scientimate.swanwindspvariedgrid#

swanwind = scientimate.swanwindspvariedgrid(xgrid, ygrid, xpointgrid, ypointgrid, windvelgrid, winddirgrid, winddirtype='mete', windvelmin=0, savedata='no', outfilename='swanwind.wnd', outfilelocation=None, CalcMethod='linear')

Description#

Generate SWAN wind file for spatially varied wind from gridded input data

Inputs#

xgrid: x (longitude) of output grid points as a [M*N] array
ygrid: y (latitude) of output grid points as a [M*N] array
xpointgrid: x (longitude) of the locations that wind is known in those locations as a [K*L] array
ypointgrid: y (latitude) of the locations that wind is known in those locations as a [K*L] array
windvelgrid: Wind velocity at (xpointgrid,ypointgrid) as a [K*L*P] array

P is number of time steps for a time series
winddirgrid: Wind direction at (xpointgrid,ypointgrid) as a [K*L*P] array in (Degree)

P is number of time steps for a time series
winddirtype=’mete’: Define wind direction type

‘mete’: meteorological wind direction

Meteorological direction represents a direction wind comes from and is measured counter-clockwise from the North

0 (degree): from North, 90 (degree): from East, 180 (degree): from South, 270 (degree): from West

‘trig’: trigonometric wind direction
windvelmin=0: Minimum allowed wind velocity
savedata=’no’: Define if save data in a file or not

‘no’: does not save

‘yes’: save data as ascii file
outfilename=’swanwind.wnd’: Name of output file between ‘ ‘ mark, example: ‘swanwind.wnd’

outfilename should have ‘.wnd’ extension
outfilelocation=pwd: Location of output file between ‘ ‘ mark, example: ‘C:' in MATLAB, or ‘C:/’ in Python
CalcMethod=’linear’: Interpolation method

‘linear’: Use default or ‘linear’ method to interpolate

‘nearest’: Use nearest neighbor method to interpolate

Outputs#

swanwind: Spatially varied wind velocity data formated for SWAN

Wind velocity data at each time step is assigned into the grid points

Examples#

import scientimate as sm
import numpy as np

xgrid,ygrid=np.meshgrid(np.linspace(-91,-90,100),np.linspace(28,30,100))
xpointgrid,ypointgrid=np.meshgrid(np.linspace(-92,-89,100),np.linspace(27,31,100))
windvelgrid=10+(12-10)*np.random.rand(100,100,4) #Data for 4 time steps
winddirgrid=60+(65-60)*np.random.rand(100,100,4) #Data for 4 time steps
winddirtype='mete'
windvelmin=2.5
savedata='no'
outfilename='swanwind.wnd'
outfilelocation=None
CalcMethod='linear'
swanwind=sm.swanwindspvariedgrid(xgrid,ygrid,xpointgrid,ypointgrid,windvelgrid,winddirgrid,winddirtype,windvelmin,savedata,outfilename,outfilelocation,CalcMethod)


xgrid,ygrid=np.meshgrid(np.linspace(-91,-90,100),np.linspace(28,30,100))
xpointgrid,ypointgrid=np.meshgrid(np.linspace(-92,-89,100),np.linspace(27,31,100))
windvelgrid=10+(12-10)*np.random.rand(100,100) #Data for 1 time step
winddirgrid=60+(65-60)*np.random.rand(100,100) #Data for 1 time step
winddirtype='mete'
windvelmin=2.5
savedata='no'
outfilename='swanwind.wnd'
outfilelocation=None
CalcMethod='linear'
swanwind=sm.swanwindspvariedgrid(xgrid,ygrid,xpointgrid,ypointgrid,windvelgrid,winddirgrid,winddirtype,windvelmin,savedata,outfilename,outfilelocation,CalcMethod)

References#

Booij, N. R. R. C., Ris, R. C., & Holthuijsen, L. H. (1999). A third‐generation wave model for coastal regions: 1. Model description and validation. Journal of geophysical research: Oceans, 104(C4), 7649-7666.

SWAN Team. (2007). S WAN user manual. Delft University of Technology. The Netherlands.

scientimate.swanwindspvariedgrid

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