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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"\n# Parameterised Stokesdrift\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"If surface Stokes drift is not awailable 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)`\nThis 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()" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.11.6" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 0 | ||
} |
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#!/usr/bin/env python | ||
""" | ||
Parameterised Stokesdrift | ||
========================= | ||
""" | ||
|
||
#%% | ||
# | ||
# If surface Stokes drift is not awailable from a wave model, there are two alternatives: | ||
# - one can increase the wind_drift_factor by e.g. 1.5%, as the Stokes Drift is typically 1.5% of the wind speed | ||
# - or the surface Stokes drift can be parameterized from wind speed and fetch distance | ||
|
||
#%% | ||
# The latter option is activated in OpenDrift with the config setting | ||
# `o.set_config('drift:use_tabularised_stokes_drift', True)` | ||
# This activates a paramterisation of Stokes drift with the following method, as implemented by Petter Nygren from SMHI: | ||
# https://opendrift.github.io/_modules/opendrift/models/physics_methods.html#wave_stokes_drift_parameterised | ||
# The 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). | ||
|
||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
from opendrift.models.physics_methods import wave_stokes_drift_parameterised | ||
|
||
for fetch in ['5000', '25000', '50000']: | ||
wind = ([np.arange(1, 35), np.array([0])]) | ||
sx, sy = wave_stokes_drift_parameterised(wind=wind, fetch=fetch) | ||
plt.plot(wind[0], sx/wind[0], label=f'Fetch: {fetch[:-3]} km') | ||
plt.xlabel('Wind speed [m/s]') | ||
plt.ylabel('Stokes drift / wind speed [ratio]') | ||
plt.legend() | ||
plt.show() |
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