I am reading an article in which the author claims the means and standard deviations for the annual weather data she's studying vary along the years; i.e., every year has different mean and standard deviation. (She didn't mention how much variation she detected.)
The author didn't delve into the topic, yet she affirmed these values needed to be deseasonalized so the time series would go from non-stationary to stationary.
The problem is that she does not give a statistical reason for that; e.g., 'the data set has means and standard deviations that are too disparate' or 'the seasonality is overshadowing other important factors'. She simply did it. It's very likely it's easy to understand why she did it but since I am new to all this, it makes no sense to me.
I've Googled the topic looking for a direct and simple answer but had no success in doing so.
I also wonder why would one deseasonalize data in the first place if seasonality is embedded in it? Isn't it a vital part of the data? An important "trend"?"