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find new turbidity data #101

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@wholmgren

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@wholmgren
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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()

monthly

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()

january
july

Sure, it has the global, seasonal trends right, but there is a ton of crap in there.

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