u/Tamulel

So i have seen some comments, and i have done a mistake when i was new to models (and probably you were or are doing that mistake), of seeing what models says and "parroting" it as true, as if is going to 100% happen, and i want beginners that are learning about those models to understand why this is a bad idea, as i would wish i didn't expend THAT much time into "stalking" and seeing what models say, instead of trying to understand why the models predict and do forecast for.

Analogy:
Imagine seeing a flashlight (let's say, for the sake of simplicity, a storm system) for the first time, you observe that there is a ON button and that makes the flashlight, well, flash a light (produce a thunderstorm, for example), you press it, and indeed, you observe that it produces light (thunderstorm).

Now imagine that exact same scenario, but with already knowing this information, what your brain (model) will tell you is that when you press ON, it will flash a light, which makes total sense, but the brain (model) is making an assumption.

The thing is, to make a flashlight work (at least the flashlight i have), you need a battery (let's say it's CAPE, is basically the fuel of storms), but your brain (model) just makes the assumption that the battery is already there, as the brain has seen the exact same scenario and says "this will happen again", but what if the battery (CAPE) ISN'T there?, well, the flashlight (storm system) will fail to produce the light (thunderstorm), making it a fail prediction (bust).

But you can verify if the battery (CAPE) is REALLY there, by just observing inside the flashlight, and you can also observe other things that make the flashlight work as well!.

So let's fully translate that analogy into the real world:

Remember i said you can verify if a storm system has the right ingredients to produce thunderstorms or severe weather?, well, in 5 to 6 days, you can't really know the future events, you can't really fact-check if that storm system has the right ingredients to produce let's say tornadoes, as a lot of things can change, you can't really predict the future exactly how it is, sadly, you can only try to, and that is what models are for.

Models. especially far in the future, can only predict an specific area of which it can be possible to have the right ingredients for, let's say again for example, tornadoes, they can show you where it may happen, what you really need to do is to fact-check the model, ask ¿why is it predicting this?, and start from there.

Then you can do your own "forecast", and try to predict yourself, in that area in specific, if that could happen, you know what to look for, you know what are the ingredients that the models predict is going to be in a certain area, so you search for them, you do NOT only base out of what the models predict, as again, they can predict things that are not physically possible, and "busting out". But thankfully, the NWS does that for you and are mostly accurate as they are professionals dedicated to do exactly that, but they are humans too and can make mistakes as well.

There is obviously so much more into this, but i tried to explain it if i were explain it to myself when i discovered models, i'm obviously human, english is not my first language and i barely know much terms of meteorology, but what i know from experience is that models do tend to bust, and that they don't predict the future, and they don't tell the future.

If i said something incorrect, PLEASE correct me because if i'm wrong, and i teach people things like this (i would probably do that), it would be a nightmare for me, and also i want to learn about things i don't know, thanks and sorry in advance :)