DailyMeteo.org – 2014 Conference
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Our friend Stefan has been participating in MilanoR since the beginning, and was one of the people who started using R intensively after the “Introduction to R” Quantide course. Since he is from Belgrade (Serbia), and takes part in the activities of the Belgrade R community, there is an interesting R event/conference which will take place in Belgrade in June, which he would like to share with us.
Having in mind that the MilanoR group is a group of all R users of Milan, we are always open to publish news that could be interesting for our users. It is enough to contact the webmaster.
University of Belgrade – Faculty of Civil Engineering
Belgrade (Serbia)
Event date:
Monday, June 23, 2014 – 09:00 to Friday, June 27, 2014 – 17:00
Spatial and spatio-temporal modelling of meteorological and climatic variables using Open Source software (R + OSGeo)
International conference and workshop: three days of software tutorials (DailyMeteo.org WORKSHOPS) + two days of conference with keynote speakers. Register for this event.
Aims and scope:
This workshop aims at forming an international consortium to support production of an open archive of meteorological images at high spatial (1 km) and temporal (1 day) resolution (the WorldDailyMeteo initiative).
We look at implementing the state of the art spatio-temporal geostatistics (with the focus on algorithms implemented in the Open Source Software: R, GRASS GIS, SAGA GIS) to produce a public repository of meteorological images (as an update to the existing global climatic archives e.g. WorldClim).
We invite all international and national teams that work on mapping climatic and meteorological variables to come to Belgrade and present their work, participate in the discussion forums and consider joining the WorldDailyMeteo initiative.
The workshop will consist of a 3-day training session (tutorials) on spatio-temporal interpolation of meteo data in R (packages: raster,gstat, space
Themes:
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Spatio-temporal interpolation of daily temperature, rainfall, meteorological conditions
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Global climatic and meteorological data sets
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Combining geostatistics and remote sensing
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Time-series analysis of meteo data
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Automated detection of climatic change
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Sampling optimization for monitoring climate change
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Multivariate geostatistics for climatology
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New software for spatial analysis in meteorology
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