Wind blow model

from opendrift.readers import reader_netCDF_CF_generic
from opendrift.models.windblow import WindBlow

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

17:10:55 INFO    opendrift:509: OpenDriftSimulation initialised (version 1.13.0 / v1.13.0-15-g93fccf7)

Example of elements blowing with the wind, also over land

#reader_arome = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/mepslatest/meps_lagged_6_h_latest_2_5km_latest.nc')
reader_arome = reader_netCDF_CF_generic.Reader(o.test_data_folder() +
    '2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc')

o.add_reader([reader_arome])

17:10:55 INFO    opendrift.readers:58: Opening file with xr.open_dataset
17:10:55 INFO    opendrift.readers.reader_netCDF_CF_generic:338: Detected dimensions: {'time': 'time', 'x': 'x', 'y': 'y'}

Seeding some particles

lat = 68.5; lon = 16.0  # Lofoten
o.seed_elements(lon, lat, radius=5000, number=1000,
                time=reader_arome.start_time)

Running model for 48 hours

o.run(steps=48*4, time_step=900, time_step_output=3600)

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<xarray.Dataset> Size: 2MB
Dimensions:        (trajectory: 1000, time: 49)
Coordinates:
  * trajectory     (trajectory) int64 8kB 0 1 2 3 4 5 ... 995 996 997 998 999
  * time           (time) datetime64[ns] 392B 2016-02-02T12:00:00 ... 2016-02...
Data variables:
    status         (trajectory, time) float32 196kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    moving         (trajectory, time) float32 196kB 1.0 1.0 1.0 ... 1.0 1.0 1.0
    age_seconds    (trajectory, time) float32 196kB 0.0 3.6e+03 ... 1.728e+05
    origin_marker  (trajectory, time) float32 196kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    lon            (trajectory, time) float32 196kB 16.22 16.25 ... 16.26 16.15
    lat            (trajectory, time) float32 196kB 68.52 68.56 ... 68.87 68.76
    z              (trajectory, time) float32 196kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    x_wind         (trajectory, time) float32 196kB 0.1484 0.3618 ... -0.7952
    y_wind         (trajectory, time) float32 196kB 2.932 -0.01734 ... -2.926
Attributes: (12/67)
    Conventions:                                       CF-1.11, ACDD-1.3
    standard_name_vocabulary:                          CF Standard Name Table...
    featureType:                                       trajectory
    title:                                             OpenDrift trajectory s...
    summary:                                           Output from simulation...
    keywords:                                          trajectory, drift, lag...
    ...                                                ...
    geospatial_lon_units:                              degrees_east
    geospatial_lon_resolution:                         point
    runtime:                                           0:00:02.963434
    geospatial_vertical_min:                           0.0
    geospatial_vertical_max:                           0.0
    geospatial_vertical_positive:                      up

xarray.Dataset

• Dimensions:
  • trajectory: 1000
  • time: 49
• Coordinates: (2)
  • trajectory
    (trajectory)
    int64
    0 1 2 3 4 5 ... 995 996 997 998 999

    cf_role :

    trajectory_id

    dtype :

    <class 'numpy.int32'>

    array([  0,   1,   2, ..., 997, 998, 999])

  • time
    (time)
    datetime64[ns]
    2016-02-02T12:00:00 ... 2016-02-...

    standard_name :

    time

    long_name :

    time

    dtype :

    <class 'numpy.float64'>

    array(['2016-02-02T12:00:00.000000000', '2016-02-02T13:00:00.000000000',
           '2016-02-02T14:00:00.000000000', '2016-02-02T15:00:00.000000000',
           '2016-02-02T16:00:00.000000000', '2016-02-02T17:00:00.000000000',
           '2016-02-02T18:00:00.000000000', '2016-02-02T19:00:00.000000000',
           '2016-02-02T20:00:00.000000000', '2016-02-02T21:00:00.000000000',
           '2016-02-02T22:00:00.000000000', '2016-02-02T23:00:00.000000000',
           '2016-02-03T00:00:00.000000000', '2016-02-03T01:00:00.000000000',
           '2016-02-03T02:00:00.000000000', '2016-02-03T03:00:00.000000000',
           '2016-02-03T04:00:00.000000000', '2016-02-03T05:00:00.000000000',
           '2016-02-03T06:00:00.000000000', '2016-02-03T07:00:00.000000000',
           '2016-02-03T08:00:00.000000000', '2016-02-03T09:00:00.000000000',
           '2016-02-03T10:00:00.000000000', '2016-02-03T11:00:00.000000000',
           '2016-02-03T12:00:00.000000000', '2016-02-03T13:00:00.000000000',
           '2016-02-03T14:00:00.000000000', '2016-02-03T15:00:00.000000000',
           '2016-02-03T16:00:00.000000000', '2016-02-03T17:00:00.000000000',
           '2016-02-03T18:00:00.000000000', '2016-02-03T19:00:00.000000000',
           '2016-02-03T20:00:00.000000000', '2016-02-03T21:00:00.000000000',
           '2016-02-03T22:00:00.000000000', '2016-02-03T23:00:00.000000000',
           '2016-02-04T00:00:00.000000000', '2016-02-04T01:00:00.000000000',
           '2016-02-04T02:00:00.000000000', '2016-02-04T03:00:00.000000000',
           '2016-02-04T04:00:00.000000000', '2016-02-04T05:00:00.000000000',
           '2016-02-04T06:00:00.000000000', '2016-02-04T07:00:00.000000000',
           '2016-02-04T08:00:00.000000000', '2016-02-04T09:00:00.000000000',
           '2016-02-04T10:00:00.000000000', '2016-02-04T11:00:00.000000000',
           '2016-02-04T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (9)
  • 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 0]

    flag_values :

    [0]

    flag_meanings :

    active

    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.]], 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 :

    1

    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.]], 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.]],
          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.]], dtype=float32)

  • lon
    (trajectory, time)
    float32
    16.22 16.25 16.29 ... 16.26 16.15

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    axis :

    X

    minval :

    13.755725

    maxval :

    19.037323

    array([[16.2158  , 16.248919, 16.289194, ..., 19.008604, 19.008604,
            19.008604],
           [16.048985, 16.183342, 16.262407, ..., 18.9986  , 18.9986  ,
            18.9986  ],
           [16.1195  , 16.17048 , 16.241932, ..., 19.0145  , 19.0145  ,
            19.0145  ],
           ...,
           [16.011513, 16.188627, 16.256102, ..., 19.020643, 19.020643,
            19.020643],
           [15.860083, 15.828502, 15.804846, ..., 16.454144, 16.253345,
            16.13885 ],
           [15.956353, 16.090857, 16.088266, ..., 16.456646, 16.260311,
            16.146967]], dtype=float32)

  • lat
    (trajectory, time)
    float32
    68.52 68.56 68.56 ... 68.87 68.76

    dtype :

    <class 'numpy.float32'>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    axis :

    Y

    minval :

    68.02076

    maxval :

    70.15918

    array([[68.52478 , 68.56403 , 68.56059 , ..., 70.14737 , 70.14737 ,
            70.14737 ],
           [68.54    , 68.58305 , 68.61288 , ..., 70.05307 , 70.05307 ,
            70.05307 ],
           [68.481026, 68.53509 , 68.55049 , ..., 70.11819 , 70.11819 ,
            70.11819 ],
           ...,
           [68.5071  , 68.55052 , 68.557755, ..., 70.09679 , 70.09679 ,
            70.09679 ],
           [68.44875 , 68.40514 , 68.35107 , ..., 68.9767  , 68.875595,
            68.76584 ],
           [68.44123 , 68.42057 , 68.39919 , ..., 68.96896 , 68.87094 ,
            68.764595]], 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.]], dtype=float32)

  • x_wind
    (trajectory, time)
    float32
    0.1484 0.3618 ... -1.411 -0.7952

    minval :

    -7.8091097

    maxval :

    6.3157578

    array([[ 0.14837873,  0.3617983 ,  0.6413248 , ...,  0.        ,
             0.        ,  0.        ],
           [ 1.5947337 ,  0.87310576,  2.6470194 , ...,  0.        ,
             0.        ,  0.        ],
           [ 0.63504326,  0.6910955 ,  0.7480437 , ...,  0.        ,
             0.        ,  0.        ],
           ...,
           [ 1.7961627 ,  1.1328461 ,  0.6826552 , ...,  0.        ,
             0.        ,  0.        ],
           [-0.5491292 ,  0.04082868, -0.4245772 , ..., -2.632736  ,
            -1.4637727 , -0.66737896],
           [ 1.6799977 ,  0.26372674, -0.34679034, ..., -2.5414915 ,
            -1.4109973 , -0.79520583]], dtype=float32)

  • y_wind
    (trajectory, time)
    float32
    2.932 -0.01734 ... -2.331 -2.926

    minval :

    -10.279299

    maxval :

    6.8426275

    array([[ 2.932064  , -0.01733961, -0.02893268, ...,  0.        ,
             0.        ,  0.        ],
           [ 2.0124655 ,  0.7563105 ,  2.9672046 , ...,  0.        ,
             0.        ,  0.        ],
           [ 1.6744542 ,  0.6812084 ,  0.18511449, ...,  0.        ,
             0.        ,  0.        ],
           ...,
           [ 2.629588  ,  0.5016182 , -0.06889313, ...,  0.        ,
             0.        ,  0.        ],
           [-1.2240323 , -1.8518959 , -0.49901402, ..., -2.2873523 ,
            -2.462123  , -3.4297137 ],
           [-0.6956119 , -0.3328333 , -1.1663185 , ..., -2.2172358 ,
            -2.3308113 , -2.9259408 ]], dtype=float32)

• Indexes: (2)
  • trajectory
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           990, 991, 992, 993, 994, 995, 996, 997, 998, 999],
          dtype='int64', name='trajectory', length=1000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-02-02 12:00:00', '2016-02-02 13:00:00',
                   '2016-02-02 14:00:00', '2016-02-02 15:00:00',
                   '2016-02-02 16:00:00', '2016-02-02 17:00:00',
                   '2016-02-02 18:00:00', '2016-02-02 19:00:00',
                   '2016-02-02 20:00:00', '2016-02-02 21:00:00',
                   '2016-02-02 22:00:00', '2016-02-02 23:00:00',
                   '2016-02-03 00:00:00', '2016-02-03 01:00:00',
                   '2016-02-03 02:00:00', '2016-02-03 03:00:00',
                   '2016-02-03 04:00:00', '2016-02-03 05:00:00',
                   '2016-02-03 06:00:00', '2016-02-03 07:00:00',
                   '2016-02-03 08:00:00', '2016-02-03 09:00:00',
                   '2016-02-03 10:00:00', '2016-02-03 11:00:00',
                   '2016-02-03 12:00:00', '2016-02-03 13:00:00',
                   '2016-02-03 14:00:00', '2016-02-03 15:00:00',
                   '2016-02-03 16:00:00', '2016-02-03 17:00:00',
                   '2016-02-03 18:00:00', '2016-02-03 19:00:00',
                   '2016-02-03 20:00:00', '2016-02-03 21:00:00',
                   '2016-02-03 22:00:00', '2016-02-03 23:00:00',
                   '2016-02-04 00:00:00', '2016-02-04 01:00:00',
                   '2016-02-04 02:00:00', '2016-02-04 03:00:00',
                   '2016-02-04 04:00:00', '2016-02-04 05:00:00',
                   '2016-02-04 06:00:00', '2016-02-04 07:00:00',
                   '2016-02-04 08:00:00', '2016-02-04 09:00:00',
                   '2016-02-04 10:00:00', '2016-02-04 11:00:00',
                   '2016-02-04 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq='h'))

• Attributes: (67)

  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 2025-02-18 17:10:55.798353

  date_created :

  2025-02-18T17:10:55.798361

  source :

  Output from simulation with OpenDrift

  model_url :

  https://github.com/OpenDrift/opendrift

  opendrift_class :

  WindBlow

  opendrift_module :

  opendrift.models.windblow

  readers :

  odict_keys(['/root/project/tests/test_data/2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc'])

  time_coverage_start :

  2016-02-02 12:00:00

  time_step_calculation :

  0:15:00

  time_step_output :

  1:00:00

  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_general:use_auto_landmask :

  True

  config_drift:current_uncertainty :

  0

  config_drift:current_uncertainty_uniform :

  0

  config_drift:max_speed :

  12.0

  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

  opendrift_version :

  1.13.0

  seed_geojson :

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

  simulation_time :

  2025-02-18 17:10:55.801869

  reader_x_wind :

  ['/root/project/tests/test_data/2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc']

  reader_y_wind :

  ['/root/project/tests/test_data/2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc']

  time_coverage_end :

  2016-02-04 12:00:00

  time_coverage_duration :

  P2DT0H0M0S

  time_coverage_resolution :

  P0DT0H15M0S

  performance :

  -------------------- Reader performance: -------------------- /root/project/tests/test_data/2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc 0:00:01.6 total 0:00:00.0 preparing 0:00:00.2 reading 0:00:00.0 interpolation 0:00:00.0 interpolation_time 0:00:01.3 rotating vectors 0:00:00.0 masking -------------------- Performance: 2.9 total time 0.2 configuration 0.0 preparing main loop 2.6 main loop 0.1 updating elements 0.0 cleaning up --------------------

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_bounds_vertical_crs :

  EPSG:5831

  geospatial_lat_min :

  68.02076

  geospatial_lat_max :

  70.15918

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  point

  geospatial_lon_min :

  13.755725

  geospatial_lon_max :

  19.037323

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  point

  runtime :

  0:00:02.963434

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  geospatial_vertical_positive :

  up

Print and plot results

print(o)
o.animation(ocean_color='skyblue', land_color='burlywood')

===========================
--------------------
Reader performance:
--------------------
/root/project/tests/test_data/2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc
 0:00:01.6  total
 0:00:00.0  preparing
 0:00:00.2  reading
 0:00:00.0  interpolation
 0:00:00.0  interpolation_time
 0:00:01.3  rotating vectors
 0:00:00.0  masking
--------------------
Performance:
    2.9 total time
    0.2 configuration
    0.0 preparing main loop
    2.6 main loop
      0.1 updating elements
    0.0 cleaning up
--------------------
===========================
Model:  WindBlow     (OpenDrift version 1.13.0)
        1000 active PassiveTracer particles  (0 deactivated, 0 scheduled)
-------------------
Environment variables:
  -----
  x_wind
  y_wind
     1) /root/project/tests/test_data/2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF_20160202_subset.nc

Discarded readers:

Time:
        Start: 2016-02-02 12:00:00 UTC
        Present: 2016-02-04 12:00:00 UTC
        Calculation steps: 192 * 0:15:00 - total time: 2 days, 0:00:00
        Output steps: 49 * 1:00:00
===========================

/opt/conda/envs/opendrift/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:1692: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  result = super().scatter(*args, **kwargs)
17:11:01 INFO    opendrift:4611: Saving animation to /root/project/docs/source/gallery/animations/example_windblow_0.gif...
17:11:25 INFO    opendrift:3048: Time to make animation: 0:00:27.449051

o.plot(buffer=.5, ocean_color='skyblue', land_color='burlywood')

(<GeoAxes: title={'center': 'OpenDrift - WindBlow\n2016-02-02 12:00 to 2016-02-04 12:00 UTC (49 steps)'}>, <Figure size 910.871x1100 with 1 Axes>)