we would like to test the trends modules developed at the NIS-LNO. to do so, we need a data source that generates both standard metadata (EML) and data on demand. This is where datasource4trends comes into play.
LARS-WG (from Mikhail Semenov) is used to generate stochastic weather patterns based on a given pattern. In this case, we select the Konza Prairie LTER weather data from 1982 to 2005 as the seed. We modify the daily average temperature to that of (Tmin+Tmax)*.5. We need yearly data chunks in the format of YYYY JD Tave Tmin Tmax PPT SRAD where JD is julian day, PPT is precipitation and Srad is solar radiation.
Perl assists with the process of updating the metadata XML, the data and the upload in the development servers, the metacat.
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