2025-07-11T17:13:54.862842

  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 :

  2023-01-01 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 :

  1.14.2

  seed_geojson :

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

  simulation_time :

  2025-07-11 17:13:54.964151

  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 :

  2023-01-03 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 0:00:34.1 total 0:00:00.0 preparing 0:00:34.0 reading 0:00:00.1 interpolation 0:00:00.0 interpolation_time 0:00:00.0 rotating vectors 0:00:00.0 masking -------------------- Performance: 40.0 total time 2.9 configuration 0.1 preparing main loop 0.0 moving elements to ocean 36.9 main loop 0.0 updating elements 0.0 cleaning up --------------------

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  67.42017

  geospatial_lat_max :

  68.75868

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  point

  geospatial_lon_min :

  12.0

  geospatial_lon_max :

  15.223386

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  point

  runtime :

  0:00:40.049530

  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()

17:14:36 INFO    opendrift:4553: Saving animation to /root/project/docs/source/gallery/animations/example_shape_landmask_0.gif...
17:15:04 INFO    opendrift:2994: Time to make animation: 0:00:28.689190