In this example we will load data for Europe for the variable surface (2m) minimum temperature (var = tasmin), for the first two members (members = 1:2) of the CFSv2 hindcast (dataset = CFSv2_seasonal_16), considering the wintertime (DJF, season = c(12,1,2)) for the 10-year period 1991-2000 (years = 1991:2000), according to the forecast issued in September (leadMonth = 3). The ?original variable is 6-hourly . We will retrieve the daily minimum values, by setting the argument time = "DD" and the daily aggregation function to minimum (aggr.d = "min"):
> ex2 <- loadECOMS(dataset = "CFSv2_seasonal", var = "tasmin", members = 1:2, lonLim = c(-15,35), latLim = c(32, 75), season = c(12,1,2), years = 1991:2000, leadMonth = 3, time = "DD", aggr.d = "min")
Returning the following on-screen information during the data loading process:
[2015-05-15 12:39:51] Defining homogeneization parameters for variable "tasmin" [2015-05-15 12:39:51] Defining geo-location parameters [2015-05-15 12:39:51] Defining initialization time parameters NOTE: Daily aggregation will be computed from 6-hourly data [2015-05-15 12:39:55] Retrieving data subset ... [2015-05-15 12:46:28] Done
The size of the object is 35 Mb:
print(object.size(ex2), units = "Mb")
In this case, the data are stored in a 4D-array, with the dimensions indicated by the dimensionsattribute, always following the canonical ordering of dimensions:
str(ex2$Data)
num [1:2, 1:903, 1:47, 1:54] 21.6 16.1 21.1 17.4 21.5 ... - attr(*, "dimensions")= chr [1:4] "member" "time" "lat" "lon"
Members can be plotted individually by setting multimember = TRUE in the plotMeanGrid function of the downscaleR package:
plotMeanGrid(ex2, multi.member = TRUE)