wiki:udg/ecoms/RPackage/examples/pointSelection

Single point selections

First of all, the user needs to be 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:

library(loadeR.ECOMS)
loginUDG("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).

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 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:

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.

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 is not used. 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:

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):

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)

Last modified 5 years ago Last modified on Mar 31, 2016 4:15:44 PM

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