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 2001-2010 (years = 2001:2010), according to the forecast of previous September (leadMonth = 3). The original variable is stored as 6-hourly data for this particular dataset. We will retrieve the daily mean values, by setting the argument time = "DD", that internally computes the daily minimum from the 6-hourly instantaneous values (see the NOTE below when executing the command):
ex2 <- loadECOMS(dataset = "CFSv2_seasonal_16", var = "tasmin", members = 1:2, lonLim = c(-15,35), latLim = c(32, 75), season = c(12,1,2), years = 2001:2010, leadMonth = 3, time = "DD")
Returns the following on-screen messages:
[2014-09-02 16:45:58] Defining homogeneization parameters for variable "tasmin" NOTE: daily minimum will be calculated from the 6-h model output [2014-09-02 16:45:58] Defining geo-location parameters [2014-09-02 16:45:58] Defining initialization time parameters [2014-09-02 16:46:03] Retrieving data subset ... [2014-09-02 16:52:57] 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, 1:902, 1:47, 1:54] 17.4 17.2 16.4 18.7 17.4 ... - attr(*, "dimensions")= chr [1:4] "member" "time" "lat" "lon"
Members can be plotted individually by setting multimember = TRUE in the plotMeanField function of the downscaleRpackage:
plotMeanField(ex2, multi.member = TRUE)