Archives for posts with tag: NCAR

I mentioned the decision process for the dataset ds570.0 in a previous post. Today I would like to introduce the dataset a little further. I also made some first steps with the package lattice for plotting, and will show the results of that. From the dataset website:

World Monthly Surface Station Climatology, 1738-cont

This world monthly surface station climatology has data for over 4700 different stations (2600 in more recent years). Data for some stations goes as far back as the mid-1700’s. See decedal coverage for more detail. Most of the data was obtained directly from the National Climatic Data Center (NCDC), Asheville, North Carolina. However, much of the data prior to 1951 came from John Wolbach of Harvard College Observatory, who contracted to have this data key entered at NCDC. The first six months of 1961 were key entered at NCAR. Sharon Nicholson, Florida State University, provided African precipitation data to extend the records of over 250 stations. Dennis Shea, NCAR/CGD, has been a valuable source for data obtained directly from various countries.

Check out the detailed information if you want to know more. I took the following graphic from that page, showing the land stations’ positions (also available as a KML file):
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One motivation for the SOS4R plugin is reproducible research – and what data could be more appropriate than the question of climate change? I am certainly not saying that all problems (“Climategate“) could be helped with making analysis simpler just because data is easily accessible… but hopefully some!

My two use cases (excerpt from application) that I will base one temperature data are:

First, a researcher wants to use temporally ordered univariate data to create a forecast based on an ARIMA model. She uses the package forecast to achieve her goal.

Second, a point pattern analysis shall be performed. The user wants to analyse covariate effects of spatially distributed data. She uses the package spatstat. An analysis from the workshop paper “Analysing spatial point patterns in R” by Adrian Baddeley is carried out.

Natually, I am not a climate researcher, but nevertheless I’d like to see whether I can make my own temperature model of the earth based on publically available data and Open Source software. Read the rest of this entry »