Visualising the Remote Sensing Satellite data
Posted by softestpawn on March 5, 2011
To practice getting back into coding again, I wrote a quick visualiser for the global temperature satellite measurements, and it’s quite pretty so seems worth presenting.
The RSS data is already presented as graphs and outlines here, but I thought it would be interesting to see what happened across the globe over time.
The data files for the TLT (near surface ‘brightness’) from here: ftp://ftp.ssmi.com/msu/data/binary/
I plotted these using Java into four panels:
- top left simply shows brightness corresponding to the absolute temperature
- top right is the anomaly according to the average data set. ie, each pixel was averaged over the time series, then for each time step the difference is shown. Negatives are blue, positives red.
- Bottom left is the anomaly averaged over 12 months to remove seasonal effects. ie, each pixel was averaged as above across the whole series, then again for 6 months previous and subsequent to the time step, and the difference shown. Negatives are blue, positives red.
- Bottom right is as above but over 24 months.
A typical frame looks like this:
Lots of not rights here: I’m not quite sure which months correspond to exactly which year, I’ve not got the colour distributions right so there are occasional wierd pixels, and the shape isn’t right.
It’s still quite pretty; you can see the seasonal shifts from north to south hemisphere, and the outline of the continents on the absolute temperature frame top left. The bottom two show the changing patterns of local weather over the medium term.
I assembled a subselection into an animated gif; every 3rd month and about half (1994-2007ish) the full time range (1985-2011) . This is a subset as my GIF animator only handles something like 70 frames (which is fair enough, should really find out how to do this as a proper video) which hopefully will show here, though it’s an 11MB file:
Code is available if you want it.
Used a trial version of A4Video (looks useful) to assemble this video for most of the time range (it’s top-and-tailed a bit):