I have a 40 year time-series of surge levels in the ocean to which I'm trying to fit a lognormal distribution using scipy.stats. However, as far as I know (and read) a lognormal distribution cannot have negative values by definition. The scipy implementation uses a generalized version with three parameters, shape, location and scale, enabling to 'shift and scale' the distribution, which makes it possible to fit to negative values. However, can it then still be considered a lognormal distribution?
The surge data in the example below (grey histogram) has around half its values below 0, and the computed lognorm fit is actually very good (orange line; shape = 0.27, loc = -0.57, scale = 0.56). However, if I am trying to use a lognorm with the mu / sigma parameterization (i.e. mu = log(scale), sigma = shape, and loc fixed at 0), see also Wikipedia, it returns an error (due to the negative values).
What I don't really understand is if a 'shifted' 3 parameter lognorm still classifies as a lognormal distribution? I prefer to use the standard parameterization, however for many timeseries this will not be possible and generally the obtained fit is worse.