A scientific cycle

Two years. Two years between the first ideas and the submit of the paper, which has gone on its journey today. Sounds like a long time, but to be honest it is not. To show this I would like to explain in this post a little bit the generalised basic steps of my work towards a paper. I will not take the submitted paper from today as an example, because its creation was quite unusual. Therefore, I will stick with the general approach, which is divided in several phases: Continue reading


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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All observations are models

Doing statistics between the two worlds of observations and model results lead often to the assumption that both are completely different things. There are the observations, where real people moved into the field, drilled, dug and measured and delivered the pure truth of the world we want to describe. In contrast to this, the clean laboratory of a computer, which takes all our knowledge and creates a virtual world. This world need not necessary have something to do with its real counterpart, but at least it delivers us nice information and visualisation. But this contrast between the dirty observations and the clean models is usually only something, which exists in our heads, in reality they are much more connected to each other.

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What’s it all about?

When you start a new blog, the most important thing is to think about the scope of it. No, its not my first blog, I had others on different topics in the past. Nevertheless, it is the first, which is connected to my current work.
I am a learned meteorologist, doing something which many would call climate science and in the end my main work can be classified as statistical data science. In this field a huge blogosphere exists, sometimes very political, often as part of a war between different view points. No, my aim is not to take part in this.
I would rather prefer to write on some topics within this field from a view of a simple scientists, to show how the daily work looks like. And yes, sometimes some simple words, spoken in a hurry during discussions, need a longer explanation. When this gets a philosophical touch, then this is more than appreciated. I think it is important that when you work in science you think about the way you work and how we get to our knowledge we call established science. Additionally, new developments like new papers, conferences and other stuff which come along will be shown here, certainly with a different view like it is done at the entity itself.
My aim will be at least one post per month, but I know that this will vary from time to time. Comments are appreciated, but will be controlled before publication: Advertisements and personal attacks will be strictly prevented.

So let’s start the project!