In the next lines we describe an illustrative example of the `loadSystem4` function. We will retrieve System4 simulation data for the Iberian Peninsula, considering mean surface temperature for January and the first simulation member, for the three-year period 1990-1999. This simple example has been chosen because of the fast data access (note that this also depends on the connection speed). Using a standard broadband connection, running this example took approximately 21 seconds. {{{ > openDAP.query <- loadSystem4(dataset = "http://www.meteo.unican.es/tds5/dodsC/system4/System4_Seasonal_15Members.ncml", + var = "tas", members = 1, + lonLim = c(-10,5), latLim = c(35,45), + season = 1, years = 1990:1999, leadMonth = 1) }}} Data are now loaded into the R session: {{{ > str(openDAP.query) List of 7 $ VarName : chr "Mean_temperature_at_2_metres_since_last_24_hours_surface" $ VarUnits : chr "degC" $ TimeStep : chr "24h" $ MemberData :List of 1 ..$ : num [1:310, 1:280] 13.3 13.9 12.5 13 13 ... $ LatLonCoords : num [1:280, 1:2] 45 44.2 43.5 42.7 42 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:2] "lat" "lon" $ RunDates : POSIXlt[1:310], format: "1989-12-01" "1989-12-01" "1989-12-01" "1989-12-01" ... $ ForecastDates: POSIXlt[1:310], format: "1990-01-01" "1990-01-02" "1990-01-03" "1990-01-04" ... }}} A simple representation of data consists of mapping the spatial mean for the period considered: {{{ > mean.field <- colMeans(openDAP.query$MemberData[[1]]) > lat <- openDAP.query$LatLonCoords[ ,1] > lon <- openDAP.query$LatLonCoords[ ,2] > # Requires package "akima" > library(akima) > filled.contour(interp(lon, lat, mean.field), asp=1, + plot.title = title (main = "Mean surface T January 1990-99",ylab = "latitude", xlab = "longitude"), + key.title = title(main = "degC"), + key.axes = axis(4, seq(-1,16,1)), + color.palette = topo.colors) }}} [[Image(tracFig.png)]]