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      "source": [
        "\n# Parameterised Stokesdrift\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If surface Stokes drift is not available from a wave model, there are two alternatives:\n   - one can increase the wind_drift_factor by e.g. 1.5%, as the Stokes Drift is typically 1.5% of the wind speed\n   - or the surface Stokes drift can be parameterized from wind speed and fetch distance\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The latter option is activated in OpenDrift with the config setting\n`o.set_config('drift:use_tabularised_stokes_drift', True)`\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This activates a paramterisation of Stokes drift with the following method, as implemented by Petter Nygren from SMHI:\nhttps://opendrift.github.io/_modules/opendrift/models/physics_methods.html#wave_stokes_drift_parameterised\nThe code and corresponding plot below shows how the Stokes drift factor (fraction of wind speed) varies with wind speed and fetch (3 different tabulated fetch distances).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom opendrift.models.physics_methods import wave_stokes_drift_parameterised\n\nfor fetch in ['5000', '25000', '50000']:\n    wind = ([np.arange(1, 35), np.array([0])])\n    sx, sy = wave_stokes_drift_parameterised(wind=wind, fetch=fetch)\n    plt.plot(wind[0], sx/wind[0], label=f'Fetch: {fetch[:-3]} km')\nplt.xlabel('Wind speed  [m/s]')\nplt.ylabel('Stokes drift / wind speed  [ratio]')\nplt.legend()\nplt.show()"
      ]
    }
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