# Changes between Version 11 and Version 12 of udg/ecoms/RPackage/examples/continentalSelection

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Timestamp:
Sep 2, 2014 5:00:03 PM (7 years ago)
Comment:

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Unmodified
 v11 = Regional-Continental domain selections 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. Instead of loading the whole 6-hourly time series, or filtering by a particular time as in the [http://meteo.unican.es/ecoms-udg/RPackage/Examples/pointSelection previous example] we will retrieve the daily mean values, by setting the argument time = "DD", that internally computes the daily mean from the 6-hourly instantaneous values. 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): {{{ #!text/R > 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") [2014-06-17 12:47:49] Defining homogeneization parameters for variable "tasmin" NOTE: daily mean will be calculated from the 6-h instantaneous model output [2014-06-17 12:47:49] Defining geo-location parameters [2014-06-17 12:47:49] Defining initialization time parameters [2014-06-17 12:47:54] Retrieving data subset ... [2014-06-17 12:54:33] Done [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 > print(object.size(ex2), units = "Mb") 35 Mb }}} In this case, the data are stored in a 4D-array, with the dimensions indicated by the dimensionsattribute: 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: {{{ #!text/R > str(ex2$Data) num [1:902, 1:54, 1:47, 1:2] 17.4 16.4 17.4 18.7 18.4 ... - attr(*, "dimensions")= chr [1:4] "time" "lon" "lat" "member" num [1:2, 1:902, 1:47, 1:54] 17.4 17.2 16.4 18.7 17.4 ... - attr(*, "dimensions")= chr [1:4] "member" "time" "lat" "lon" }}} This is an example on how to plot the members selected as spatial means for the 10-year period. Note that this example uses the library fields, not attached on load of the ecomUDG.Raccess package: Members can be plottede individually by setting multimember = TRUE in the plotMeanField function of the downscaleRpackage: {{{ #!text/R > library(fields) # Install if not available to reproduce the example > member1 <- apply(ex2$Data[,,,1], FUN = mean, MARGIN = c(2,3)) > member2 <- apply(ex2$Data[,,,2], FUN = mean, MARGIN = c(2,3)) > x <- ex2$xyCoords$x > y <- ex2$xyCoords\$y > par(mfrow = c(1,2)) > image.plot(x,y,member1, asp = 1, main = "Member 1") > world(add = TRUE) > image.plot(x,y,member2, asp = 1, main = "Member 2") > world(add = TRUE) plotMeanField(ex2, multi.member = TRUE) }}}