= Single point selections

The following call to `loadECOMS` will load a time series of summer 2001 (JJA, `season = 6:8`, `years = 2001`) surface (2m) daily mean temperature (`var = "tas"`, as defined in the [http://meteo.unican.es/ecoms-udg/RPackage/Homogeneization vocabulary]) for the coordinate -3.7E 40.4N, (`lonLim = -3.7`, `latLim = 40.4`) corresponding to the city of Madrid (Spain), as forecasted the previous March (`leadMonth = 2`) by the CFSv2 hindcast (`dataset = "CFSv2_seasonal_16"`). We will select the first 10 members (`members = 1:10`). Note that the original variable is stored in the CFSv2 database as 6-hourly. Hence, we indicate to the function to compute the daily mean from the 6-hourly data using the argument `time = "DD".`


First of all, the library is called and we log-in into the ECOMS-UDG:

{{{
#!text/R
library(ecomsUDG.Raccess)
loginECOMS_UDG("user","password")
}}}

Now we are ready for accessing the ECOMS-UDG:

{{{
#!text/R
point.cfs <- loadECOMS(dataset = "CFSv2_seasonal_16",
                       var = "tas",
                       members = 1:10,
                       lonLim = -3.7,
                       latLim = 40.4,
                       season = 6:8,
                       years = 2001,
                       leadMonth = 2,
                       time = "DD")
}}}

Note that a number of informative messages will appear on-screen:

{{{
[2014-09-02 15:28:42] Defining homogeneization parameters for variable "tas"
NOTE: daily mean will be calculated from the 6-h model output
[2014-09-02 15:28:42] Defining geo-location parameters
[2014-09-02 15:28:42] Defining initialization time parameters
[2014-09-02 15:28:46] Retrieving data subset ...
[2014-09-02 15:31:25] Done
}}}

This is the size of the loaded object:

{{{#!text/R
print(object.size(point.cfs)) # 22456 bytes
}}}


The returned object contains all the necessary information for data representation (geo-location, time ...). In the next lines we plot the loaded time series for each member. The element `Data` contains the data itself. In this case, it is a 2D array with the dimensions `member` (10 members selected) and `time` (92 days for June, July and August), as indicated by the `dimensions` attribute.


{{{
#!text/R
str(point.cfs$Data)
}}}

which returns information on the Data array structure:

{{{
 num [1:10, 1:92] 16.3 17.1 12.9 10.4 15.3 ...
 - attr(*, "dimensions")= chr [1:2] "member" "time"
}}}


Note that, by convention, the dimensions of the Data array will be always ordered in the canonical form ''member > time > lat > lon''. Several vertical levels are never loaded at the same time, so the dimension ''level'' will never appear. This will be indicated in the Variables element of the returned output if existing. In this case it is NULL, because the variable loaded is a surface variable:

{{{
#!text/R
str(point.cfs$Variable)
}}}

returns the structure of the Variable element of the output:

{{{
List of 3
 $ varName   : chr "tas"
 $ isStandard: logi TRUE
 $ level     : NULL
}}}


In the following example we plot the time series with the multi-member mean and its dispersion (interquartilic range 25-75):

{{{
#!text/R
quartiles <- apply(point.cfs$Data, MARGIN = 2, FUN = quantile, probs = c(.25,.75))
ens.mean <- colMeans(point.cfs$Data)
dates <- as.POSIXlt(point.cfs$Dates$start, tz="GMT")
plot(dates, ens.mean, ylim = range(point.cfs$Data),
     type = 'n', ylab = "tas - Daily Mean", xlab = "time")
polygon(x = c(dates, rev(dates)),
        y = c(quartiles[1, ], rev(quartiles[2, ])),
        border = "transparent", col = rgb(0,0,1,.4))
lines(dates, ens.mean)
}}}

[[Image(image-20140617-160406.png)]]

{{{#!comment

The interface `loadECOMS` is also used in the case of gridded observations. For instance, using the same geo-location and time arguments used in the previous selection, we can now retrieve the observed temperature for that particular grid cell accessing the WFDEI dataset:

{{{
#!text/R
point.wfdei <- loadECOMS(dataset = "WFDEI",
                         var = "tas",
                         lonLim = -3.7,
                         latLim = 40.4,
                         season = 6:8,
                         years = 2001,
                         time = "DD")
print(object.size(point.wfdei)) # 13704 bytes
}}}

The WFDEI point loaded is unidimensional, corresponding to a time series of a single point location. It lacks the "member" dimension because it is an observational dataset:

{{{
#!text/R
str(point.wfdei$Data)
}}}

{{{
 num [1:92(1d)] 26.6 25.7 25.6 24.3 23.3 ...
 - attr(*, "dimensions")= chr "time"
}}}

{{{#!text/R
point.wfdei$xyCoords
}}}

{{{
$x
[1] -3.75

$y
[1] 40.25

attr(,"projection")
[1] "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0"
}}}


It is now possible to overlay the observed temperature series with the 2-month ahead prediction of CFS for that particular grid cell (the result is not very impressive, though):

{{{
#!text/R
lines(dates, point.wfdei$Data, col = "red", lwd = 1.5)
}}}

[[Image(image-20140617-161214.png)]]

We could include a legend in the figure to define the different elements shown:

{{{
#!text/R
legend('bottomright', c("CFS-Members","WFDEI"), lty=c(1,1), col=c(rgb(0,0,1,.4),"red"))
}}}

}}}