ground- water, geo- statistics, environmental- engineering, earth- science

Improved Individual Decision-Making: Weather

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When I read Rufus Pollock’s editorial on “Forget big data, small data is the real revolution”, it occurred to me that everybody, probably even I, could take advantage of what Pollock calls the “democratization of the masses”. In this post I will show how information can be “pulled together” using only basic programming skills. This information then can be used for improved decision making. The example that I decided to use to put this into practice might be the most universal conversation topic: weather 😉

Practicing “Small Data”

Usually, I follow my interest in weather on a very basic level: I read the weather forecast. I try to use not the basic forecasts. Hence, I like to visit because of the fantastic amount of information they make available, and the fantastic visualizations that and present.

The unfortunate thing is that you kind of have to believe those products. I hadn’t seen a good weather map in a long time, until I was sailing recently at DHH Chiemsee, who make prints from the DWD analysis maps of air pressure at ground surface (together with annotations of observations) available on a daily basis.

The following ideas came to my mind:

  • it would be very interesting to see the progression of these pressure maps over time
  • since they are analysis maps, commonly still hand drawn, it would be interesting to compare them to other analyses, done by somebody else
  • a description of the current situation associated with the pressure maps would be useful, so that an amateur like me gets some hints

With this information at hand, everybody could form their own opinion of the current weather situation in an improved way!

After some research, that did not take very long at all, I found some other sources on the internet, that allowed me to come up with the following map:

Grosswetterlage overview 2013 05 07 07 30 08

The code I wrote allows to create this plot at times that can be specified. The left column shows the current analysis performed by different institutions, the right column shows predictions performed by KMNI.

I wanted to do all this in python, so I needed to figure out how to get images from the internet and learned about the packages urllib2, HTMLParser, Image (I didn’t know that there was a greyscale png), and sched. Despite the fact, that there are still some (minor?) things that need to be ironed out (plotting of text with matplotlib, style of the headings) I put the code up on github.

I’d be very happy to hear what you guys think! Happy birthday Ferdi! 😉

Written by Claus

May 11th, 2013 at 10:32 pm

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