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All IPCC definitions taken from Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I, Glossary, pp. 941-954. Cambridge University Press.

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Rescue Climate Data

Posted on 27 January 2011 by MartinS

Through a post  by Stefan Broennimann - a Swiss climate researcher - I became aware of two projects dealing with the digitisation of historical weather data. Both are important projects, because they will make available for study historical climate data that have never been taped before, from measurements that can never be repeated. These transcripts will help the scientist to better understand the climate of the past which is necessary to understand the climate of the future. Stefan Broennimann describes it the following way:

The available climate record is essential to understanding current and future climate change and improving climate prediction. However, this record could be considerably improved. Hundreds of thousands of volumes of historical meteorological observations have not yet been digitised. These data could be very valuable for society - they should be digitised and made publicly available.

Stefan's own project is called Data Rescue at home. To participate, just click on the Digitise button and type out the digits (if you can read them ;-)). Currently, two data sets are being digitised by volunteers: German radiosonde data from the Second World War and meteorological station data from Tulagi (Solomon Islands) for the first half of the 20th century. When finished, Stefan will publish the data on his website.

Data from radiosondes are waiting to be digitized!

The second project is called Old Weather. The aim is to digitise the logs of ships that sailed the oceans in the last century. These logs document the location of the vessels, weather data (temperature, cloud cover, etc.), and even the emergencies on board (already digitised logbook observations from another project can be viewed here). How it works is explained quite well in the tutorial: How to take part.

To avoid errors (sometimes you can only guess at what was intended), in both projects, each typed page is checked by several volunteers. Well, the activity is not terribly exciting, but if you ever feel like you wanted to contribute to climate science, here's your chance!

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Comments

Comments 1 to 9:

  1. links to both projects as given in the article are unreachable, try these: Data Rescue and Old Weather - at least from Google Chrome in Linux; the extra space at the link addresses causes problems
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  2. should be: at the end of link addresses
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  3. Yeah I've done a few of these. It's not thrilling, but you can see why people are needed for this.
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  4. I have put an advert for the projects at Zvon.org guide to RealClimate.org and Zvon.org.guide to Skeptic Arguments from SkepticalScience.com. I hope it helps to find a few people.
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  5. @Mila: Yes you are right, thanks for that. The project sites are now reachable.
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  6. Neat project, thanks for bringing this to our attention. I was vaguely aware of the ships' log project, but had no idea about the upper-air data. Something to do on a quiet day...
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  7. If you want to know what can be done with these data. I suggest this powerpoint presentation,. www.usclivar.org/Meeting_Files/Reanalysis2010/Monday/Compo.ppt Results are amazing at the end of 19th century. In the future, it is expected that reconstruction could be decent up to 1900.
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  8. Here is a related paper describing the project. http://www.esrl.noaa.gov/psd/people/gilbert.p.compo/20CRv2_Compoetal2010.pdf
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  9. These data are neatly typed and, with a bit of preprocessing, should be conducive to using automated character recognition! Has anyone considered automating part of this? You could do an OCR scan on the images, use that to fill in the appropriate columns a priori, and then have several volunteers check the results. That should make their lives much easier. You can even make the OCR "trainable" to extend it to other type writer fonts, and possibly enhance it further by using some outlier detection. I have only limited experience with OCR myself, but it looks like this would be a pretty good option here.
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