Fjord

from datetime import timedelta
from opendrift import test_data_folder as tdf
from opendrift.readers import reader_global_landmask
from opendrift.readers import reader_netCDF_CF_generic
from opendrift.models.leeway import Leeway

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

17:04:44 INFO    opendrift:569: OpenDriftSimulation initialised (version 1.14.11 / v1.14.12-66-g07283eb)

Add readers for wind and current

reader_arome = reader_netCDF_CF_generic.Reader(tdf +
    '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc')
reader_norkyst = reader_netCDF_CF_generic.Reader(tdf +
    '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')
o.add_reader([reader_norkyst, reader_arome])

17:04:44 INFO    opendrift.readers:67: Opening file with xr.open_dataset
17:04:44 INFO    opendrift.readers.reader_netCDF_CF_generic:340: Detected dimensions: {'time': 'time', 'x': 'x', 'y': 'y'}
17:04:44 INFO    opendrift.readers:67: Opening file with xr.open_dataset
17:04:45 INFO    opendrift.readers.reader_netCDF_CF_generic:340: Detected dimensions: {'x': 'X', 'y': 'Y', 'z': 'depth', 'time': 'time'}

Seed elements

time = reader_arome.start_time
object_type = 1  # 1: Person-in-water (PIW), unknown state (mean values)
o.seed_elements(lon=6.55, lat=61.117594, radius=50, number=5000, time=time, object_type=object_type)

17:04:45 INFO    opendrift.models.leeway:321: Seeding elements of object type 1: PIW-1 (Person-in-water (PIW), unknown state (mean values))
17:04:45 INFO    opendrift.models.basemodel.environment:203: Adding a global landmask from GSHHG
17:04:51 INFO    opendrift.models.basemodel.environment:227: Fallback values will be used for the following variables which have no readers:
17:04:51 INFO    opendrift.models.basemodel.environment:230:    sea_surface_wave_stokes_drift_x_velocity: 0.000000
17:04:51 INFO    opendrift.models.basemodel.environment:230:    sea_surface_wave_stokes_drift_y_velocity: 0.000000

Running model for 12 hours, using small time step due to high resolution coastline

o.run(duration=timedelta(hours=12), time_step=300, time_step_output=3600)

17:04:51 INFO    opendrift:1911: Skipping environment variable sea_surface_wave_stokes_drift_x_velocity because of condition ['drift:stokes_drift', 'is', False]
17:04:51 INFO    opendrift:1911: Skipping environment variable sea_surface_wave_stokes_drift_y_velocity because of condition ['drift:stokes_drift', 'is', False]
17:04:51 INFO    opendrift:1922: Storing previous values of element property lon because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
17:04:51 INFO    opendrift:1922: Storing previous values of element property lat because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
17:04:51 INFO    opendrift:958: Using existing reader for land_binary_mask to move elements to ocean
17:04:51 INFO    opendrift:990: All points are in ocean
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<xarray.Dataset> Size: 6MB
Dimensions:               (trajectory: 5000, time: 13)
Coordinates:
  * trajectory            (trajectory) int64 40kB 0 1 2 3 ... 4997 4998 4999
  * time                  (time) datetime64[ns] 104B 2015-11-16 ... 2015-11-1...
Data variables: (12/23)
    status                (trajectory, time) float32 260kB 0.0 0.0 ... nan nan
    moving                (trajectory, time) float32 260kB 1.0 1.0 ... nan nan
    age_seconds           (trajectory, time) float32 260kB 0.0 3.6e+03 ... nan
    origin_marker         (trajectory, time) float32 260kB 0.0 0.0 ... nan nan
    lon                   (trajectory, time) float32 260kB 6.55 6.549 ... nan
    lat                   (trajectory, time) float32 260kB 61.12 61.12 ... nan
    ...                    ...
    current_drift_factor  (trajectory, time) float32 260kB 1.0 1.0 ... nan nan
    x_wind                (trajectory, time) float32 260kB -4.579 -4.233 ... nan
    y_wind                (trajectory, time) float32 260kB -2.255 -3.908 ... nan
    x_sea_water_velocity  (trajectory, time) float32 260kB 0.2016 ... nan
    y_sea_water_velocity  (trajectory, time) float32 260kB 0.1273 ... nan
    land_binary_mask      (trajectory, time) float32 260kB 0.0 0.0 ... nan nan
Attributes: (12/88)
    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: 5000
  • time: 13
• Coordinates: (2)
  • trajectory
    (trajectory)
    int64
    0 1 2 3 4 ... 4996 4997 4998 4999

    cf_role :

    trajectory_id

    dtype :

    <class 'numpy.int32'>

    array([   0,    1,    2, ..., 4997, 4998, 4999], shape=(5000,))

  • time
    (time)
    datetime64[ns]
    2015-11-16 ... 2015-11-16T12:00:00

    standard_name :

    time

    long_name :

    time

    dtype :

    <class 'numpy.float64'>

    array(['2015-11-16T00:00:00.000000000', '2015-11-16T01:00:00.000000000',
           '2015-11-16T02:00:00.000000000', '2015-11-16T03:00:00.000000000',
           '2015-11-16T04:00:00.000000000', '2015-11-16T05:00:00.000000000',
           '2015-11-16T06:00:00.000000000', '2015-11-16T07:00:00.000000000',
           '2015-11-16T08:00:00.000000000', '2015-11-16T09:00:00.000000000',
           '2015-11-16T10:00:00.000000000', '2015-11-16T11:00:00.000000000',
           '2015-11-16T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (23)
  • status
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    dtype :

    <class 'numpy.int32'>

    valid_range :

    [0 1]

    flag_values :

    [0 1]

    flag_meanings :

    active stranded

    array([[ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

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

    dtype :

    <class 'numpy.int32'>

    minval :

    0

    maxval :

    1

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 1.,  1.,  1., ..., nan, nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • age_seconds
    (trajectory, time)
    float32
    0.0 3.6e+03 7.2e+03 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    s

    minval :

    0.0

    maxval :

    43200.0

    array([[    0.,  3600.,  7200., ...,    nan,    nan,    nan],
           [    0.,  3600.,  7200., ..., 36000., 39600., 43200.],
           [    0.,  3600.,  7200., ...,    nan,    nan,    nan],
           ...,
           [    0.,  3600.,  7200., ...,    nan,    nan,    nan],
           [    0.,  3600.,  7200., ..., 36000., 39600., 43200.],
           [    0.,  3600.,  7200., ...,    nan,    nan,    nan]],
          shape=(5000, 13), dtype=float32)

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

    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., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • lon
    (trajectory, time)
    float32
    6.55 6.549 6.539 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    axis :

    X

    minval :

    5.8376217

    maxval :

    6.596315

    array([[6.5504675, 6.549111 , 6.5386243, ...,       nan,       nan,
                  nan],
           [6.549419 , 6.550969 , 6.53742  , ..., 6.1723366, 6.105177 ,
            6.0366793],
           [6.549974 , 6.5518546, 6.54389  , ...,       nan,       nan,
                  nan],
           ...,
           [6.549061 , 6.547153 , 6.5254707, ...,       nan,       nan,
                  nan],
           [6.552106 , 6.5573688, 6.552206 , ..., 6.371861 , 6.34115  ,
            6.3257604],
           [6.5498157, 6.556272 , 6.55137  , ...,       nan,       nan,
                  nan]], shape=(5000, 13), dtype=float32)

  • lat
    (trajectory, time)
    float32
    61.12 61.12 61.11 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    axis :

    Y

    minval :

    61.06856

    maxval :

    61.16879

    array([[61.11805 , 61.115505, 61.10842 , ...,       nan,       nan,
                  nan],
           [61.117336, 61.11975 , 61.114624, ..., 61.13484 , 61.14556 ,
            61.1478  ],
           [61.11751 , 61.11683 , 61.11089 , ...,       nan,       nan,
                  nan],
           ...,
           [61.118107, 61.1216  , 61.11603 , ...,       nan,       nan,
                  nan],
           [61.118076, 61.11921 , 61.115166, ..., 61.077156, 61.078033,
            61.081554],
           [61.11746 , 61.119972, 61.11621 , ...,       nan,       nan,
                  nan]], shape=(5000, 13), dtype=float32)

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

    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., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • object_type
    (trajectory, time)
    float32
    1.0 1.0 1.0 1.0 ... nan nan nan nan

    dtype :

    <class 'numpy.uint16'>

    units :

    1

    minval :

    1

    maxval :

    1

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 1.,  1.,  1., ..., nan, nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • orientation
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    dtype :

    <class 'numpy.uint8'>

    units :

    1

    description :

    0/1 is left/right of downwind. Randomly chosen at seed time

    minval :

    0

    maxval :

    1

    array([[ 0.,  0.,  0., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 1.,  1.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • jibe_probability
    (trajectory, time)
    float32
    0.04 0.04 0.04 0.04 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    1/h

    description :

    Probability per hour that an object may change orientation (jibing)

    minval :

    0.04

    maxval :

    0.04

    array([[0.04, 0.04, 0.04, ...,  nan,  nan,  nan],
           [0.04, 0.04, 0.04, ..., 0.04, 0.04, 0.04],
           [0.04, 0.04, 0.04, ...,  nan,  nan,  nan],
           ...,
           [0.04, 0.04, 0.04, ...,  nan,  nan,  nan],
           [0.04, 0.04, 0.04, ..., 0.04, 0.04, 0.04],
           [0.04, 0.04, 0.04, ...,  nan,  nan,  nan]],
          shape=(5000, 13), dtype=float32)

  • capsized
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    dtype :

    <class 'numpy.uint8'>

    units :

    1

    description :

    0 is not capsized, changed to 1 after capsizing (irreversible). After capsizing, leeway coeffieiencts are reduced as given by config item capsized:leeway_fraction

    minval :

    0

    maxval :

    0

    array([[ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • downwind_slope
    (trajectory, time)
    float32
    0.96 0.96 0.96 0.96 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    %

    minval :

    0.96

    maxval :

    0.96

    array([[0.96, 0.96, 0.96, ...,  nan,  nan,  nan],
           [0.96, 0.96, 0.96, ..., 0.96, 0.96, 0.96],
           [0.96, 0.96, 0.96, ...,  nan,  nan,  nan],
           ...,
           [0.96, 0.96, 0.96, ...,  nan,  nan,  nan],
           [0.96, 0.96, 0.96, ..., 0.96, 0.96, 0.96],
           [0.96, 0.96, 0.96, ...,  nan,  nan,  nan]],
          shape=(5000, 13), dtype=float32)

  • crosswind_slope
    (trajectory, time)
    float32
    0.54 0.54 0.54 0.54 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    1

    minval :

    -0.54

    maxval :

    0.54

    array([[ 0.54,  0.54,  0.54, ...,   nan,   nan,   nan],
           [-0.54, -0.54, -0.54, ..., -0.54, -0.54, -0.54],
           [ 0.54,  0.54,  0.54, ...,   nan,   nan,   nan],
           ...,
           [-0.54, -0.54, -0.54, ...,   nan,   nan,   nan],
           [ 0.54,  0.54,  0.54, ...,  0.54,  0.54,  0.54],
           [-0.54, -0.54,  0.54, ...,   nan,   nan,   nan]],
          shape=(5000, 13), dtype=float32)

  • downwind_offset
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    cm/s

    minval :

    0.0

    maxval :

    0.0

    array([[ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • crosswind_offset
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    cm/s

    minval :

    0.0

    maxval :

    0.0

    array([[ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • downwind_eps
    (trajectory, time)
    float32
    21.17 21.17 21.17 ... nan nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    cm/s

    minval :

    -19.194134

    maxval :

    45.619923

    array([[21.168629 , 21.168629 , 21.168629 , ...,        nan,        nan,
                   nan],
           [ 4.8018866,  4.8018866,  4.8018866, ...,  4.8018866,  4.8018866,
             4.8018866],
           [11.744856 , 11.744856 , 11.744856 , ...,        nan,        nan,
                   nan],
           ...,
           [ 5.5480957,  5.5480957,  5.5480957, ...,        nan,        nan,
                   nan],
           [ 1.5582527,  1.5582527,  1.5582527, ...,  1.5582527,  1.5582527,
             1.5582527],
           [-3.8805673, -3.8805673, -3.8805673, ...,        nan,        nan,
                   nan]], shape=(5000, 13), dtype=float32)

  • crosswind_eps
    (trajectory, time)
    float32
    -0.4974 -0.4974 -0.4974 ... nan nan

    dtype :

    <class 'numpy.float32'>

    units :

    cm/s

    minval :

    -29.386293

    maxval :

    30.951326

    array([[ -0.49739662,  -0.49739662,  -0.49739662, ...,          nan,
                     nan,          nan],
           [ -5.4836106 ,  -5.4836106 ,  -5.4836106 , ...,  -5.4836106 ,
             -5.4836106 ,  -5.4836106 ],
           [ -3.348712  ,  -3.348712  ,  -3.348712  , ...,          nan,
                     nan,          nan],
           ...,
           [-12.274682  , -12.274682  , -12.274682  , ...,          nan,
                     nan,          nan],
           [ -6.6403794 ,  -6.6403794 ,  -6.6403794 , ...,  -6.6403794 ,
             -6.6403794 ,  -6.6403794 ],
           [ -1.6843666 ,  -1.6843666 ,  -1.6843666 , ...,          nan,
                     nan,          nan]], shape=(5000, 13), dtype=float32)

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

    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., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 1.,  1.,  1., ..., nan, nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

  • x_wind
    (trajectory, time)
    float32
    -4.579 -4.233 -2.977 ... nan nan

    minval :

    -20.020557

    maxval :

    1.0963066

    array([[ -4.578784 ,  -4.2331743,  -2.9770007, ...,         nan,
                    nan,         nan],
           [ -4.4579573,  -5.0708246,  -4.2537227, ..., -16.803509 ,
            -14.661805 , -13.407859 ],
           [ -4.4833565,  -4.4628134,  -3.3413057, ...,         nan,
                    nan,         nan],
           ...,
           [ -4.6086583,  -4.8422627,  -4.700517 , ...,         nan,
                    nan,         nan],
           [ -4.561414 ,  -4.9084463,  -4.097376 , ...,  -9.70048  ,
             -9.517749 , -10.431824 ],
           [ -4.4754586,  -5.0748672,  -4.3537426, ...,         nan,
                    nan,         nan]], shape=(5000, 13), dtype=float32)

  • y_wind
    (trajectory, time)
    float32
    -2.255 -3.908 -2.181 ... nan nan

    minval :

    -11.122188

    maxval :

    2.5470214

    array([[-2.2554243 , -3.908098  , -2.1807878 , ...,         nan,
                    nan,         nan],
           [-2.2252295 , -4.877529  , -3.9607975 , ..., -0.00666532,
            -2.7158952 , -0.09242412],
           [-2.2093244 , -4.039304  , -2.4357474 , ...,         nan,
                    nan,         nan],
           ...,
           [-2.3788693 , -5.3209066 , -5.500447  , ...,         nan,
                    nan,         nan],
           [-2.1273673 , -4.1400766 , -2.7746038 , ..., -3.6989434 ,
            -3.9523976 , -3.7379987 ],
           [-2.2133646 , -4.4415    , -3.0846694 , ...,         nan,
                    nan,         nan]], shape=(5000, 13), dtype=float32)

  • x_sea_water_velocity
    (trajectory, time)
    float32
    0.2016 0.07437 -0.09389 ... nan nan

    minval :

    -1.1441972

    maxval :

    0.20655037

    array([[ 0.2016413 ,  0.07437186, -0.0938893 , ...,         nan,
                    nan,         nan],
           [ 0.19843063,  0.07269538, -0.2443818 , ..., -0.8418578 ,
            -0.6893592 , -1.0131634 ],
           [ 0.19952954,  0.07920196, -0.11176198, ...,         nan,
                    nan,         nan],
           ...,
           [ 0.20010862,  0.047608  , -0.3197676 , ...,         nan,
                    nan,         nan],
           [ 0.20398831,  0.07893433, -0.11316467, ..., -0.38314384,
            -0.1994108 , -0.07471067],
           [ 0.19920006,  0.06129721, -0.14214249, ...,         nan,
                    nan,         nan]], shape=(5000, 13), dtype=float32)

  • y_sea_water_velocity
    (trajectory, time)
    float32
    0.1273 -0.004758 ... nan nan

    minval :

    -0.37959373

    maxval :

    0.4947611

    array([[ 0.12734297, -0.00475833, -0.07939768, ...,         nan,
                    nan,         nan],
           [ 0.12474818, -0.00300201, -0.3400757 , ...,  0.39377162,
             0.02072594,  0.2739789 ],
           [ 0.12478171, -0.01097625, -0.15712652, ...,         nan,
                    nan,         nan],
           ...,
           [ 0.13019638, -0.00341915, -0.31978405, ...,         nan,
                    nan,         nan],
           [ 0.12510164, -0.03362731, -0.18441139, ...,  0.08648206,
             0.11238429,  0.14833395],
           [ 0.12474002, -0.04169097, -0.21471389, ...,         nan,
                    nan,         nan]], shape=(5000, 13), dtype=float32)

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

    minval :

    0.0

    maxval :

    0.0

    array([[ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.],
           [ 0.,  0.,  0., ..., nan, nan, nan]],
          shape=(5000, 13), dtype=float32)

• Attributes: (88)

  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 2026-10-26 17:04:51.712695

  date_created :

  2026-10-26T17:04:51.712708

  source :

  Output from simulation with OpenDrift

  model_url :

  https://github.com/OpenDrift/opendrift

  opendrift_class :

  Leeway

  opendrift_module :

  opendrift.models.leeway

  readers :

  odict_keys(['/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc', '/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc', 'global_landmask'])

  time_coverage_start :

  2015-11-16 00:00:00

  time_step_calculation :

  0:05:00

  time_step_output :

  1:00:00

  config_environment:constant:x_wind :

  None

  config_environment:fallback:x_wind :

  None

  config_environment:constant:y_wind :

  None

  config_environment:fallback:y_wind :

  None

  config_environment:constant:x_sea_water_velocity :

  None

  config_environment:fallback:x_sea_water_velocity :

  None

  config_environment:constant:y_sea_water_velocity :

  None

  config_environment:fallback:y_sea_water_velocity :

  None

  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:land_binary_mask :

  None

  config_environment:fallback:land_binary_mask :

  None

  config_general:use_auto_landmask :

  True

  config_drift:current_uncertainty :

  0

  config_drift:current_uncertainty_uniform :

  0

  config_drift:max_speed :

  5.0

  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 :

  10.0

  config_general:time_step_output_minutes :

  60.0

  config_seed:ocean_only :

  True

  config_seed:number :

  1

  config_drift:max_age_seconds :

  None

  config_drift:advection_scheme :

  euler

  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:jibe_probability :

  0.04

  config_seed:capsized :

  0

  config_seed:current_drift_factor :

  1

  config_seed:object_type :

  Person-in-water (PIW), unknown state (mean values)

  config_processes:capsizing :

  False

  config_capsizing:leeway_fraction :

  0.4

  config_capsizing:wind_threshold :

  30

  config_capsizing:wind_threshold_sigma :

  5

  config_drift:stokes_drift :

  False

  opendrift_version :

  1.14.11

  seed_geojson :

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

  simulation_time :

  2026-10-26 17:04:51.723472

  reader_x_wind :

  ['/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc']

  reader_y_wind :

  ['/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc']

  reader_x_sea_water_velocity :

  ['/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc']

  reader_y_sea_water_velocity :

  ['/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc']

  reader_land_binary_mask :

  ['global_landmask']

  time_coverage_end :

  2015-11-16 12:00:00

  time_coverage_duration :

  P0DT12H0M0S

  time_coverage_resolution :

  P0DT0H5M0S

  performance :

  -------------------- Reader performance: -------------------- /root/project/tests/test_data/16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc 0:00:00.9 total 0:00:00.0 preparing 0:00:00.2 reading 0:00:00.1 interpolation 0:00:00.0 interpolation_time 0:00:00.6 rotating vectors 0:00:00.0 masking -------------------- /root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc 0:00:00.9 total 0:00:00.0 preparing 0:00:00.2 reading 0:00:00.1 interpolation 0:00:00.0 interpolation_time 0:00:00.6 rotating vectors 0:00:00.0 masking -------------------- global_landmask 0:00:00.2 total 0:00:00.0 preparing 0:00:00.2 reading 0:00:00.0 masking -------------------- Performance: 14.1 total time 7.3 configuration 0.0 preparing main loop 0.0 moving elements to ocean 6.7 main loop 0.6 updating elements 0.0 cleaning up --------------------

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  61.06856

  geospatial_lat_max :

  61.16879

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  point

  geospatial_lon_min :

  5.8376217

  geospatial_lon_max :

  6.596315

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  point

  runtime :

  0:00:14.108886

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  geospatial_vertical_positive :

  up

Print and plot results

print(o)
o.animation()

===========================
--------------------
Reader performance:
--------------------
/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc
 0:00:00.9  total
 0:00:00.0  preparing
 0:00:00.2  reading
 0:00:00.1  interpolation
 0:00:00.0  interpolation_time
 0:00:00.6  rotating vectors
 0:00:00.0  masking
--------------------
/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc
 0:00:00.9  total
 0:00:00.0  preparing
 0:00:00.2  reading
 0:00:00.1  interpolation
 0:00:00.0  interpolation_time
 0:00:00.6  rotating vectors
 0:00:00.0  masking
--------------------
global_landmask
 0:00:00.2  total
 0:00:00.0  preparing
 0:00:00.2  reading
 0:00:00.0  masking
--------------------
Performance:
   14.1 total time
    7.3 configuration
    0.0 preparing main loop
      0.0 moving elements to ocean
    6.7 main loop
      0.6 updating elements
    0.0 cleaning up
--------------------
===========================
Model:  Leeway     (OpenDrift version 1.14.11)
        971 active LeewayObj particles  (4029 deactivated, 0 scheduled)
-------------------
Environment variables:
  -----
  x_sea_water_velocity
  y_sea_water_velocity
     1) /root/project/tests/test_data/16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc
  -----
  x_wind
  y_wind
     1) /root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc
  -----
  land_binary_mask
     1) global_landmask

Discarded readers:

Time:
        Start: 2015-11-16 00:00:00 UTC
        Present: 2015-11-16 12:00:00 UTC
        Calculation steps: 144 * 0:05:00 - total time: 12:00:00
        Output steps: 13 * 1:00:00
===========================

17:05:08 INFO    opendrift:4889: Saving animation to /root/project/docs/source/gallery/animations/example_fjord_0.gif...
17:05:12 INFO    opendrift:3297: Time to make animation: 0:00:13.710385

o.plot()

(<GeoAxes: title={'center': 'OpenDrift - Leeway (PIW-1)\n2015-11-16 00:00 to 2015-11-16 12:00 UTC (13 steps)'}>, <Figure size 1100x615.06 with 1 Axes>)