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Description
Has anybody ever looked at the default turbidity data? I finally got around to it and I think it's horrible.
First, I was surprised when changing my latitude and longitude by just a small amount gave me fairly different results.
times = pd.DatetimeIndex(start='2015-01-01', end='2016-01-01', freq='1D')
pvlib.clearsky.lookup_linke_turbidity(times, 32, -111, interp_turbidity=False).plot(label='Tucson1')
pvlib.clearsky.lookup_linke_turbidity(times, 32.2, -110.9, interp_turbidity=False).plot(label='Tucson2')
pvlib.clearsky.lookup_linke_turbidity(times, 33.5, -112.1, interp_turbidity=False).plot(label='Phoenix')
pvlib.clearsky.lookup_linke_turbidity(times, 35.1, -106.6, interp_turbidity=False).plot(label='Albuquerque')
plt.legend()
The values for Tucson change by up to 0.5 depending on if I round to the nearest degree. No significant terrain differences between these points.
Then I decided to just plot the full array for every month. Here's the code plus a couple of images.
import pvlib
import calendar
import os
import scipy.io
pvlib_path = os.path.dirname(os.path.abspath(pvlib.clearsky.__file__))
filepath = os.path.join(pvlib_path, 'data', 'LinkeTurbidities.mat')
mat = scipy.io.loadmat(filepath)
linke_turbidity_table = mat['LinkeTurbidity'] / 20
for month in range(0, 12):
plt.figure(figsize=(20,10))
plt.imshow(linke_turbidity_table[:,:,month], vmin=1, vmax=5, )
plt.title(calendar.month_name[1+month])
plt.colorbar()
Sure, it has the global, seasonal trends right, but there is a ton of crap in there.