Changes between Version 12 and Version 13 of udg/ecoms/RPackage/examples/continentalSelection

Timestamp:

Sep 2, 2014 5:22:00 PM (8 years ago)

Author:

juaco

Comment:

--

udg/ecoms/RPackage/examples/continentalSelection

@@ -33 +33 @@
 
 
-[[Image(image-20140617-130616.png)]]
+[[Image(image-20140902-170632.png)]]
 
-It is possible to load now the reference observations for the spatio-temporal domain selected, using the same values in the corresponding arguments:
+
+We load now the reference observations for the spatio-temporal domain previously chosen:
+
 
 {{{
 #!text/R
 > ex2.obs <- loadECOMS(dataset = "WFDEI", var = "tasmin", lonLim = c(-15,35), latLim = c(32, 75), season = c(12,1,2), years = 2001:2010)
-[2014-06-17 16:28:13] Defining homogeneization parameters for variable "tasmin"
-[2014-06-17 16:28:13] Defining geo-location parameters
-[2014-06-17 16:28:13] Defining time selection parameters
-[2014-06-17 16:28:32] Done
+[2014-09-02 17:07:43] Defining homogeneization parameters for variable "tasmin"
+[2014-09-02 17:07:44] Defining geo-location parameters
+[2014-09-02 17:07:44] Defining time selection parameters
+[2014-09-02 17:07:44] Retrieving data subset ...
+[2014-09-02 17:07:58] Done
 > print(object.size(ex2.obs), units = "Mb")
 60.6 Mb
@@ -52 +55 @@
 {{{
 #!text/R
-> observed <- apply(ex2.obs$Data, FUN = mean, MARGIN = c(1,2))
-> x.obs <- ex2.obs$xyCoords$x
-> y.obs <- ex2.obs$xyCoords$y
-> image.plot(x.obs, y.obs, observed, asp = 1, xlab = "", ylab = "", main = "Mean minimum surface temp observed")
-> world(add=TRUE)
+> plotMeanField(ex2.obs)
 }}}
 
-[[Image(image-20140617-163609.png)]]
+[[Image(image-20140902-171138.png)]]
 
-Note that WFDEI provides data for land areas only, and its spatial resolution is much higher that CFS (1º vs 0.5º). In order to compare both datasets, it is first necessary to put them in the same grid (i.e., to interpolate). We use the bilinear interpolation algorithm of package `fields` to this aim, included in the function `interp.surface.grid`:
+Note that WFDEI provides data for land areas only, and its spatial resolution is finer than CFS (1º vs 0.5º). In order to compare both datasets, it is first necessary to put them in the same grid (i.e., to interpolate). We use bilinear interpolation to this aim, using the `downscaleR` function `interpGridData` in combination with the `getGrid` method, useful to recover the parameters defining the grid of a dataset to pass them to the interpolator: 
 
 {{{
 #!text/R
-> obs.regridded <- interp.surface.grid(obj = list(x = x.obs, y = y.obs, z = observed), grid.list = list(x = x, y = y))
-> par(mfrow = c(1,2))
-> image.plot(member1 - obs.regridded$z, asp = 1, main = "Bias Member 1")
-> image.plot(member2 - obs.regridded$z, asp = 1, main = "Bias Member 2")
+> obs.regridded <- interpGridData(gridData = ex2.obs, new.grid = getGrid(ex2), method = "bilinear")
+> plotMeanField(obs.regridded)
 }}}
-
-[[Image(image-20140617-170438.png)]]
 
 
@@ -79 +74 @@
 
 
-
-