= Single point selections

First of all, the user needs to be [https://meteo.unican.es/trac/wiki/udg/registration registered] in the ECOMS-UDG via the Thredds administration Panel (TAP) before using the package for accessing any dataset. Once registered, the user will have a valid username and password and can log-in using the `loginECOMS_UDG` function:

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

Now we are ready for accessing the ECOMS-UDG.

The following call to `loadECOMS` will load a time series of summer 2001 (JJA, `season = 6:8`, `years = 2001`) daily (`time = "DD"`) mean (`aggr.d = "mean"`) surface (2m) temperature (`var = "tas"`), 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"`). We will select the first 10 members (`members = 1:10`).


{{{
#!text/R
point.cfs <- loadECOMS(dataset = "CFSv2_seasonal",
                       var = "tas",
                       members = 1:10,
                       lonLim = -3.7,
                       latLim = 40.4,
                       season = 6:8,
                       years = 2001,
                       leadMonth = 2,
                       time = "DD",
                       aggr.d = "mean")
}}}

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

{{{
[2015-05-15 11:46:49] Defining homogeneization parameters for variable "tas"
[2015-05-15 11:46:50] Defining geo-location parameters
[2015-05-15 11:46:50] Defining initialization time parameters
NOTE: Daily aggregation will be computed from 6-hourly data
[2015-05-15 11:46:54] Retrieving data subset ...
[2015-05-15 11:49:47] Done
}}}

The original variable is stored in the CFSv2 database as 6-hourly (see the [https://meteo.unican.es/trac/wiki/udg/ecoms/dataserver/listofvariables available variables] and their time resolution). Hence, we indicate to the function to perform a daily average using the argument `time = "DD"` via the `mean` function (`aggr.d = "mean"`).`

This is the size of the loaded object:

{{{#!text/R
print(object.size(point.cfs)) # 20128 bytes
}}}

The returned object contains all the necessary information for data representation (geo-location, time, other metadata...). 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] 13.3 16.3 17.1 12.9 10.4 ...
 - 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. In this case it is NULL, because the variable loaded is a surface variable. Other useful metadada are included in the Variables element:

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

returns the structure of the Variable element of the output:

{{{
List of 2
 $ varName: chr "tas"
 $ level  : NULL
 - attr(*, "is_standard")= logi TRUE
 - attr(*, "units")= chr "degrees Celsius"
 - attr(*, "longname")= chr "2-meter air temperature"
 - attr(*, "daily_agg_cellfun")= chr "mean"
 - attr(*, "monthly_agg_cellfun")= chr "none"
 - attr(*, "verification_time")= chr "DD"
}}}

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$Data),
     type = 'n', ylab = point.cfs$Variable$varName, xlab = "time",
     main = attr(point.cfs$Variable, "longname"))
mtext(attr(point.cfs$Variable, "units"))
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-20150515-121332.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-20150515-121332.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"))
}}}

}}}