Also, I ran the script! This is what I got:
There are some wonky climate zones there, which I figure I should smooth out manually.
I can't figure out which colors correspond to which climate zones. The chart Azelor put on the main post doesn't seem to correspond to what I have--could someone please let me know what I'm missing?
I'm hoping there aren't any deeper inconsistencies--my precipitation maps seemed a bit sparse to me and now my world is pretty desert-heavy. Does it look implausible?
I agree with MrBragg and Michi il Disperso that the equatorial aridity is suspect, since land directly on the equator should be getting hit by the ITCZ for at least part of the year and thus get a lot of precipitation, barring some extreme mountains causing a powerful rain shadow (and even then the windward side of those mountains would still probably be very wet).Originally Posted by CTA
To find out what climates colors correspond to, you can peek at the table ("kColorTableDefault") in the script (or look at the "defaultOutputProfile" color profile, which replicates the climate category colors the script defaults to) for a mapping from RGB to Köppen category.
Regarding the pixel cleanup script, what sort of results are you getting from it? I experimented with a similar feature that I called a "fuzzy" mode for the script itself but kept getting "hot pixels" where a gradientized/antialiased border between two regions was resulting in isolated pixels getting assigned colors that were neither of the two bordering categories. Regarding adding the script to the repository, I've sent you a PM with a few questions pertaining to that.
Last edited by AzureWings; 07-31-2020 at 02:57 PM.
I'm getting exactly the results I want--as far as I can tell, every erroneous pixel is changed to the value of its nearest neighbor.
This is my jan temp map before running the script:
And this is my jan temp map after running the script:
(you have to zoom in on the borders to see the difference)
The only problem I encountered was that certain pixels were corrected to ocean in some maps and corrected to a prec/temp zone in others. To solve this, I wrote another short script that goes through all 4 images and converts those pixels to ocean.
That's pretty good output! The ocean vs. land problem shouldn't need a separate step once I've fixed up the ocean detection bug (the intended behavior of the climate script is already to only treat as land pixels that are land in all four input maps).
@AzureWings: Sounds good, I'll send over the script today
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