3 | | The following call to `loadECOMS` will load a time series of 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), corresponding to the summer (JJA, `season = 6:8`) of 2001 (`years = 2001`) 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 funciton to compute the daily mean from the 6-hourly data using the argument `time = "DD".` |
| 3 | 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".` |
| 4 | |
| 5 | |
| 6 | First of all, the library is called and we log-in into the ECOMS-UDG: |
| 7 | |
| 8 | {{{ |
| 9 | #!text/R |
| 10 | > library(ecomsUDG.Raccess) |
| 11 | > loginECOMS_UDG("user","password") |
| 12 | }}} |
| 13 | |
| 14 | Now we are ready for accessing the ECOMS-UDG: |
8 | | [2014-06-17 15:51:15] Defining homogeneization parameters for variable "tas" |
9 | | NOTE: daily mean will be calculated from the 6-h instantaneous model output |
10 | | [2014-06-17 15:51:15] Defining geo-location parameters |
11 | | [2014-06-17 15:51:16] Defining initialization time parameters |
12 | | [2014-06-17 15:51:20] Retrieving data subset ... |
13 | | [2014-06-17 15:54:16] Done |
| 19 | [2014-09-02 15:28:42] Defining homogeneization parameters for variable "tas" |
| 20 | NOTE: daily mean will be calculated from the 6-h model output |
| 21 | [2014-09-02 15:28:42] Defining geo-location parameters |
| 22 | [2014-09-02 15:28:42] Defining initialization time parameters |
| 23 | [2014-09-02 15:28:46] Retrieving data subset ... |
| 24 | [2014-09-02 15:31:25] Done |
31 | | > quartiles <- apply(point.cfs$Data, MARGIN = 1, FUN = quantile, probs = c(.25,.75)) |
32 | | > ens.mean <- rowMeans(point.cfs$Data) |
33 | | > dates <- as.POSIXlt(point.cfs$Dates$start, tz="GMT") |
34 | | > plot(dates, ens.mean, ylim = range(point.cfs$Data), ty = 'n', ylab = "tas - Daily Mean", xlab = "time") |
35 | | > polygon(x = c(dates, rev(dates)), y = c(quartiles[1, ], rev(quartiles[2, ])), border = "transparent", col = rgb(0,0,1,.4)) |
36 | | > lines(dates, ens.mean) |
| 56 | quartiles <- apply(point.cfs$Data, MARGIN = 2, FUN = quantile, probs = c(.25,.75)) |
| 57 | ens.mean <- colMeans(point.cfs$Data) |
| 58 | dates <- as.POSIXlt(point.cfs$Dates$start, tz="GMT") |
| 59 | plot(dates, ens.mean, ylim = range(point.cfs$Data), ty = 'n', ylab = "tas - Daily Mean", xlab = "time") |
| 60 | polygon(x = c(dates, rev(dates)), y = c(quartiles[1, ], rev(quartiles[2, ])), border = "transparent", col = rgb(0,0,1,.4)) |
| 61 | lines(dates, ens.mean) |
47 | | [2014-06-17 16:05:55] Defining homogeneization parameters for variable "tas" |
48 | | [2014-06-17 16:06:55] Defining geo-location parameters |
49 | | [2014-06-17 16:06:55] Defining time selection parameters |
50 | | [2014-06-17 16:06:55] Done |
| 72 | [2014-09-02 15:49:44] Defining homogeneization parameters for variable "tas" |
| 73 | [2014-09-02 15:49:44] Defining geo-location parameters |
| 74 | [2014-09-02 15:49:44] Defining time selection parameters |
| 75 | [2014-09-02 15:49:45] Retrieving data subset ... |
| 76 | [2014-09-02 15:49:45] Done |