In this section a number of examples for data download and visualization/analysis are presented within the R environment, through the loadECOMS function. Currently, there are four different seasonal to annual hindcasts and one observational gridded dataset available at ECOMS-UDG. All of them are available through the common interface loadECOMS, and therefore the argument values may vary slightly. For instance, arguments members and leadMonth do not apply in the case of the observational gridded dataset WFDEI, and hence ignored. Similarly, the output data structure may vary consequently, and forecast data types include the initialization dates and the names of the chosen members, while this information is not included for other types of gridded data.
The examples given have been kept deliberately simple in order to preserve a moderate output size (<150 Mb) and reasonable execution times (<10 minutes), although larger (and hence more time-consuming) requests can be done. The limitations in data loading depend essentially on two factors:
In the particular case of global domain selections (lonLim and latLim arguments set to NULL) for forecast data, it is recommended that only single-member, single-year selections are performed, due to the large size of this type of requests. Note that this is just an approximate recommendation. Object sizes also depend on the spatial resolution (CFS has approximately 1º horizontal res., while System4 is 0.75º and WFDEI 0.5º). Similarly, while GCM data will normally return data for the whole Earth (including oceans) for most variables, many observational datasets (like WFDEI) provide only data for land areas. In addition, It is always advisable to temporally aggregate to the maximum level possible. To this aim, it is possible to aggregate monthly using the argument aggr.m to specify a monthly aggregation function (see EXAMPLE 3), which dramatically reduces the size of the data, allowing for large global domain data requests. Type help("loadECOMS") for details on time aggregation options.