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

16:16:29 INFO    opendrift:576: OpenDriftSimulation initialised (version 1.14.6 / v1.14.6-23-g72ef044)

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])

16:16:29 INFO    opendrift.readers:63: Opening file with xr.open_dataset
16:16:29 INFO    opendrift.readers.reader_netCDF_CF_generic:332: Detected dimensions: {'time': 'time', 'x': 'x', 'y': 'y'}
16:16:29 INFO    opendrift.readers:63: Opening file with xr.open_dataset
16:16:29 INFO    opendrift.readers.reader_netCDF_CF_generic:332: 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)

16:16:29 INFO    opendrift.models.leeway:321: Seeding elements of object type 1: PIW-1 (Person-in-water (PIW), unknown state (mean values))
16:16:29 INFO    opendrift.models.basemodel.environment:203: Adding a global landmask from GSHHG
16:16:33 INFO    opendrift.models.basemodel.environment:227: Fallback values will be used for the following variables which have no readers:
16:16:33 INFO    opendrift.models.basemodel.environment:230:    sea_surface_wave_stokes_drift_x_velocity: 0.000000
16:16:33 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)

16:16:33 INFO    opendrift:1803: Skipping environment variable sea_surface_wave_stokes_drift_x_velocity because of condition ['drift:stokes_drift', 'is', False]
16:16:33 INFO    opendrift:1803: Skipping environment variable sea_surface_wave_stokes_drift_y_velocity because of condition ['drift:stokes_drift', 'is', False]
16:16:33 INFO    opendrift:1814: Storing previous values of element property lon because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
16:16:33 INFO    opendrift:1814: Storing previous values of element property lat because of condition (('general:coastline_action', 'in', ['stranding', 'previous']), 'or', ('general:seafloor_action', 'in', ['previous']))
16:16:33 INFO    opendrift:952: Using existing reader for land_binary_mask to move elements to ocean
16:16:33 INFO    opendrift:982: All points are in ocean
16:16:33 INFO    opendrift:2111: 2015-11-16 00:00:00 - step 1 of 144 - 5000 active elements (0 deactivated)
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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 ... 0.0 0.0
    moving                (trajectory, time) float32 260kB 1.0 1.0 ... 1.0 1.0
    age_seconds           (trajectory, time) float32 260kB 0.0 ... 4.32e+04
    origin_marker         (trajectory, time) float32 260kB 0.0 0.0 ... 0.0 0.0
    lon                   (trajectory, time) float32 260kB 6.55 6.549 ... 6.325
    lat                   (trajectory, time) float32 260kB 61.12 61.12 ... 61.09
    ...                    ...
    current_drift_factor  (trajectory, time) float32 260kB 1.0 1.0 ... 1.0 1.0
    x_wind                (trajectory, time) float32 260kB -4.579 ... -11.41
    y_wind                (trajectory, time) float32 260kB -2.255 ... -3.389
    x_sea_water_velocity  (trajectory, time) float32 260kB 0.2016 ... -0.3024
    y_sea_water_velocity  (trajectory, time) float32 260kB 0.1273 ... 0.1734
    land_binary_mask      (trajectory, time) float32 260kB 0.0 0.0 ... 0.0 0.0
Attributes: (12/89)
    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 ... 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., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  1.],
           [ 0.,  0.,  0., ..., nan, nan, nan],
           ...,
           [ 0.,  0.,  0., ..., nan, nan, nan],
           [ 0.,  0.,  0., ...,  0.,  1., nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.]],
          shape=(5000, 13), 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., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  0.],
           [ 1.,  1.,  1., ..., nan, nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  0., nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.]],
          shape=(5000, 13), dtype=float32)

  • age_seconds
    (trajectory, time)
    float32
    0.0 3.6e+03 ... 3.96e+04 4.32e+04

    dtype :

    <class 'numpy.float32'>

    units :

    s

    minval :

    0.0

    maxval :

    43200.0

    array([[    0.,  3600.,  7200., ...,    nan,    nan,    nan],
           [    0.,  3600.,  7200., ..., 36000., 39600., 42000.],
           [    0.,  3600.,  7200., ...,    nan,    nan,    nan],
           ...,
           [    0.,  3600.,  7200., ...,    nan,    nan,    nan],
           [    0.,  3600.,  7200., ..., 36000., 38400.,    nan],
           [    0.,  3600.,  7200., ..., 36000., 39600., 43200.]],
          shape=(5000, 13), 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., ..., 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., nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.]],
          shape=(5000, 13), dtype=float32)

  • lon
    (trajectory, time)
    float32
    6.55 6.549 6.539 ... 6.356 6.325

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    axis :

    X

    minval :

    5.815277

    maxval :

    6.597492

    array([[6.5504675, 6.549111 , 6.5386243, ...,       nan,       nan,
                  nan],
           [6.549419 , 6.550969 , 6.53742  , ..., 6.1706853, 6.105833 ,
            6.0677166],
           [6.549974 , 6.5518546, 6.54389  , ...,       nan,       nan,
                  nan],
           ...,
           [6.549061 , 6.547153 , 6.5254707, ...,       nan,       nan,
                  nan],
           [6.552106 , 6.5573688, 6.552206 , ..., 6.3457994, 6.3268223,
                  nan],
           [6.5498157, 6.556272 , 6.55137  , ..., 6.3879457, 6.3560114,
            6.324538 ]], shape=(5000, 13), dtype=float32)

  • lat
    (trajectory, time)
    float32
    61.12 61.12 61.11 ... 61.09 61.09

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    axis :

    Y

    minval :

    61.067173

    maxval :

    61.167183

    array([[61.11805 , 61.115505, 61.10842 , ...,       nan,       nan,
                  nan],
           [61.117336, 61.11975 , 61.114624, ..., 61.136517, 61.146626,
            61.14615 ],
           [61.11751 , 61.11683 , 61.11089 , ...,       nan,       nan,
                  nan],
           ...,
           [61.118107, 61.1216  , 61.11603 , ...,       nan,       nan,
                  nan],
           [61.118076, 61.11921 , 61.115166, ..., 61.078026, 61.08018 ,
                  nan],
           [61.11746 , 61.119972, 61.11621 , ..., 61.08029 , 61.085243,
            61.091255]], shape=(5000, 13), 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., ..., 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., nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.]],
          shape=(5000, 13), dtype=float32)

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

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

  • orientation
    (trajectory, time)
    float32
    0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0

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

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

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

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

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

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

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

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

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

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

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

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

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

  • downwind_eps
    (trajectory, time)
    float32
    21.17 21.17 21.17 ... -3.881 -3.881

    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,
                   nan],
           [-3.8805673, -3.8805673, -3.8805673, ..., -3.8805673, -3.8805673,
            -3.8805673]], shape=(5000, 13), dtype=float32)

  • crosswind_eps
    (trajectory, time)
    float32
    -0.4974 -0.4974 ... -1.684 -1.684

    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 ,          nan],
           [ -1.6843666 ,  -1.6843666 ,  -1.6843666 , ...,  -1.6843666 ,
             -1.6843666 ,  -1.6843666 ]], shape=(5000, 13), 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., ..., 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., nan],
           [ 1.,  1.,  1., ...,  1.,  1.,  1.]],
          shape=(5000, 13), dtype=float32)

  • x_wind
    (trajectory, time)
    float32
    -4.579 -4.233 ... -10.97 -11.41

    minval :

    -19.924046

    maxval :

    0.99743176

    array([[ -4.578784 ,  -4.2331743,  -2.9770007, ...,         nan,
                    nan,         nan],
           [ -4.4579573,  -5.0708246,  -4.2537227, ..., -16.68042  ,
            -14.436137 , -13.362433 ],
           [ -4.4833565,  -4.4628134,  -3.3413057, ...,         nan,
                    nan,         nan],
           ...,
           [ -4.6086583,  -4.8422627,  -4.700517 , ...,         nan,
                    nan,         nan],
           [ -4.561414 ,  -4.9084463,  -4.097376 , ..., -10.353207 ,
            -10.535994 ,         nan],
           [ -4.4754586,  -5.0748672,  -4.3537426, ...,  -9.993105 ,
            -10.97294  , -11.407165 ]], shape=(5000, 13), dtype=float32)

  • y_wind
    (trajectory, time)
    float32
    -2.255 -3.908 ... -4.487 -3.389

    minval :

    -11.135896

    maxval :

    2.8019693

    array([[-2.2554243 , -3.908098  , -2.1807878 , ...,         nan,
                    nan,         nan],
           [-2.2252295 , -4.877529  , -3.9607975 , ..., -0.19452201,
            -2.6550245 , -1.1919305 ],
           [-2.2093244 , -4.039304  , -2.4357474 , ...,         nan,
                    nan,         nan],
           ...,
           [-2.3788693 , -5.3209066 , -5.500447  , ...,         nan,
                    nan,         nan],
           [-2.1273673 , -4.1400766 , -2.7746038 , ..., -3.8006887 ,
            -3.5789773 ,         nan],
           [-2.2133646 , -4.4415    , -3.0846694 , ..., -4.0052495 ,
            -4.487425  , -3.3890533 ]], shape=(5000, 13), dtype=float32)

  • x_sea_water_velocity
    (trajectory, time)
    float32
    0.2016 0.07437 ... -0.4402 -0.3024

    minval :

    -1.1555066

    maxval :

    0.20655037

    array([[ 0.2016413 ,  0.07437186, -0.0938893 , ...,         nan,
                    nan,         nan],
           [ 0.19843063,  0.07269538, -0.2443818 , ..., -0.83698374,
            -0.6571505 , -0.8703992 ],
           [ 0.19952954,  0.07920196, -0.11176198, ...,         nan,
                    nan,         nan],
           ...,
           [ 0.20010862,  0.047608  , -0.3197676 , ...,         nan,
                    nan,         nan],
           [ 0.20398831,  0.07893433, -0.11316467, ..., -0.37015063,
            -0.17896152,         nan],
           [ 0.19920006,  0.06129721, -0.14214249, ..., -0.32121956,
            -0.44016677, -0.3024366 ]], shape=(5000, 13), dtype=float32)

  • y_sea_water_velocity
    (trajectory, time)
    float32
    0.1273 -0.004758 ... 0.09873 0.1734

    minval :

    -0.37959373

    maxval :

    0.5025773

    array([[ 0.12734297, -0.00475833, -0.07939768, ...,         nan,
                    nan,         nan],
           [ 0.12474818, -0.00300201, -0.3400757 , ...,  0.3942697 ,
             0.0173843 , -0.0012358 ],
           [ 0.12478171, -0.01097625, -0.15712652, ...,         nan,
                    nan,         nan],
           ...,
           [ 0.13019638, -0.00341915, -0.31978405, ...,         nan,
                    nan,         nan],
           [ 0.12510164, -0.03362731, -0.18441139, ...,  0.08660229,
             0.15730794,         nan],
           [ 0.12474002, -0.04169097, -0.21471389, ...,  0.0926571 ,
             0.09872583,  0.17343421]], shape=(5000, 13), 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., ..., 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., nan],
           [ 0.,  0.,  0., ...,  0.,  0.,  0.]],
          shape=(5000, 13), dtype=float32)

• Indexes: (2)
  • trajectory
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           4990, 4991, 4992, 4993, 4994, 4995, 4996, 4997, 4998, 4999],
          dtype='int64', name='trajectory', length=5000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-11-16 00:00:00', '2015-11-16 01:00:00',
                   '2015-11-16 02:00:00', '2015-11-16 03:00:00',
                   '2015-11-16 04:00:00', '2015-11-16 05:00:00',
                   '2015-11-16 06:00:00', '2015-11-16 07:00:00',
                   '2015-11-16 08:00:00', '2015-11-16 09:00:00',
                   '2015-11-16 10:00:00', '2015-11-16 11:00:00',
                   '2015-11-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq='h'))

• Attributes: (89)

  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 2025-11-27 16:16:33.761652

  date_created :

  2025-11-27T16:16:33.761663

  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: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: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 :

  1.14.6

  seed_geojson :

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

  simulation_time :

  2025-11-27 16:16:33.768222

  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 0:00:00.8 total 0:00:00.0 preparing 0:00:00.2 reading 0:00:00.1 interpolation 0:00:00.0 interpolation_time 0:00:00.5 rotating vectors 0:00:00.0 masking -------------------- /root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc 0:00:00.7 total 0:00:00.0 preparing 0:00:00.1 reading 0:00:00.1 interpolation 0:00:00.0 interpolation_time 0:00:00.5 rotating vectors 0:00:00.0 masking -------------------- global_landmask 0:00:01.6 total 0:00:00.0 preparing 0:00:01.6 reading 0:00:00.0 masking -------------------- Performance: 12.9 total time 4.4 configuration 0.0 preparing main loop 0.0 moving elements to ocean 8.4 main loop 0.5 updating elements 0.0 cleaning up --------------------

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  61.067173

  geospatial_lat_max :

  61.167183

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  point

  geospatial_lon_min :

  5.815277

  geospatial_lon_max :

  6.597492

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  point

  runtime :

  0:00:12.947556

  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
 0:00:00.8  total
 0:00:00.0  preparing
 0:00:00.2  reading
 0:00:00.1  interpolation
 0:00:00.0  interpolation_time
 0:00:00.5  rotating vectors
 0:00:00.0  masking
--------------------
/root/project/tests/test_data/16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc
 0:00:00.7  total
 0:00:00.0  preparing
 0:00:00.1  reading
 0:00:00.1  interpolation
 0:00:00.0  interpolation_time
 0:00:00.5  rotating vectors
 0:00:00.0  masking
--------------------
global_landmask
 0:00:01.6  total
 0:00:00.0  preparing
 0:00:01.6  reading
 0:00:00.0  masking
--------------------
Performance:
   12.9 total time
    4.4 configuration
    0.0 preparing main loop
      0.0 moving elements to ocean
    8.4 main loop
      0.5 updating elements
    0.0 cleaning up
--------------------
===========================
Model:  Leeway     (OpenDrift version 1.14.6)
        1101 active LeewayObj particles  (3899 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
===========================

16:16:53 INFO    opendrift:4651: Saving animation to /root/project/docs/source/gallery/animations/example_fjord_0.gif...
16:17:01 INFO    opendrift:3086: Time to make animation: 0:00:19.232960

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 1100x719.684 with 1 Axes>)