Drawing a line between models and observations

In my last post I showed that observations are models as well.  But when this is the case, why do we distinguish between these two kinds of data the way we do? Why is everyone so keen on observations, when they are just another model output?

The reason can be found usually in their different structure. The amount of modelling, which is applied to an observation to still be called observation should usually be very basic. Coming from the atmospheric sciences myself, the border between the two worlds can often be drawn in the type of the data. Generally the observations in that field are point data, often in situ data, which are irregular in time and space. In contrast to this, model data is usually very regular and sometimes high-dimensional.

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