Observations and reanalayses: Our shaky reference

For everyone working on data analysis in climatological science, using references is essential. These references, representing some form of truth, is often the target, which models have to reach. Verification (or in non-meteorological science validation) methodologies evaluate the results against the references and dependent on the methodology deliver good results when the model is near to it, matches its variability or is close in other statistical parameters. The power of these references in these analysis and defining our knowledge about the world is immense and so it is essential that it really has something to do with things we see in front of our windows.

Last month Wendy Parker published a paper named “Reanalyses and Observations: What’s the Difference” and looked at the references from a more philosophical point of view. She listed four points, which critically looked at the connection between references and observations and in this post I would like to take a look at them.

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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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